2021
DOI: 10.1186/s40798-021-00372-0
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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

Abstract: 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. Althou… Show more

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Cited by 28 publications
(9 citation statements)
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“…Recently, wearable sensors and wireless body area networks (WBANs) have been extensively studied for gait analysis and remote body condition monitoring [15,16]. A framework namely artificial intelligence-based body sensor network framework (AIBSNF) [15] has been proposed to strategize the usage of body sensor networks (BSNs). e proposed framework optimizes real-time location system (RTLS) and wearable biosensors to gather multivariate, lownoise, and high-fidelity data.…”
Section: Introductionmentioning
confidence: 99%
“…Recently, wearable sensors and wireless body area networks (WBANs) have been extensively studied for gait analysis and remote body condition monitoring [15,16]. A framework namely artificial intelligence-based body sensor network framework (AIBSNF) [15] has been proposed to strategize the usage of body sensor networks (BSNs). e proposed framework optimizes real-time location system (RTLS) and wearable biosensors to gather multivariate, lownoise, and high-fidelity data.…”
Section: Introductionmentioning
confidence: 99%
“…AI-enabled modeling and simulation are promising tools to improve data interpretation and distinguish between changes that are caused by a disease from those that cause the disease. Next-generation systems biology will undoubtedly benefit from AI methods capable of converting multi-omics data at different scales into actionable knowledge (Nielsen, 2017;Angione, 2019), especially considering the expected advances in data collection from patients (e.g., biosensors for measuring the concentration of chemical species from body fluids) (Jin et al, 2020;Bhave et al, 2021;Phatak et al, 2021). In turn, personalized datasets are poised to substantially enhance the ability of AI for parametrizing quantitative systems pharmacology (QSP) models that combine systems biology with pharmacokinetics and pharmacodynamics (PK/PD) in order to find optimized therapeutics for individual patients or populations with a given disease [for example, see (McEwen et al, 2021)].…”
Section: Discussionmentioning
confidence: 99%
“…The process uses techniques that include the use of “situational knowledge” or “features” ( Table 1 ) essential to train the machine learning system to improve decision-making and prediction capability. Machine learning (ML) [ 20 ] requires a training data set to extract associations and insights from disparate data. Hardware-constrained microcontroller “edge” devices use (TinyML) [ 11 ] models created in the cloud to function at the “edge” [ 21 ].…”
Section: Methodsmentioning
confidence: 99%