JMIR Biomedical Engineering

Bioacoustics classification plays a crucial role in ecological surveillance, and neonatal health monitoring. Infant cry analysis can aid early health diagnostics, while ecological acoustics informs conservation. However, the presence of environmental noise, signal variability, and limited annotated datasets often hinders model reliability and deployment. Robust feature extraction and denoising techniques have become critical for improving model robustness, enabling more accurate interpretation of acoustic events across diverse bioacoustics domains under real-world conditions.

Diagnostic errors and administrative burdens, including medical coding, remain major challenges in healthcare. Large language models (LLMs) have the potential to alleviate these problems, but their adoption has been limited by concerns regarding reliability, transparency, and clinical safety.

Brain-Computer Interface (BCI) closed-loop systems have emerged as a promising tool in healthcare and wellness monitoring, particularly in neurorehabilitation and cognitive assessment. With the increasing burden of neurological disorders, including Alzheimer’s Disease and Related Dementias (AD/ADRD), there is a critical need for real-time, non-invasive monitoring technologies. BCIs enable direct communication between the brain and external devices, leveraging artificial intelligence (AI) and machine learning (ML) to interpret neural signals. However, challenges such as signal noise, data processing limitations, and privacy concerns hinder widespread implementation. This review explores the integration of ML and AI in BCI closed-loop systems, evaluating their effectiveness in improving neurological assessments and interventions.

Adapting physical activity monitors to detect gait events (initial & final contact) has the potential to build a more personalised approach to gait rehabilitation after stroke. Meeting laboratory standards for detecting these events in impaired populations is challenging without resorting to a multi-sensor solution. The Teager-Kaiser Energy Operator (TKEO) estimates the instantaneous energy of a signal; its enhanced sensitivity has successfully detected gait events from the acceleration signals of individuals with impaired mobility, but has not been applied to stroke.

Photoplethysmography (PPG) signals captured by wearable devices can provide vascular age information and support pervasive and long-term monitoring of personal health condition.

Implantable Medical Devices (IMDs), such as pacemakers, increasingly communicate wirelessly with external devices. To secure this wireless communication channel, a pairing process is needed to bootstrap a secret key between the devices. Previous work has proposed pairing approaches that often adopt a “seamless” design and render the pairing process imperceptible to patients. This lack of user perception can significantly compromise security and pose threats to patients.

Accurately assessing pain severity is essential for effective pain treatment and desirable patient outcomes. In clinical settings, pain intensity assessment relies on self-reporting methods, which are subjective to individuals and impractical for non-communicative or critically ill patients. Previous studies have attempted to measure pain objectively using physiological responses to an external pain stimulus, assuming that the human subject is free of internal body pain. However, this approach does not reflect the situation in a clinical setting, where a patient subjected to an external pain stimulus may already be experiencing internal body pain.

Acoustic biomarkers derived from speech signals is a promising non-invasive technique for diagnosing Type 2 Diabetes Mellitus (T2DM). Despite its potential, there remains a critical gap in knowledge regarding the optimal number of voice recordings and recording schedule necessary to achieve effective diagnostic accuracy.

This study analyzed the capability of GPT-4o to properly identify knee osteoarthritis and found that the model had good sensitivity but poor specificity in identifying knee osteoarthritis; patients and clinicians should practice caution when using GPT-4o for image analysis in knee osteoarthritis.

Cardiovascular diseases (CVDs) are the leading cause of death globally, and almost one-half of all adults in the United States have at least one form of heart disease. This review focused on advanced technologies, genetic variables in CVD, and biomaterials used for organ-independent cardiovascular repair systems.

Exercise is essential for physical rehabilitation, helping to improve functional performance and manage chronic conditions. Telerehabilitation offers an innovative way to deliver personalized exercise programs remotely, enhancing patient adherence and clinical outcomes. The Home Automated Telemanagement (HAT) System, integrated with the interactive bike (iBikE) system, was designed to support home-based rehabilitation by providing patients with individualized exercise programs that can be monitored remotely by a clinical rehabilitation team.

Cell concentration in body fluid is an important factor for clinical diagnosis. The traditional method involves clinicians manually counting cells under microscopes, which is labor-intensive. Automated cell concentration estimation can be achieved using flow cytometers; however, their high cost limits accessibility. Microfluidic systems, although cheaper than flow cytometers, still require high-speed cameras and syringe pumps to drive the flow and ensure video quality. In this paper, we present SmartFlow, a low-cost solution for cell concentration estimation using smartphone-based computer vision on 3D-printed, pump-free microfluidic platforms.