JMIR Publications

ABSTRACT

Background:

Wearable sensor technologies, such as inertial measurement units (IMUs), smartwatches, and multi-sensor systems, have emerged as valuable tools in clinical and real-world health monitoring. These devices allow continuous, non-invasive tracking of gait, mobility, and functional health across a variety of populations. However, significant challenges remain, including variability in sensor placement, data processing methodologies, and insufficient validation in real-world settings.

Objective:

This systematic review aims to evaluate recent literature on the clinical and research applications of wearable sensors. Specifically, it investigates how these technologies are used to assess mobility, predict disease risk, and support rehabilitation. It also identifies limitations and proposes future research directions.

Methods:

The review was conducted according to PRISMA guidelines. A comprehensive search of PubMed, Scopus, and Web of Science databases was performed for studies published in the past ten years. Inclusion criteria focused on studies using wearable sensors in clinical or real-world environments. A total of 30 eligible studies were identified for qualitative synthesis. Data extracted included study design, population characteristics, sensor type and placement, machine learning algorithms, and clinical outcomes.

Results:

Among the reviewed studies, observational designs were the most common (43.3%), followed by experimental studies (26.7%) and randomized controlled trials (10%). IMU-based sensors were used in 66.7% of studies, with wrist-worn devices being the most common placement (43.3%). Machine learning techniques were frequently applied, with random forest (20%) and deep learning (16.7%) models predominating. Clinical applications spanned Parkinson’s disease, stroke, multiple sclerosis, and frailty, with several studies reporting high predictive accuracy for fall risk and mobility decline (AUROC up to 0.919, p < 0.05).

Conclusions:

Wearable sensors demonstrate strong potential for enhancing mobility monitoring, disease risk assessment, and rehabilitation tracking in both clinical and real-world settings. However, challenges remain in standardizing sensor protocols and data analysis. Future research should focus on large-scale, longitudinal studies, harmonized machine learning pipelines, and integration with cloud-based health systems to improve scalability and clinical translation.