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, V.; Della Corte, F. G.. - (2020), pp. 212-215. (Intervento presentato al convegno 20th IEEE Mediterranean Electrotechnical Conference, MELECON 2020 tenutosi a ita nel 2020) [10.1109/MELECON48756.2020.9140602].
A Novel Fitness Tracker Using Edge Machine Learning
Della Corte F. G.
2020
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
