JMIR Biomedical Engineering
Engineering for health technologies, medical devices, and innovative medical treatments and procedures
Editor-in-Chief: Gunther Eysenbach, MD, MPH, FACMI, Adjunct Professor, School of Health Information Science, University of Victoria (Canada)
Gunther Eysenbach, MD, MPH, FACMI, Adjunct Professor, School of Health Information Science, University of Victoria (Canada)
JMIR Biomedical Engineering (JBME) is a new sister journal of JMIR (the leading open-access journal in health informatics), focusing on the application of engineering principles, technologies, and medical devices to medicine and biology.
As an open access journal, we are read by clinicians and patients alike and have (as are all JMIR journals) a focus on readable and applied science reporting the design and evaluation of health innovations and emerging technologies. We publish original research, viewpoints, and reviews (both literature reviews and medical device/technology/app reviews).
JMIR Biomedical Engineering has been publishing since 2016 and features a rapid and thorough peer-review process. Articles are carefully copyedited and XML-tagged, ready for submission in PubMed Central. JMIR Biomedical Engineering is indexed in DOAJ.
In an age when telehealth services are increasingly being used for forward triage, there is a need for accurate suicide risk detection. Vocal characteristics analyzed using artificial intelligence are now proving capable of detecting suicide risk with accuracies superior to traditional survey-based approaches, suggesting an efficient and economical approach to ensuring ongoing patient safety.
Mental fatigue is a common and potentially debilitating state that can affect individuals’ health and quality of life. In some cases, its manifestation can precede or mask early signs of other serious mental or physiological conditions. Detecting and assessing mental fatigue can be challenging nowadays as it relies on self-evaluation and rating questionnaires, which are highly influenced by subjective bias. Introducing more objective, quantitative, and sensitive methods to characterize mental fatigue could be critical to improve its management and the understanding of its connection to other clinical conditions.
Telerehabiliation has been shown to have great potential in expanding access to rehabilitation services, enhancing patients’ quality of life, and improving clinical outcomes. Stationary biking exercise can serve as an effective aerobic component of home-based physical rehabilitation programs. Remote monitoring of biking exercise provides necessary safeguards to ensure exercise adherence and safety in patients' homes. The scalability of the current remote monitoring of biking exercise solutions is impeded by the high cost that limits patient access to these services, especially among older adults with chronic health conditions.
Cervical myelopathy (CM) causes several symptoms such as clumsiness of the hands and often requires surgery. Screening and early diagnosis of CM are important because some patients are unaware of their early symptoms and consult a surgeon only after their condition has become severe. The 10-second hand grip and release test is commonly used to check for the presence of CM. The test is simple but would be more useful for screening if it could objectively evaluate the changes in movement specific to CM. A previous study analyzed finger movements in the 10-second hand grip and release test using the Leap Motion, a noncontact sensor, and a system was developed that can diagnose CM with high sensitivity and specificity using machine learning. However, the previous study had limitations in that the system recorded few parameters and did not differentiate CM from other hand disorders.
In this study, we propose an approach that provides a useful data summary related to a patient’s experience of pain. Because pain is a very important but subjective phenomenon that currently has no calibratable method for assessing it, we suggest an approach that uses calibratable biomarker sensors with the patient’s self-assessment of perceived pain. We surmise that such an approach may only be able to clearly distinguish between cases in which the available evidence is consistent. However, this information may provide clinicians with valuable insights, and as research progresses into how biomarkers are related to pain, more specific insights may emerge regarding how specific evidence inconsistencies may point to particular pain causes. We provide a brief overview of pain science, including the types of pain, contemporary pain theories, pain, and pain assessment techniques. Next, we present novel approaches to pain sensor development, including an overview of research on pain-related biomarker sensors and artificial intelligence methods for summarizing the evidence. We then provide some illustrations of the implementation of our approach. Some specifics are presented in the Methods section of this paper. For example, in a set of 379 patients, we observed 80% evidence of consistency and 5 types of inconsistencies. Information regarding the gender and individual differences in cyclooxygenase-2 and inducible nitric oxide synthase data on reported pain could contribute to the inconsistency. Different causes of inconsistencies are also attributed to cultural or temporal variability of cyclooxygenase-2 and inducible nitric oxide synthase (as well as their serum variation and half-life), visual analog scale, and other tools. We emphasize that this presentation is illustrative. Much work remains to be done before implementing and testing this approach in a clinically meaningful context.
Respiratory rate (RR) is arguably the most important vital sign to detect clinical deterioration. Change in RR can also, for example, be associated with the onset of different diseases, opioid overdoses, intense workouts, or mood. However, unlike for most other vital parameters, an easy and accurate measuring method is lacking.
Precision public health (PPH) can maximize impact by targeting surveillance and interventions by temporal, spatial, and epidemiological characteristics. Although rapid diagnostic tests (RDTs) have enabled ubiquitous point-of-care testing in low-resource settings, their impact has been less than anticipated, owing in part to lack of features to streamline data capture and analysis.
A formal autism diagnosis can be an inefficient and lengthy process. Families may wait several months or longer before receiving a diagnosis for their child despite evidence that earlier intervention leads to better treatment outcomes. Digital technologies that detect the presence of behaviors related to autism can scale access to pediatric diagnoses. A strong indicator of the presence of autism is self-stimulatory behaviors such as hand flapping.
Applications of robotics in daily life are becoming essential by creating new possibilities in different fields, especially in the collaborative environment. The potentials of collaborative robots are tremendous as they can work in the same workspace as humans. A framework employing a top-notch technology for collaborative robots will surely be worthwhile for further research.
Many commodity pulse oximeters are insufficiently calibrated for patients with darker skin. We demonstrate a quantitative measurement of this disparity in peripheral blood oxygen saturation (SpO2) with a controlled experiment. To mitigate this, we present OptoBeat, an ultra–low-cost smartphone-based optical sensing system that captures SpO2 and heart rate while calibrating for differences in skin tone. Our sensing system can be constructed from commodity components and 3D-printed clips for approximately US $1. In our experiments, we demonstrate the efficacy of the OptoBeat system, which can measure SpO2 within 1% of the ground truth in levels as low as 75%.
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