Published on 15.08.19 in Vol 4, No 1 (2019): Jan-Dec
Preprints (earlier versions) of this paper are available at http://preprints.jmir.org/preprint/13732, first published Feb 16, 2019.
Factors That Contribute to the Use of Stroke Self-Rehabilitation Technologies: A Review
Background: Stroke is increasingly one of the main causes of impairment and disability. Contextual and empirical evidence demonstrate that, mainly due to service delivery constraints, but also due to a move toward personalized health care in the comfort of patients’ homes, more stroke survivors undergo rehabilitation at home with minimal or no supervision. Due to this trend toward telerehabilitation, systems for stroke patient self-rehabilitation have become increasingly popular, with many solutions recently proposed based on technological advances in sensing, machine learning, and visualization. However, by targeting generic patient profiles, these systems often do not provide adequate rehabilitation service, as they are not tailored to specific patients’ needs.
Objective: Our objective was to review state-of-the-art home rehabilitation systems and discuss their effectiveness from a patient-centric perspective. We aimed to analyze engagement enhancement of self-rehabilitation systems, as well as motivation, to identify the challenges in technology uptake.
Methods: We performed a systematic literature search with 307,550 results. Then, through a narrative review, we selected 96 sources of existing home rehabilitation systems and we conducted a critical analysis. Based on the critical analysis, we formulated new criteria to be used when designing future solutions, addressing the need for increased patient involvement and individualism. We categorized the criteria based on (1) motivation, (2) acceptance, and (3) technological aspects affecting the incorporation of the technology in practice. We categorized all reviewed systems based on whether they successfully met each of the proposed criteria.
Results: The criteria we identified were nonintrusive, nonwearable, motivation and engagement enhancing, individualized, supporting daily activities, cost-effective, simple, and transferable. We also examined the motivation method, suitability for elderly patients, and intended use as supplementary criteria. Through the detailed literature review and comparative analysis, we found no system reported in the literature that addressed all the set criteria. Most systems successfully addressed a subset of the criteria, but none successfully addressed all set goals of the ideal self-rehabilitation system for home use.
Conclusions: We identified a gap in the state-of-the-art in telerehabilitation and propose a set of criteria for a novel patient-centric system to enhance patient engagement and motivation and deliver better self-rehabilitation commitment.
JMIR Biomed Eng 2019;4(1):e13732
Stroke has become a global problem . One new case is reported every 2 seconds, and the number of stroke patients is predicted to increase by 59% over the next 20 years [ ]. In the United Kingdom alone, more than 100,000 stroke cases are reported annually [ ], with impairment or disability affecting two-thirds of the 1.2 million stroke survivors [ ]. In the United Kingdom, only 77% of stroke survivors are taken directly to the stroke unit. Due to the high number of patients, in England, for example, the social care costs are almost £1.7 billion per annum. The social care cost varies with the age of the patient: the older the patient, the higher the cost. The cost for a person who has had a stroke was reported in 2017 to be around £22,000 per annum. Thus, cost is one of the main drives for service delivery practices. In that respect, early discharge units have been used due to better outcomes and greater success on rehabilitation. Early discharge units consist of specialized personnel who offer an intensive rehabilitation program to the patient. However, after this intensive program of relatively short duration, the patient is discharged and continues the rehabilitation at home. This is expected to reduce costs by £1600 over 5 years for every patient, according to a 2017 report [ ].
Due to increasing pressure to discharge patients early from hospital , they rely increasingly on home rehabilitation to improve their condition after discharge. As a result, the need has been increasing for home rehabilitation systems that are not dependent on specialist or clinician operators [ , , ] while providing service similar to a clinical environment. Technological advances in home rehabilitation have been mainly focused on motor control impairments due to their prevalence in the patient population (85% worldwide [ ]).
Rehabilitation in a home environment can prove more efficient than that in a clinical environment, as the home environment supports patient empowerment through self-efficacy [, ]. The presence of supportive family members and a familiarity with the space are significant contributors to motivation. Additionally, rehabilitation in cooperation or in competition with family members demonstrates higher level of engagement [ ].
Though rehabilitation in the comfort of a patient’s home seems an attractive option, home environments have limitations that can affect the use of clinical devices. The most prevalent limitations are related to space and the lack of qualified personnel to operate devices. The number of occupants; the patient’s mobility, individual personality, and mood disorders following stroke; and sound insulation, home modification requirements, and cost [, ] also contribute to limitations of home rehabilitation. Finally, different age groups react differently to technology and devices; for example, elderly survivors often do not engage with wearable devices or video games [ ]. As a result, stroke rehabilitation requires a person-centric approach that is suitable for the home environment and that does not require infrastructure change in the home.
The success of stroke rehabilitation depends heavily on personal commitment and effort. Recent studies, for example, on applied psychology in behavior change theories for stroke rehabilitation [- ], do support that the self-esteem of the patient is limited after stroke. In addition, there is an extended sedentary period due to disability and, thus, different programs of activities are set to motivate the patients. Thus, the patient’s motivation and engagement have a critical impact on the success of any routine that is to be encouraged [ ]. This is especially critical for devices used at home, since patients are usually interacting with them alone without frequent checks. Indeed, if a device does not provide a high level of engagement or motivation enhancement, it is more likely to be abandoned within 90 days [ ]. Motivation levels depend on the individual, their achievements, and their needs at each given point in time. For example, once the patients achieve their physiotherapy exercise targets, they lose motivation for further practice. There are 3 main approaches to enhancing patients’ motivation: (1) goal-setting theory, (2) self-efficacy improvement theory, and (3) possible selves theory.
This approach has been proved effective for stroke survivors. According to the goal-setting theory, the patient’s motivation can be increased through setting small goals or targets. These need to be realistic, manageable, and well defined for the individual patient. However, they also need to be sufficiently challenging for the patient to be engaged [, - ]. presents the main components contributing to motivation enhancement based on the goal-setting theory.
Self-Efficacy Improvement Theory
Self-efficacy is the individual’s ability to appreciate his or her capability to execute a set of actions to manage a situation or challenge . According to this theory, self-efficacy makes patients feel more empowered and more comfortable to overcome difficulties. In the case of rehabilitation, this has been strongly linked with self-confidence in executing activities of daily living (ADL) [ , ] and with better future performance [ , ]. presents the main components contributing to self-efficacy improvement. Completing achievable goals supports mastery and allows engagement with more complex goals. The observation of others provides a vicarious experience, which supports enhanced confidence. Verbal appraisal provides the courage to tackle more difficult goals, while physiological feedback supports the will to improve.
Possible Selves Theory
The third theory focuses the patient’s motivation on achieving a positive future image of themselves . This approach is based on the patient’s psychological condition and their ability to envisage a positive future and a successful recovery. When implemented successfully, this approach creates an optimistic environment leading to better engagement with rehabilitation and faster recovery. However, creating a pessimistic environment can have a negative impact.
Factors Affecting Motivation
Regardless of the approach implemented, several factors affect motivation positively () or negatively ( ). The main contributor to a positive motivation effect is the information available to the patient. This includes acknowledgment of the condition, control of one’s actions, achievement of goals, individualized care, overcoming an uncertain psychological condition, and receiving timely feedback [ , , ]. Motivational feedback can be oral or visual. Also, receiving performance feedback is instrumental in maintaining motivation and engagement. Finally, self-selecting goals and having personal control is a major contributor. Negative factors usually arise from the patient’s environment and, thus, care needs to be adapted to these environmental aspects to minimize their impact [ , ].
Additionally, constructive, supportive, and competitive motivational activities, such as specially designed games, can further enhance motivation and engagement with the required rehabilitation activity [, ].
Based on the above approaches, motivation levels can be increased and engagement maintained at a high level. This is particularly important for home rehabilitation, as it reduces the requirement for caregiver engagement and provides greater control and independence to the patient. Thus, home rehabilitation has a direct impact on the cost of care and the requirement for physiotherapist visits.
Moreover, patients who have an increased capacity to perform daily activities are less dependent on other family members or care providers. This personal improvement in daily activities turns the home environment into a positive contributor to rehabilitation and recovery.
We aimed to examine the state-of-the-art in home rehabilitation systems and to assess their suitability and functionality from a patient engagement perspective. Although several review (narrative and systematic) articles have been published on rehabilitation technologies focused on particular areas of the taxonomy (eg, wearable sensor systems review  and robotic systems review [ ]), to our knowledge, no extensive narrative review of existing home-based rehabilitation technologies to identify criteria for designing future solutions has been done.
Our goal was to make the following contributions: (1) extend the state-of-the-art in assessment of home-based rehabilitation by combining research from 3 research domains: motivation enhancement as part of patient psychology, home rehabilitation technologies, and monitoring technologies through an interdisciplinary approach; (2) provide an in-depth narrative review of home rehabilitation systems that addresses both information and communication technologies and mechanical engineering solutions; (3) develop a patient motivation and engagement analysis of the reviewed technologies; and (4) identify a list of comparative criteria and successful device requirements to address patient motivation and engagement designed based on research findings from all 3 research domains.
We selected a list of articles and references for review of home rehabilitation systems and monitoring systems to be included in the comparative analysis. The data sources used to search for items to be included in this review were the following databases of academic references, journals with a particular focus on stroke rehabilitation, and web sources: (1) PubMed, (2) Elsevier, (3) IEEE, (4) Springer, (5) Hindawi.com, (6) Journal of NeuroEngineering and Rehabilitation, (7) websites of stroke-related institutions and foundations presenting articles on rehabilitation found through a generic Google search, and (8) Google Scholar (including ResearchGate).
The search criteria included the following keywords and combinations thereof: stroke; devices for stroke rehabilitation; home rehabilitation; rehabilitation engagement; rehabilitation motivation; stroke rehabilitation; telerehabilitation; smart meter; pattern recognition; kinematic analysis; robotic systems; exoskeleton systems; virtual reality; games; mobile applications; individualization; gait analysis; upper limb rehabilitation; balance rehabilitation and/or training.
As the above combination of data sources and keywords returned a vast amount of results, we selected the following inclusion criteria to identify the most relevant sources. (1) Language: English. (2) Date range: within the past 20 years (1996-2018). The majority of articles were published within the past 5 years to reflect the state-of-the-art (since 2014). Older references were made to technologies that substantially shaped the future direction of home rehabilitation systems. (3) Relevance: home or self-rehabilitation was necessary.
The literature search returned a total of 307,550 results after the inclusion criteria were applied as presented in.
We used the following exclusion criteria to identify the most relevant sources and reduce the number of literature search results: (1) no relevance to stroke rehabilitation in the home environment, (2) trained personnel required to operate the technology; (3) medication or other clinical intervention required, (4) no report of engagement or motivation as a result of using the technology or other form of patient feedback, (5) no description of the technology, (6) no report of usability especially for older people, and (7) no additional contribution to the review findings compared with the previously reviewed articles.
Overall, we read 420 sources, as we excluded the majority by reading the abstracts. A total of 96 sources remained for analysis after meeting the inclusion criteria and having not been eliminated through the exclusion process.
|Topic||Results of topic search||Results after inclusion criteria|
|Devices for stroke rehabilitation||325,000||6800|
|Smart meter; pattern recognition||83,200||18,100|
|Stroke; kinematic analysis||105,000||15,700|
|Stroke rehabilitation; robotic systems||43,700||16,900|
|Stroke rehabilitation; exoskeleton systems||15,300||4440|
|Stroke rehabilitation; virtual reality||41,000||14,100|
|Stroke rehabilitation; games||47,100||16,900|
|Stroke rehabilitation; mobile applications||46,500||17,400|
|Stroke rehabilitation; individualized systems||35,800||17,300|
|Stroke rehabilitation; gait analysis||112,000||16,000|
|Stroke; upper limb rehabilitation||138,000||17,200|
|Stroke; balance rehabilitation||398,000||15,600|
|Stroke; balance training||799,000||11,600|
|Total literature search results||5,766,780||307,550|
Home Rehabilitation Systems
To perform a systematic and comprehensive review, we proposed a taxonomy of rehabilitation systems, presented in, based on the type of technology presented in the reviewed articles. We obtained the taxonomy on the basis of the therapeutic effect in combination with sensing technology.
Home rehabilitation mainly focuses on motor control impairments due to minimal or no clinical and medical intervention [, ]. On the other hand, most clinical systems (see left-hand side of ) have dependencies and are difficult to implement at home. Therapy that requires either clinical or specialist personnel to assist in execution includes transcranial magnetic stimulation and transcranial direct current stimulation [ ], regeneration of neural tissue stem cell therapy [ ], and mirror therapy [ , ]. Similarly, treatment of aphasia and cognitive impairments is predominantly within a clinical environment or through specialist intervention [ , ]. As a result, these approaches would require regular home visits or would be impossible to perform away from the clinical environment.
The right-hand side ofshows a variety of methods and approaches developed to support home rehabilitation focusing on locomotor training. They differ based on the individual’s situation and disabilities [ ].
Locomotor training [, - ] can be implemented through various methods. One approach is through the use of exoskeleton devices [ , ] for gait [ , ] or upper limb [ , ] training. Most large exoskeleton devices reduce clinic personnel costs [ - ] but are inappropriate for home use [ , ]. Some devices in this category have started to have feedback mechanisms incorporated, such as that described by Baran et al [ ]. However, these are still very expensive systems requiring a caregiver to guide and support training. Thus, we did not review these systems.
Biofeedback electromyography is based on feedback systems [, ]. Though mainly designed for clinical use, some devices using this approach have been designed for home use, such as Biomove [ ]. However, the disadvantage of this method [ ] is the use of wearable equipment, which is not suitable for all patients and particularly the for the elderly [ ].
The same challenge is faced by wearable body sensor network systems . Additionally, observation by expert or clinical personnel is often needed and, thus, we did not investigate these 2 categories further in this review.
Another approach is to use cameras or wearable sensors for motion or kinematic analysis [- ]. Cameras and wearables, however, are considered too intrusive for home use by many patients and individuals [ ]. Many applications of cameras and wearables in home rehabilitation systems exist; thus, we reviewed these in detail.
Robotic systems have been heavily investigated [- ] for home use. However, they face the same challenges of high complexity and cost. This includes systems such as low-cost resistive elements training [ ]. However, these systems still do not avoid the requirement for supervision of the exercise. We reviewed systems in this category to identify their ability to enhance motivation and patient engagement.
Another area of research interest is the virtual reality and video game domain [, - ]. Although this is a promising area for home rehabilitation, there are still many challenges. The games are not individualized to the patients’ needs; hence, patients lose motivation easily and are not engaged with the activities they need to perform [ , , , ]. In particular, elderly patients demonstrate very low engagement with this technology [ ]. This category can be expanded to include balance measurement [ ], cell phone balance training [ ], and even a music glove, which motivates patients with the help of music [ ]. We further analyzed systems in this category.
We critically evaluated home-based rehabilitation technologies with a focus on patient engagement as the widely recognized key indicator of success of rehabilitation systems in the reviewed articles. In contrast with usability, which is a measure preferred in human-computer interaction studies, engagement is not the singular measure of the usability of an interface, but rather of the perpetual retention of the user’s interest over a prolonged period of time as defined by Peters et al . Engagement can be the effect of a successful human-computer interaction design in combination with the psychological motivation of stroke survivors for rehabilitation [ ]. Based on the literature, engagement is more likely when the feedback is sufficient and well understood by the patient, and the system, apparatus, or device is easy and convenient to use without employing intrusive means and without complex requirements from the user [ ].
Kinematic Analysis at Home
In their presentation of a representative example of kinematic analysis systems, Baran et al  proposed a home rehabilitation system for upper limb recovery after stroke. They used a specially designed desk and chair to monitor the patient’s movement through sensors and cameras. Other examples of kinematic analysis used cameras for upper limb [ ] and gait analysis [ , ]. The methods were based either on an expensive camera, to accurately capture fast motion [ , ], or on a laptop and Microsoft Kinect camera sensor, depth image processing, and machine learning [ ], to extract the motion patterns, which is relatively difficult to set up and operate.
Wang et al presented another approach for kinematic analysis without the use of cameras. Instead, they proposed a wireless wearable body sensor network system with inertia sensors (accelerometers, gyroscopes, and magnetometers), implemented with 2 wearable sensors per arm to support upper limb rehabilitation. However, the study had several limitations, including the misrepresentation of the Brunnstrom method [, ] and the lack of feedback provided to the patient.
Kinematic analysis systems rarely provided individualized feedback to the patient. They relied on wearable components or cameras and were of relatively high complexity, making them outside the scope of our review. These are disadvantages, as they contradict the motivation and engagement requirements identified above (see Enhancing Motivation).
Robotic Systems at Home
Robotic systems in this domain have been extensively researched. Zhang et al  described an exoskeleton device that they claimed was lighter than similar technologies, to support upper limb rehabilitation. However, the device did not provide feedback to the patient, which would render home rehabilitation impossible. The device was focused on receiving and acting on signals from the patient to increase the task’s difficulty. But it did not demonstrate to the patient any positive or negative changes to their rehabilitation exercise.
Similarly, Amirabdollahian et al  focused on finger and wrist rehabilitation through a robotic system combined with a computer game to enhance motivation. Additionally, this system incorporated feedback to the health care professional caring for the patient. Nevertheless, the same issues as with wearable components, increased complexity and not being individualized to the needs of the patient, appeared in this device. Nijenhuis et al [ ] presented an extension of this work, where individualization and bilateral training were used to enhance motivation. However, this approach still used wearable technology.
Mohamaddan et al  addressed ADL, but their device did not provide feedback or keep the patient engaged. The device did not have progressively more difficult or easier exercises to support different stages of the recovery process.
Kohler et al  used a simpler approach by combining resistive elements with goal-oriented training. However, the recorded data was presented to the patient in the form of a sinusoidal graph, which is often not understandable information for the patient [ ]. Extending research in this direction, Nimmy and Hepsiba [ ] provided individualized exercises based on patient monitoring and also provided feedback and comparison with reference exercises. However, the resistive elements used constricted the device’s applicability.
Systems in this category demonstrated disadvantages similar to those of kinematic analysis systems. Robotic systems included wearable components, were highly complex, and when feedback was provided it was complicated and not tailored to the individual. These systems lacked engagement and motivation, especially when used by the elderly.
Video Games and Virtual Reality at Home
Some work on demonstrating engagement has been reported for video games and virtual reality approaches. Yano et al  presented a system for gait rehabilitation based on body balance training. The device supported slope and stair climbing training. The software received angle positioning data to determine position, but this feedback was not tailored to the patient. Thus, supervised rehabilitation was needed, and engagement was not supported. Similarly, González et al [ ] combined balance training with a game environment through Nintendo Wii and Microsoft Kinect platforms. However, they did not investigate engagement with this platform, particularly for the elderly population.
Sivan et al  reported on a platform for upper limb rehabilitation in which the patient interacted with a leg support and a joystick and was offered 8 different games. The game became progressively more difficult. However, there was no detailed feedback to the patient when his or her actions did not fully meet the requirements of the game. Additionally, the presence of other people in the home during the game was not taken into account. The device was difficult to set up. Slijper et al [ ] took a similar approach and extended their work to cover bilateral training to enhance engagement. This system supported individualization and feedback, but it was unclear how this system supported ADL.
Johnson et al  proposed a software system offering different tasks for upper limb rehabilitation, with extensive feedback, focusing on increasing engagement through individualization of therapy and including bilateral and unilateral therapy. However, the system had several components and used a game environment, which can lead to aversion to the rehabilitation process. Along the same lines, Gorsic and Novak [ ] aimed to increase engagement and motivation through the use of competitive or cooperative gaming. However, the game was not individualized to support all users (patients and healthy users).
Friedman et al  used a different approach where the focus was on gamification of the patient music experience for motivation enhancement. The device also combined visual feedback through light-emitting diodes installed in the wearable musical glove. However, the study raised the well-known issue of elderly patients reacting negatively to wearable devices. Also, the device was not individualized to support different patient needs.
Wittmann et al  harnessed the concept of gradually increasing difficulty to support motivation, through a virtual reality system targeting upper limb rehabilitation and use of several wearable components. The patient’s motion was continuously monitored and assessed to calibrate the device and to set tailored goals.
Saposnik et al  developed a game as an iPad app that did not use wearable technology. However, the app had several limitations for a variety of patients to engage with this game (eg, age, familiarity, mobility, capacity to hold the device) and provided no explicit feedback to the patient.
Some evidence of motivation or engagement was provided for systems in this category. Some approaches also focused on individualization. However, the main disadvantage was the lack of incorporation into daily activities. Furthermore, the elderly were less engaged and motivation could be hindered, as the benefit in daily life was not directly perceived. Finally, the use of wearable devices or tools was a common trend in these systems.
Monitoring and Home Rehabilitation
Some rehabilitation technologies required renovation and other modifications in the patient’s home [, , ]. Other challenges for the successful deployment and engagement with home assistive rehabilitation technologies were design and technical limitations [ ], and many systems did not meet the acceptance and motivation criteria as reviewed above. Indeed, retrofitting infrastructure in existing homes can be significantly more challenging than designing a smart home that will already be equipped with embedded technology. To avoid these issues, research has mostly focused on smart home environment or monitoring devices that stand alone and do not require redesign of the home [ ]. Such systems mostly focus on monitoring generic parameters and provide individualization through pattern recognition algorithms, but do not contribute to rehabilitation activities. Hence, monitoring systems can be tailored to the individual home environment [ - ] and to study individual patterns. To support rehabilitation, their scope would need to be altered to encompass rehabilitation goals, and patient motivation and engagement, while at the same time being transferable (supporting different application domains).
Systems for smart home environments have been proposed for various health-related applications [, - ]. They usually require extensive installation of sensors in several locations such as doors, windows, electrical appliances, and furniture. Monitoring devices can provide increasing understanding of the home environment, and of the patient and their condition; they can even provide a diagnosis. Such systems might be more appropriate to support rehabilitation based on performance of daily activities [ , ].
Research in this area has focused on machine learning for information extraction based on recorded data streams [, - ]. However, other challenges are introduced when such devices are used, including data security, data correctness, and device operational efficacy [ , ]. Cavallo et al [ ] presented an example of such a system targeting rehabilitation. The system provided monitoring and remote supervision, but did not actively support rehabilitation. Some monitoring systems focused on specialist support [ ] and behavior analysis [ ]. Thus, rehabilitation, motivation, and engagement are outside the scope of these systems (leading to low scores in motivation).
According to Fell et al , there is a need for new monitoring devices that incorporate different sensors or input data streams. presents the additional information that can be used to support patient rehabilitation at home. The environment must be monitored and any deviation from the “normal” behavior must be identified. Changes in refer to identification of unexpected behavior, events, or abnormalities identified in the recorded data streams. “Other sensors” refers to the need for data fusion, acquisition of information from multiple sources, and a higher level of information extraction. For example, energy patterns can be identified through active power (smart meter) measurements; appliance use, though, might require other information such as time, temperature, location, and motion. Similarly, a health event may be recorded by a single monitoring parameter (eg, glucose level dropped below a threshold). However, changes in behavior and competence in daily activities require a series of additional measurements (eg, sound, motion, temperature, humidity, gases).
We found a gap in applying monitoring technologies (supported, for example, by machine learning, fusion, or pattern recognition) to rehabilitation that, at the same time, support patients in performing daily activities and enhance motivation. We suggest that, by using these monitoring technologies, individualization can be achieved for rehabilitation purposes, via providing appropriate feedback, applying machine learning to the individual patient, and focusing on daily activities, thus meeting the acceptance, motivation, and engagement requirements reviewed above (see Enhancing Motivation).
- summarize the criteria we selected for the comparative analysis. The methods for selecting the criteria were as follows. For , we selected these criteria based on the narrative review of motivation and engagement aspects we analyzed (see Enhancing Motivation). For , we selected these criteria according to commonly used and evaluated metrics in the majority of the reviewed articles. This was additionally informed by the conclusions outlined in the Enhancing Motivation and Home Rehabilitation Systems sections. For , we selected the criteria to meet other acceptability and economic aspects (including long term usability and transferability), as well as a separate category for the application area presented in the reviewed articles.
We used the extracted information from the reviewed articles to establish the criteria and to identify whether the criteria were met by the proposed systems. For the engagement and motivation criteria, as well as acceptance, all of the reviewed articles reported results on a common basis; thus, we needed no additional steps to cross-validate the reported results.
- present a detailed comparative analysis of all the aforementioned technologies that were applicable for use in the home environment. We selected the technologies as representative examples of each category we analyzed.
|First author, year, reference no.||Motivation method||Engaging||Supports daily activities|
|Baran, 2011 ||No||No||No||Yes||No||No|
|Zhang, 2016 ||No||No||No||No||No||No|
|Yano, 2015 ||No||No||No||No||No||No|
|Mohamaddan, 2015 ||No||No||No||No||No||No|
|Sivan, 2014 ||No||No||No||Yes||No||No|
|Johnson, 2007 ||No||No||No||Yes||No||Yes|
|González, 2015 ||No||No||No||No||No||No|
|Wang, 2017 ||No||No||No||No||No||No|
|Kohler, 2010 ||No||No||No||No||No||No|
|Friedman, 2011 ||No||No||No||Yes||No||Yes|
|Nimmy, 2013 ||No||No||No||No||No||No|
|Wittmann, 2015 ||No||No||No||Yes||No||No|
|Slijper, 2014 ||No||No||No||Yes||No||No|
|Nijenhuis, 2015 ||No||No||No||Yes||Yes||Yes|
|Saposnik, 2014 ||No||No||No||No||No||No|
|Gorsic, 2016 ||Yes||Yes||Yes||Yes||Yes||No|
|Fell, 2017 ||No||No||No||No||No||No|
|Cavallo, 2009 ||No||No||No||No||No||No|
|First author, year, reference no.||Individualized||Suitable for the elderly||Nonwearable||Nonintrusive|
|Baran, 2011 ||No||No||No||No|
|Zhang, 2016 ||No||No||No||Yes|
|Yano, 2015 ||No||Yes||No||Yes|
|Mohamaddan, 2015 ||No||Yes||No||Yes|
|Sivan, 2014 ||No||No||No||No|
|Johnson, 2007 ||Yes||No||No||Yes|
|González, 2015 ||No||Yes||No||Yes|
|Wang, 2017 ||No||No||No||Yes|
|Kohler, 2010 ||No||Yes||No||Yes|
|Friedman, 2011 ||No||Yes||No||Yes|
|Nimmy, 2013 ||No||Yes||No||Yes|
|Wittmann, 2015 ||No||No||No||Yes|
|Slijper, 2014 ||No||Yes||Yes||Yes|
|Nijenhuis, 2015 ||No||No||No||Yes|
|Saposnik, 2014 ||No||No||Yes||Yes|
|Gorsic, 2016 ||No||Yes||No||Yes|
|Fell, 2017 ||Yes||Yes||Yes||Yes|
|Cavallo, 2009 ||Yes||Yes||No||No|
|First author, year, reference no.||Intended use||Cost-effective||Technologically simple||Transferable|
|Baran, 2011 ||No||Yes||No||No||No||No|
|Zhang, 2016 ||No||Yes||No||No||No||No|
|Yano, 2015 ||No||Yes||No||No||Yes||No|
|Mohamaddan, 2015 ||No||Yes||No||Yes||Yes||No|
|Sivan, 2014 ||No||Yes||No||No||No||No|
|Johnson, 2007 ||No||Yes||No||No||No||No|
|González, 2015 ||No||Yes||No||Yes||Yes||No|
|Wang, 2017 ||No||Yes||No||No||Yes||No|
|Kohler, 2010 ||No||Yes||No||Yes||Yes||No|
|Friedman, 2011 ||No||Yes||No||No||Yes||No|
|Nimmy, 2013 ||No||Yes||No||Yes||Yes||No|
|Wittmann, 2015 ||No||Yes||No||No||No||No|
|Slijper, 2014 ||No||Yes||No||No||Yes||No|
|Nijenhuis, 2015 ||No||Yes||No||No||No||No|
|Saposnik, 2014 ||No||Yes||No||Yes||Yes||No|
|Gorsic, 2016 ||No||Yes||No||No||No||No|
|Fell, 2017 ||Yes||No||Yes||No||Yes||Yes|
|Cavallo, 2009 ||Yes||No||No||Yes||No||Yes|
Our analysis identified 3 aspects of technologies that we used for comparison: (1) motivation, (2) acceptance of technology, and (3) technological aspects. We selected these aspects for their importance in supporting patients’ motivation and engagement (motivation) and in being incorporated into patients’ rehabilitation routines (acceptance, technology).
For each aspect, we identified several comparison criteria. Regarding motivation, the criteria are (1) the motivation method used, (2) the patient’s engagement with the technology, and (3) whether the technology supports daily activities as an additional measure of motivation. There are 3 motivation methods: cooperative, supportive, and constructive. When the method used in a technology was not specified, we characterized it as general. With respect to acceptance, the criteria are (1) individualization of the device to meet patients’ needs, (2) suitability of the device for elderly patients, (3) the use of wearable components, and (4) the use of intrusive monitoring methods (eg, wearable sensors, on-body sensors, cameras, microphones). Wearable and intrusive methods have a negative impact on acceptance. Technological aspects are (1) intended use for the technology (monitoring, rehabilitation, diagnosis), (2) cost, (3) complexity, and (4) transferability to other domains.
Regarding intended use, besides our focus of rehabilitation, we also included 2 systems that perform monitoring and diagnostics.
“Yes” in the table highlights that the system met the criteria, which was demonstrated in the published work, subject to our interpretation. “No” indicates that the system did not meet the criteria.
We assessed a system to be individualized or personalized or person centric when it learned or adapted to the needs of a particular patient by incorporating some type of feedback loop mechanism where the device adjusted the requested task(s) to the ability of the patient. Examples of such mechanisms are machine learning approaches and increasing task difficulty. We assessed suitability for the elderly based on Debes et al . We classified nonwearable (on-body sensors, wearable components) and nonintrusive (cameras, microphones) systems according to the system inputs that were used. The intended use of the system can be for rehabilitation, smart home monitoring, or smart home diagnosis of a health condition.
In the analysis, we considered systems that could be purchased by an average household in the United Kingdom to be cost-effective. We considered systems that would require a high capital investment, and thus reimbursement from the health care provider, to be not cost-effective. We considered technologically complex systems to be those that had a significant number of components, required significant training before use, or required extensive installation to be usable in a household. Finally, transferable systems were those that could be used for other rehabilitation purposes and were not restricted to stroke rehabilitation.
As the tables show, no technology met all the selected criteria. Most of the technologies were suitable for the elderly and were nonintrusive. However, most technologies lacked motivation and engagement enhancement through the use of a variety of motivation methods. The developed approaches were technology centric, whereas a person-centric approach is necessary to keep patients engaged and motivated in achieving their rehabilitation goals. Several devices claimed to enhance motivation but produced little or no evidence of patient engagement [, , , , , , , ]. None of the devices intended for rehabilitation were transferable to other uses. Devices intended for monitoring or diagnosis had the desired transferability features [ , ]. Only 1 of the reviewed technologies proposed for rehabilitation supported individualization [ ]; however, it did not meet the requirements for elderly patients and it used wearable components. On the other hand, individualization was supported by monitoring devices that were not intended for rehabilitation use [ , ]. Several technologies we reviewed were inappropriate for home rehabilitation, as they were technologically complex and expensive.
The first rows of- list all the selected criteria, drawn from our extensive literature review, that must be met for a home rehabilitation system to be engaging and enable stroke recovery patients to meet their progressively ambitious goals or targets. Based on the above analysis, an ideal home rehabilitation device should meet all the identified criteria and requirements. The device needs to avoid wearable or intrusive components. It needs to support enhanced motivation and engagement by being incorporated into the daily activity routine. It must be cost-effective and not complex to install, maintain, and use. It needs to support the needs of all patients, regardless of age and background. Moreover, it needs to be portable and transferable to other domains.
The successful design of an assistive technology or rehabilitation device should take under consideration what the individual should and can achieve during rehabilitation . Quantification and further analysis of the present and future conditions of the patient could overcome difficulties and unforeseen circumstances and could result in better assistive technology design.
Data and patterns from electronic databases are quite important to tailor rehabilitation, as the device can learn patients requirements and goals, adapt to their individual needs, and provide suitable challenges, for example, through machine learning. Individual choice and personal control are mandatory for success (see Monitoring and Home Rehabilitation). Technology design has to follow a person-centric approach considering technology ability levels. Given the developments in smart devices, algorithms, and information extraction, devices can adopt a person-centric approach while meeting the requirements for cost and complexity.
Contributions from the patient’s environment can be used to enhance motivation and engagement with the activity. Members of the family or others can provide a competitive or cooperative stimulation. Additionally, rehabilitation incorporated into the completion of daily activities could enhance motivation. Finally, the continuous adjustment of the technology or device to the patient’s changing requirements has a more beneficial effect. The device should adapt to increased levels of difficulty to provide stimulation for achieving higher targets.
Thus, a system catering to every occasion, individualized and adapted to support the patient’s daily activities in their home environment, has a higher potential for successful acceptance and engagement. This system should incorporate a device, hardware, and additional software. However, developing such a system for the full range of impairments and rehabilitation tasks is an unrealistic goal. This is due to the requirement for different types of inputs for each condition, the range of rehabilitation goals, the differences between patients, and the differences between home environments. Hence, the successful system should focus on supporting specific daily activities that have measurable outcomes specified in recognized health care rehabilitation tests (see Comparative Analysis).
We reviewed rehabilitation devices for stroke patients in detail. The focus was on systems that are intended for use within the home environment for self-rehabilitation routines. We reviewed several technology domains under the criteria of motivation and engagement enhancement for continued use without the need for clinical or specialist involvement. We demonstrated that the existing approaches do not meet all the criteria in the motivation, acceptance, and technological categories. However, there is evidence that some devices proposed for monitoring instead of rehabilitation might provide solutions to individualization and thus wider engagement and acceptance. We identified the criteria for a device and system that will provide the required level of self-rehabilitation commitment as nonintrusive, nonwearable, motivation and engagement enhancing through a list of motivation methods, individualized, supporting daily activities, suitable for the elderly, cost-effective, simple, transferable, and intended for use in rehabilitation.
Conflicts of Interest
- Stroke Association. State of the nation: stroke statistics January 2017. London, UK: The Association; 2017. URL: https://www.stroke.org.uk/sites/default/files/state_of_the_nation_2017_final_1.pdf
- Stroke Association. State of the nation: stroke statistics February 2018. London, UK: The Association; 2018. URL: https://www.stroke.org.uk/system/files/sotn_2018.pdf
- Saposnik G, Chow C, Gladstone D, Cheung D, Brawer E, Thorpe KE, iHOME Research Team for the Stroke Outcomes Research Canada Working Group. iPad technology for home rehabilitation after stroke (iHOME): a proof-of-concept randomized trial. Int J Stroke 2014 Oct;9(7):956-962. [CrossRef] [Medline]
- Balaam M, Egglestone S, Fitzpatrick G, Rodden T, Hughes A, Wilkinson A, et al. Motivating mobility: designing for lived motivation in stroke rehabilitation. New York, NY: ACM Press; 2011 Presented at: 29th Annual Conference on Human Factors in Computing Systems; May 7-12 2011; Vancouver, BC, Canada p. 3073-3082.
- Baran M, Lehrer N, Siwiak D, Chen Y, Duff M, Ingalls T, et al. Design of a home-based adaptive mixed reality rehabilitation system for stroke survivors. 2011 Presented at: 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Aug 30-Sep 3, 2011; Boston, MA, USA p. 7602-7605.
- Chaiyawat P, Kulkantrakorn K, Sritipsukho P. Effectiveness of home rehabilitation for ischemic stroke. Neurol Int 2009 Nov 16;1(1):e10 [FREE Full text] [CrossRef] [Medline]
- Murray J. 2018 UCI study shows in-home therapy effective for stroke rehabilitation. Orange, CA: UCI Health; 2018 May 24. URL: http://www.ucihealth.org/news/2018/05/uci-stroke-therapy-study [accessed 2019-06-04] [WebCite Cache]
- Gorsic M, Novak D. Design and pilot evaluation of competitive and cooperative exercise games for arm rehabilitation at home. 2016 Presented at: 38th Annual International Conference of the IEEE Engineering in MedicineBiology Society; Aug 16-20, 2016; Orlando, FL, USA p. 4690-4694.
- Langhorne P, Bernhardt J, Kwakkel G. Stroke rehabilitation. Lancet 2011 May 14;377(9778):1693-1702. [CrossRef] [Medline]
- World Health Organization. International Classification of Functioning, Disability and Health (ICF). Geneva, Switzerland: WHO; 2017.
- Debes C, Merentitis A, Sukhanov S, Niessen M, Frangiadakis N, Bauer A. Monitoring activities of daily living in smart homes: understanding human behavior. IEEE Signal Process Mag 2016 Mar;33(2):81-94. [CrossRef]
- Connell LA, McMahon NE, Redfern J, Watkins CL, Eng JJ. Development of a behaviour change intervention to increase upper limb exercise in stroke rehabilitation. Implement Sci 2015 Mar 12;10:34 [FREE Full text] [CrossRef] [Medline]
- Ezeugwu VE, Manns PJ. Using intervention mapping to develop and implement a home-based sedentary behavior change intervention after stroke. Transl Behav Med 2018 Dec 19. [CrossRef] [Medline]
- Peters D, Calvo RA, Ryan RM. Designing for motivation, engagement and wellbeing in digital experience. Front Psychol 2018;9:797 [FREE Full text] [CrossRef] [Medline]
- Pickrell M, Bongers B, van den Hoven E. Understanding persuasion and motivation in interactive stroke rehabilitation. In: MacTavish T, Basapur S, editors. Persuasive Technology. Cham, Switzerland: Springer Nature Switzerland AG; 2015:15-26.
- Szeto A. Rehabilitation engineering and assistive technology. In: Enderle JD, Blanchard SM, Bronzino JD, editors. Introduction to Biomedical Engineering. Second edition. Boston, MA: Academic Press; 2005:211-254.
- Sugavanam T, Mead G, Bulley C, Donaghy M, van Wijck F. The effects and experiences of goal setting in stroke rehabilitation - a systematic review. Disabil Rehabil 2013 Feb;35(3):177-190. [CrossRef] [Medline]
- Rosewilliam S, Roskell CA, Pandyan A. A systematic review and synthesis of the quantitative and qualitative evidence behind patient-centred goal setting in stroke rehabilitation. Clin Rehabil 2011 Mar 25;25(6):501-514. [CrossRef]
- Hartigan I. Goal setting in stroke rehabilitation: part 1. Br J Neurosci Nurs 2012 Apr;8(2):65-69. [CrossRef]
- Ayers S, Baum A, McManus Stanton Newman C, Wallston K, Weinman J, West R. Cambridge Handbook of Psychology, Health and Medicine. Cambridge, UK: Cambridge University Press; 2007.
- Patel S, Park H, Bonato P, Chan L, Rodgers M. A review of wearable sensors and systems with application in rehabilitation. J Neuroeng Rehabil 2012;9:21 [FREE Full text] [CrossRef] [Medline]
- Fell M, Kennard H, Huebner G, Nicolson M, Elam S, Shipworth D. Energising Health: A Review of the Health and Care Applications of Smart Meter Data. London, UK: SMART Energy GB; May 2017.
- Robinson-Smith G, Pizzi ER. Maximizing stroke recovery using patient self-care self-efficacy. Rehabil Nurs 2003;28(2):48-51. [Medline]
- Jones F, Riazi A. Self-efficacy and self-management after stroke: a systematic review. Disabil Rehabil 2010 Aug 27;33(10):797-810. [CrossRef]
- Guthrie S, Harvey A. Motivation and its influence on outcome in rehabilitation. Rev Clin Gerontol 2008 Nov 17;4(03):235. [CrossRef]
- Krebs HI, Volpe B, Hogan N. A working model of stroke recovery from rehabilitation robotics practitioners. J Neuroeng Rehabil 2009 Feb 25;6(1):6. [CrossRef]
- Maclean N, Pound P, Wolfe C, Rudd A. Qualitative analysis of stroke patients' motivation for rehabilitation. BMJ 2000 Oct 28;321(7268):1051-1054. [Medline]
- Maclean N, Pound P, Wolfe C, Rudd A. The concept of patient motivation: a qualitative analysis of stroke professionals' attitudes. Stroke 2002 Feb;33(2):444-448. [Medline]
- Pennycott A, Wyss D, Vallery H, Klamroth-Marganska V, Riener R. Towards more effective robotic gait training for stroke rehabilitation: a review. J Neuroeng Rehabil 2012 Sep 07;9:65 [FREE Full text] [CrossRef] [Medline]
- Lawrence ES, Coshall C, Dundas R, Stewart J, Rudd AG, Howard R, et al. Estimates of the prevalence of acute stroke impairments and disability in a multiethnic population. Stroke 2001 Jun;32(6):1279-1284 [FREE Full text] [Medline]
- Brewer L, Horgan F, Hickey A, Williams D. Stroke rehabilitation: recent advances and future therapies. QJM 2012 Sep 27;106(1):11-25. [CrossRef]
- Webster BR, Celnik PA, Cohen LG. Noninvasive brain stimulation in stroke rehabilitation. NeuroRX 2006 Oct;3(4):474-481. [CrossRef]
- Moniche F, Escudero I, Zapata E, Mancha F, Vega-Salvatierra Á, Pardo B, et al. Clinical studies of bone marrow-derived stem cell therapy in stroke patients. In: Jin K, Ji X, Zhuge Q, editors. Bone Marrow Stem Cell Therapy for Stroke. Singapore: Springer Nature Singapore; 2017:241-259.
- Invernizzi M, Negrini S, Carda S, Lanzotti L, Cisari C, Baricich A. The value of adding mirror therapy for upper limb motor recovery of subacute stroke patients: a randomized controlled trial. Eur J Phys Rehabil Med 2013 Jun;49(3):311-317. [Medline]
- McDermott A, Kagan A, Harvey-Vaillancourt S, Tavakol S, Moldoveanu D, Cheang P, et al. Stroke Engine. Ottawa, ON: Heart & Stroke Foundation Canadian Partnership for Stroke Recovery; 2018. Mirror therapy – upper extremity URL: https://www.strokengine.ca/en/intervention/mirror-therapy/ [accessed 2018-01-16] [WebCite Cache]
- Brady MC, Kelly H, Godwin J, Enderby P. Speech and language therapy for aphasia following stroke. Cochrane Database Syst Rev 2012 May 16(5):CD000425. [CrossRef] [Medline]
- Timmermans AAA, Seelen HAM, Willmann RD, Kingma H. Technology-assisted training of arm-hand skills in stroke: concepts on reacquisition of motor control and therapist guidelines for rehabilitation technology design. J Neuroeng Rehabil 2009 Jan 20;6:1 [FREE Full text] [CrossRef] [Medline]
- Moore JL, Roth EJ, Killian C, Hornby TG. Locomotor training improves daily stepping activity and gait efficiency in individuals poststroke who have reached a ‘plateau’ in recovery. Stroke 2010 Jan;41(1):129-135. [CrossRef] [Medline]
- Hornby TG, Campbell DD, Kahn JH, Demott T, Moore JL, Roth HR. Enhanced gait-related improvements after therapist- versus robotic-assisted locomotor training in subjects with chronic stroke: a randomized controlled study. Stroke 2008 Jun;39(6):1786-1792. [CrossRef] [Medline]
- Macko RF, DeSouza CA, Tretter LD, Silver KH, Smith GV, Anderson PA, et al. Treadmill aerobic exercise training reduces the energy expenditure and cardiovascular demands of hemiparetic gait in chronic stroke patients. A preliminary report. Stroke 1997 Feb;28(2):326-330. [Medline]
- Barbeau H, Visintin M. Optimal outcomes obtained with body-weight support combined with treadmill training in stroke subjects. Arch Phys Med Rehabil 2003 Oct;84(10):1458-1465. [Medline]
- Schmidt H, Werner C, Bernhardt R, Hesse S, Krüger J. Gait rehabilitation machines based on programmable footplates. J Neuroeng Rehabil 2007 Feb 09;4:2 [FREE Full text] [CrossRef] [Medline]
- Egglestone SR, Axelrod L, Nind T, Turk R, Wilkinson A. A design framework for a home-based stroke rehabilitation system: identifying the key components. 2009 Presented at: 3rd International Conference on Pervasive Computing Technologies for Healthcare; Apr 1-3, 2009; London, UK p. 1-8.
- Saini S, Rambli DRA, Sulaiman S, Zakaria MN, Shukri SRM. A low-cost game framework for a home-based stroke rehabilitation system. 2012 Presented at: International Conference on Computer Information Science (ICCIS); Jun 12-14, 2012; Kuala Lumpur, Malaysia p. 55-60.
- Walkbot. San Jose, CA: Exoskeleton Report LLC; 2016 Nov 26. URL: https://exoskeletonreport.com/product/walkbot/ [WebCite Cache]
- Kim S, Yang L, Park IJ, Kim EJ, JoshuaPark MS, You SH, et al. Effects of innovative WALKBOT robotic-assisted locomotor training on balance and gait recovery in hemiparetic stroke: a prospective, randomized, experimenter blinded case control study with a four-week follow-up. IEEE Trans Neural Syst Rehabil Eng 2015 Jul;23(4):636-642. [CrossRef] [Medline]
- Wong CK, Jordan K, King M. Robotic arm skate for stroke rehabilitation. 2011 Presented at: IEEE International Conference on Rehabilitation Robotics; Jun 27-Jul 1, 2011; Zurich, Switzerland p. 1-6.
- Loureiro RCV, Harwin WS, Lamperd R, Collin C. Evaluation of reach and grasp robot-assisted therapy suggests similar functional recovery patterns on proximal and distal arm segments in sub-acute hemiplegia. IEEE Trans Neural Syst Rehabil Eng 2014 May;22(3):593-602. [CrossRef] [Medline]
- Masiero S, Poli P, Rosati G, Zanotto D, Iosa M, Paolucci S, et al. The value of robotic systems in stroke rehabilitation. Expert Rev Med Devices 2014 Mar;11(2):187-198. [CrossRef] [Medline]
- Koenig A, Omlin X, Bergmann J, Zimmerli L, Bolliger M, Müller F, et al. Controlling patient participation during robot-assisted gait training. J Neuroeng Rehabil 2011 Mar 23;8:14 [FREE Full text] [CrossRef] [Medline]
- Schabowsky CN, Godfrey SB, Holley RJ, Lum PS. Development and pilot testing of HEXORR: hand EXOskeleton rehabilitation robot. J Neuroeng Rehabil 2010 Jul 28;7:36 [FREE Full text] [CrossRef] [Medline]
- Secoli R, Milot M, Rosati G, Reinkensmeyer DJ. Effect of visual distraction and auditory feedback on patient effort during robot-assisted movement training after stroke. J Neuroeng Rehabil 2011 Apr 23;8:21 [FREE Full text] [CrossRef] [Medline]
- Staubli P, Nef T, Klamroth-Marganska V, Riener R. Effects of intensive arm training with the rehabilitation robot ARMin II in chronic stroke patients: four single-cases. J Neuroeng Rehabil 2009 Dec 17;6:46 [FREE Full text] [CrossRef] [Medline]
- Ziherl J, Novak D, Olenšek A, Mihelj M, Munih M. Evaluation of upper extremity robot-assistances in subacute and chronic stroke subjects. J Neuroeng Rehabil 2010 Oct 18;7:52 [FREE Full text] [CrossRef] [Medline]
- Poli P, Morone G, Rosati G, Masiero S. Robotic technologies and rehabilitation: new tools for stroke patients' therapy. Biomed Res Int 2013;2013:153872 [FREE Full text] [CrossRef] [Medline]
- Díaz I, Gil JJ, Sánchez E. Lower-limb robotic rehabilitation: literature review and challenges. J Robotics 2011;2011:1-11 [FREE Full text] [CrossRef]
- Giggins OM, Persson UM, Caulfield B. Biofeedback in rehabilitation. J Neuroeng Rehabil 2013 Jun 18;10:60 [FREE Full text] [CrossRef] [Medline]
- Kim J. The effects of training using EMG biofeedback on stroke patients upper extremity functions. J Phys Ther Sci 2017;29(6):1085-1088. [CrossRef]
- Biomove: stroke recovery, stroke rehabilitation and stroke treatment at home. Jerusalem, Israel: Curatronic Ltd : URL: https://web.archive.org/web/20161031070446/http://www.biomove.com:80/ [WebCite Cache]
- Wang JP, Guo LQ, Sheng TY, Xiong DX. Automated Brunnstrom assessment for home rehabilitation based on GRNN model. 2017 Presented at: 4th Annual International Conference on Information Technology and Applications (ITA 2017); May 26-28, 2017; Guangzhou, China. [CrossRef]
- Yang C, Kerr A, Stankovic V, Stankovic L, Rowe P, Cheng S. Human upper limb motion analysis for post-stroke impairment assessment using video analytics. IEEE Access 2016;4:650-659. [CrossRef]
- Ye M, Yang C, Stankovic V, Stankovic L, Kerr A. A depth camera motion analysis framework for tele-rehabilitation: motion capture and person-centric kinematics analysis. IEEE J Selected Topics Signal Process 2016 Aug;10(5):877-887. [CrossRef]
- Yang C, Ugbolue UC, Kerr A, Stankovic V, Stankovic L, Carse B, et al. Autonomous gait event detection with portable single-camera gait kinematics analysis system. J Sensors 2016;2016:1-8. [CrossRef]
- Zhang Y, Guo S, Cao G, Zhang S, Liu Y. A novel variable stiffness actuator-based exoskeleton device for home rehabilitation. 2016 Presented at: IEEE International Conference on Mechatronics and Automation; Aug 7-10, 2016; Harbin, China p. 878-883.
- Amirabdollahian F, Ates S, Basteris A, Cesario A, Buurke J, Hermens H, et al. Design, development and deployment of a hand/wrist exoskeleton for home-based rehabilitation after stroke - SCRIPT project. Robotica 2014 Sep 23;32(08):1331-1346. [CrossRef]
- Yano H, Tanaka N, Kamibayashi K, Saitou H, Iwata H. Development of a portable gait rehabilitation system for home-visit rehabilitation. Sci World J 2015;2015:1-12 [FREE Full text] [CrossRef]
- Mohamaddan S, Jamali A, Abidin ASZ, Jamaludin MS, Majid NAA, Ashari MF, et al. Development of upper limb rehabilitation robot device for home setting. Procedia Comput Sci 2015;76:376-380. [CrossRef]
- Díaz M, Saez-Pons J, Heerink M, Angulo C. Emotional factors in robot-based assistive services for elderly at home. 2013 Presented at: IEEE International Symposium on Robot and Human Interactive Communication; Aug 26-29, 2013; Gyeongju, Korea p. 711-716.
- White M, Vining Radomski M, Finkelstein M, Nilsson DAS, Eugen Oddsson LI. Assistive/socially assistive robotic platform for therapy and recovery: patient perspectives. Int J Telemed Applications 2013;2013:1-6 [FREE Full text] [CrossRef]
- Sivan M, Gallagher J, Makower S, Keeling D, Bhakta B, O’Connor RJ, et al. Home-based computer assisted arm rehabilitation (hCAAR) robotic device for upper limb exercise after stroke: results of a feasibility study in home setting. J Neuroeng Rehabil 2014;11(1):163. [CrossRef]
- Johnson MJ, Feng X, Johnson LM, Winters JM. Potential of a suite of robot/computer-assisted motivating systems for personalized, home-based, stroke rehabilitation. J Neuroeng Rehabil 2007 Mar 01;4(1). [CrossRef]
- Kohler F, Schmitz-Rode T, Disselhorst-Klug C. Introducing a feedback training system for guided home rehabilitation. J Neuroeng Rehabil 2010 Jan 15;7(1). [CrossRef]
- Proffitt R, Lange B. Considerations in the efficacy and effectiveness of virtual reality interventions for stroke rehabilitation: moving the field forward. Phys Ther 2014 Oct 24;95(3):441-448. [CrossRef]
- Gaber A, Taher MF, Waheb MA. A comparison of virtual rehabilitation techniques. 2015 Presented at: World Congress on Electrical Engineering and Computer Systems and Science (EECSS ); Jul 13-14, 2015; Barcelona, Spain p. 1-6.
- Mousavi HH, Khademi M. A review on technical and clinical impact of Microsoft Kinect on physical therapy and rehabilitation. J Med Eng 2014;2014:846514 [FREE Full text] [CrossRef] [Medline]
- Wittmann F, Lambercy O, Gonzenbach RR, van Raai MA, Hover R, Held J, et al. Assessment-driven arm therapy at home using an IMU-based virtual reality system. 2015 Presented at: IEEE International Conference on Rehabilitation Robotics (ICORR); Aug 11-14, 2015; Singapore p. 707-712.
- Slijper A, Svensson KE, Backlund P, Engström H, Sunnerhagen K. Computer game-based upper extremity training in the home environment in stroke persons: a single subject design. J Neuroeng Rehabil 2014;11(1):35. [CrossRef]
- Kenyon R, Leigh J, Keshner E. Considerations for the future development of virtual technology as a rehabilitation tool. J Neuroeng Rehabil 2004 Dec 2004 Dec 23;1(1):13. [CrossRef] [Medline]
- Adamovich SV, Fluet GG, Mathai A, Qiu Q, Lewis J, Merians AS. Design of a complex virtual reality simulation to train finger motion for persons with hemiparesis: a proof of concept study. J Neuroeng Rehabil 2009;6(1):28. [CrossRef]
- Gil-Gómez J, Lloréns R, Alcañiz M, Colomer C. Effectiveness of a Wii balance board-based system (eBaViR) for balance rehabilitation: a pilot randomized clinical trial in patients with acquired brain injury. J Neuroeng Rehabil 2011 May 23;8:30 [FREE Full text] [CrossRef] [Medline]
- Smith ST, Talaei-Khoei A, Ray M. Electronic games for aged care and rehabilitation. 2009 Presented at: 11th International Conference on e-Health Networking, Applications and Services (Healthcom); Dec 16-18, 2009; Sydney, Australia p. 42-47.
- Lehrer N, Attygalle S, Wolf SL, Rikakis T. Exploring the bases for a mixed reality stroke rehabilitation system, part I: a unified approach for representing action, quantitative evaluation, and interactive feedback. J Neuroeng Rehabil 2011 Aug 30;8:51. [CrossRef] [Medline]
- Nijenhuis SM, Prange GB, Amirabdollahian F, Sale P, Infarinato F, Nasr N, et al. Feasibility study into self-administered training at home using an arm and hand device with motivational gaming environment in chronic stroke. J Neuroeng Rehabil 2015 Oct 09;12:89 [FREE Full text] [CrossRef] [Medline]
- Stewart JC, Yeh S, Jung Y, Yoon H, Whitford M, Chen S, et al. Intervention to enhance skilled arm and hand movements after stroke: a feasibility study using a new virtual reality system. J Neuroeng Rehabil 2007 Jun 23;4(1). [CrossRef]
- Colombo R, Pisano F, Mazzone A, Delconte C, Micera S, Carrozza MC, et al. Design strategies to improve patient motivation during robot-aided rehabilitation. J Neuroeng Rehabil 2007 Feb 19;4:3 [FREE Full text] [CrossRef] [Medline]
- González A, Fraisse P, Hayashibe M. A personalized balance measurement for home-based rehabilitation. 2015 Presented at: 7th International IEEE/EMBS Conference on Neural Engineering (NER); April 22-24, 2015; Montpellier, France p. 711-714.
- Lee B, Kim J, Chen S, Sienko KH. Cell phone based balance trainer. J Neuroeng Rehabil 2012;9(1):10. [CrossRef]
- Friedman N, Chan V, Zondervan D, Bachman M, Reinkensmeyer DJ. MusicGlove: motivating and quantifying hand movement rehabilitation by using functional grips to play music. 2011 Presented at: 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Aug 30-Sep 3, 2011; Boston, MA, USA p. 2359-2363.
- Pérez-Quiñones MA, Sibert JL. A collaborative model of feedback in human-computer interaction. 1996 Presented at: SIGCHI Conference on Human Factors in Computing Systems; Apr 13-18, 1996; Vancouver, BC, Canada. [CrossRef]
- Teasell R, Hussein N. Stroke Rehabilitation Clinician Handbook. London, ON: Evidence-Based Review of Stroke Rehabilitation; 2016. Motor rehabilitation: rehab of hemiplegic upper extremity post stroke URL: http://www.ebrsr.com/sites/default/files/Chapter%204B_Upper%20Extremity%20Post%20Stroke_0.pdf [WebCite Cache]
- Saebo. The Brunnstrom stages of stroke recovery. Charlotte, NC: Saebo, Inc; 2018 Jun 09. URL: https://web.archive.org/web/20181123224448/https://www.saebo.com/the-stages-of-stroke-recovery/ [accessed 2019-02-13] [WebCite Cache]
- Marchionini G. Exploratory search: from finding to understanding. Commun ACM 2006 Apr 01;49(4):41. [CrossRef]
- Nimmy JT, Hepsiba D. Rehabilitation exercise monitoring system. Int J Adv Res Electron Commun Eng 2013;2(3):389-391 [FREE Full text]
- Yassine A, Singh S, Alamri A. Mining human activity patterns from smart home big data for health care applications. IEEE Access 2017;5:13131-13141. [CrossRef] [Medline]
- Samarah S, Al Zamil MG, Aleroud AF, Rawashdeh M, Alhamid MF, Alamri A. An efficient activity recognition framework: toward privacy-sensitive health data sensing. IEEE Access 2017;5:3848-3859. [CrossRef]
- Ni Q, García Hernando AB, de la Cruz IP. The elderly’s independent living in smart homes: a characterization of activities and sensing infrastructure survey to facilitate services development. Sensors 2015 May 14;15(5):11312-11362. [CrossRef]
- Ebeid E, Heick R, Jacobsen RH. Deducing energy consumer behavior from smart meter data. Future Internet 2017 Jul 06;9(3):29. [CrossRef]
- Gajowniczek K, Ząbkowski T. Data mining techniques for detecting household characteristics based on smart meter data. Energies 2015 Jul 22;8(7):7407-7427. [CrossRef]
- Melzi FN, Zayani MH, Hamida AB, Same A, Oukhellou L. Identifying daily electric consumption patterns from smart meter data by means of clustering algorithms. 2015 Presented at: 2015 IEEE 14th International Conference on Machine LearningApplications (ICMLA); Dec 9-11, 2015; Miami, FL, USA p. 1136-1141.
- Al-Wakeel A, Wu J, Jenkins N. k -means based load estimation of domestic smart meter measurements. Appl Energy 2017 May;194:333-342. [CrossRef]
- Wang Y, Chen Q, Hong T, Kang C. Review of smart meter data analytics: applications, methodologies, and challenges. IEEE Trans Smart Grid 2019 Jun;10(3):3125-3148. [CrossRef]
- Cavallo F, Aquilano M, Odetti L, Arvati M, Carrozza MC. A first step toward a pervasive and smart ZigBee sensor system for assistance and rehabilitation. 2009 Presented at: 2009 IEEE International Conference on Rehabilitation Robotics; Jun 23-26, 2009; Kyoto, Japan p. 632-637.
|ADL: activities of daily living|
Edited by G Eysenbach; submitted 16.02.19; peer-reviewed by J Machaj, J Parker; comments to author 27.04.19; revised version received 19.06.19; accepted 19.07.19; published 15.08.19
©Ioannis Vourganas, Vladimir Stankovic, Lina Stankovic, Andrew Kerr. Originally published in JMIR Biomedical Engineering (http://biomedeng.jmir.org), 15.08.2019.
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