Artificial Intelligence Based Body Sensor Network Framework-Narrative Review: Proposing an End-to-End Framework using Wearable Sensors, Real-Time Location Systems and Artificial Intelligence/Machine Learning Algorithms for Data Collection, Data Mining and Knowledge Discovery in Sports and Healthcare
Open accessAbstract
With the rising amount of data in the sports and health sectors, a plethora of applications using big data mining have become possible. Multiple frameworks have been proposed to mine, store, preprocess, and analyze physiological vitals data using artificial intelligence and machine learning algorithms. Comparatively, less research has been done to collect potentially high volume, high-quality 'big data' in an organized, time-synchronized, and holistic manner to solve similar problems in multiple fields. Although a large number of data collection devices exist in the form of sensors. They are either highly specialized, univariate and fragmented in nature or exist in a lab setting. The current study aims to propose artificial intelligence-based body sensor network framework (AIBSNF), a framework for strategic use of body sensor networks (BSN), which combines with real-time location system (RTLS) and wearable biosensors to collect multivariate, low noise, and high-fidelity data. This facilitates gathering of time-synchronized location and physiological vitals data, which allows artificial intelligence and machine learning (AI/ML)-based time series analysis. The study gives a brief overview of wearable sensor technology, RTLS, and provides use cases of AI/ML algorithms in the field of sensor fusion. The study also elaborates sample scenarios using a specific sensor network consisting of pressure sensors (insoles), accelerometers, gyroscopes, ECG, EMG, and RTLS position detectors for particular applications in the field of health care and sports. The AIBSNF may provide a solid blueprint for conducting research and development, forming a smooth end-to-end pipeline from data collection using BSN, RTLS and final stage analytics based on AI/ML algorithms.
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AI plays a pivotal role in optimizing the design and management of WBSNs in the healthcare sector.
“Alternatively, AI plays a pivotal and significant role in optimizing the design and management of WBSNs in the healthcare sector [27].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [27]
AI and ML-based time series analysis can perform comprehensive, time-synchronized data collection from the body sensor network framework and integrate real-time location systems.
“AI and ML-based time series analysis can perform comprehensive, and time-synchronized data collection from the body sensor network framework and integrate real-time location systems [27].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [27]
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The emergence of AI-based wearable sensors for digital health technology: a review
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- Authors
- Phatak, A. A., Wieland, F.-G., Vempala, K., Volkmar, F., & Memmert, D.
- Affiliations
- German Sport University Cologne (DE)
- University of Freiburg (DE)
- Bloomberg (United States) (US)
- Corresponding author
- Phatak, Ashwin A.
- Venue
- Sports Medicine - Open
- Published
- 2021-10-30
- Volume
- 7
- Issue
- 1
- Pages
- 79-79
- ISSN
- 2198-9761 (ISSN-L)
- 2199-1170
- PMC
- PMC8556803
- OpenAlex
- W3208166899
- Cited by
- 74
- References
- 99
- Citations by year
- 2026: 12 · 2025: 29 · 2024: 16 · 2023: 12 · 2022: 4 · 2021: 1
- FWCI
- 0.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Sports Medicine - Open (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
- Funders
- Deutsche Sporthochschule Köln (DSHS)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Sensor Networks for Data Analysis
- Keywords
- wireless body area networks, wearable sensors, real-time location systems, physiological data collection, sensor fusion, accelerometer and gyroscope, ECG, electromyography, time series analysis, sports monitoring, healthcare monitoring, big data in sports
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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