Advancements in Wearable Sensor Technologies for Health Monitoring in Terms of Clinical Applications, Rehabilitation, and Disease Risk Assessment: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Wearable sensor technologies such as inertial measurement units, smartwatches, and multisensor systems have emerged as valuable tools in clinical and real-world health monitoring. These devices enable continuous, noninvasive tracking of gait, mobility, and functional health across diverse populations. However, challenges remain in sensor placement standardization, data processing consistency, and real-world validation. OBJECTIVE: This systematic review aimed to evaluate recent literature on the clinical and research applications of wearable sensors. Specifically, it investigated how these technologies are used to assess mobility, predict disease risk, and support rehabilitation. It also identified limitations and proposed future research directions. METHODS: This review was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched the PubMed, Scopus, and Web of Science databases up to March 9, 2025. 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: Of the included studies, 43% (13/30) were observational, 27% (8/30) were experimental, and 10% (3/30) were randomized controlled trials. Inertial measurement unit-based sensors were used in 67% (20/30) of the studies, with wrist-worn devices being the most common (13/20, 65%). Machine learning techniques were frequently applied, with random forest (6/30, 20%) and deep learning (5/30, 17%) models predominating. Clinical applications spanned Parkinson disease, stroke, multiple sclerosis, and frailty, with several studies (4/30, 13%) reporting high predictive accuracy for fall risk and mobility decline (area under the receiver operating characteristic curve up to 0.97). CONCLUSIONS: Wearable sensors show strong potential for mobility monitoring, disease risk assessment, and rehabilitation tracking in 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".