ReviewBiosensors2025
Wearable Sensors for Precise Exercise Monitoring and Analysis.
Review in Biosensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Special Issue "Biomechanical Analysis in Physical Activity and Sports-2nd Edition".Journal of functional morphology and kinesiology · 2026Article
- Wearable Biosensing and Machine Learning for Data-Driven Training and Coaching Support.Biosensors · 2026Review
- Machine learning framework for predicting athletic injuries and optimising performance.BMC sports science, medicine & rehabilitation · 2026Article
- Human bioimpedance-based state detection technologies for sports health monitoring: a review.Frontiers in physiology · 2026Review
- Physiological Responder Profiles and Fatigue Dynamics in Prolonged Cycling.Journal of functional morphology and kinesiology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The adoption of wearable sensors for precision training has accelerated in recent years, yet most studies and reviews remain device- or feasibility-centric and lack a field-ready decision framework. This review organizes wearable sensing across four monitoring dimensions-physiological, kinematic, biochemical, and dynamic-and maps them onto three training pillars: physical, technical, and tactical. From the perspectives of athletes and coaches, we operationalize quality control, threshold, and feedback loop to translate measurement into action. We critically appraise key limitations, including signal robustness under high-intensity motion, inter-individual variability and limited model generalizability, cross-device data fusion and latency, battery life and wearability, privacy and data ownership, and limited accessibility beyond elite settings. Looking ahead, we advocate a shift from mere multidimensional measurement to a verifiable, reusable, and deployable precision-training ecosystem that delivers actionable metrics and clear decision support for practitioners.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.