Several characteristics of the human body turn into postural behavior, recognizable also during sport activities. The presence of differences between body types could lead to different behavior of wearable and fitness-devote products. A new wearable based on machine learning techniques for the exercise detection and repetitions count is described in this work. A proper dataset has been obtained in order to offline train the network. Eventually, the machine learning algorithm has been implemented inside an edge device for real-time test e verification.

A Novel Fitness Tracker Using Edge Machine Learning / Merenda, M.; Astrologo, M.; Laurendi, D.; Romeo, Vincenzo; Della Corte, F. - (2020), pp. 212-215. (Intervento presentato al convegno 2020 IEEE 20th Mediterranean Electrotechnical Conference ( MELECON) tenutosi a Palermo nel 16-18 giugno 2020) [10.1109/MELECON48756.2020.9140602].

A Novel Fitness Tracker Using Edge Machine Learning

Merenda M.
;
Della Corte F
2020-01-01

Abstract

Several characteristics of the human body turn into postural behavior, recognizable also during sport activities. The presence of differences between body types could lead to different behavior of wearable and fitness-devote products. A new wearable based on machine learning techniques for the exercise detection and repetitions count is described in this work. A proper dataset has been obtained in order to offline train the network. Eventually, the machine learning algorithm has been implemented inside an edge device for real-time test e verification.
2020
978-1-7281-5200-4
Artificial neural networks, Machine learning, Random access memory, Sensors, Accelerometers, Hardware, Testing
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/66421
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