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
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 algorith...
| Publicado en: | Sports Medicine - Open Vol. 7; no. 1; pp. 1 - 16 |
|---|---|
| Autores principales: | , , , , |
| Formato: | pictorial review tables/charts Journal Article |
| Publicado: |
Springer Nature
10/30/2021
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153317736&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153317736 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 21991170 HLUO jtl: Sports Medicine - Open issn: 21991170 maglogo: N pubinfo: dt: 10/30/2021 vid: 7 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 153317736 153317736 153317736 10.1186/s40798-021-00372-0 153317736 ppf: 1 ppct: 15 formats: tig: atl: 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 aug: au: Phatak, Ashwin A. Wieland, Franz-Georg Vempala, Kartik Volkmar, Frederik Memmert, Daniel affil: Institute of Exercise Training and Sport Informatics, German Sports University, Cologne, Germany sug: subj: Artificial Intelligence Wearable Sensors Conceptual Framework Global Positioning System Machine Learning Data Mining Sports Medicine Mobile Applications Fitness Trackers Alternative Therapies Algorithms Utilization Data Science Utilization ab: 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. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|