Lightweight Machine Learning-Based Approach for Supervision of Fitness Workout

• It is widely known that physical activity helps preventing several diseases. However, unsupervised training often results in low exercise quality, ineffective training, and, in worst cases, injuries. Automatic tracking and quantification of exercises by means of wearable devices could be an effective mean for the monitoring of exercise correctness. As a consequence, such devices could help motivating people, thus improving the quantity of performed physical exercise, with positive effects on users' health conditions. However, despite the availability of several commercial devices, the performance and effectiveness are not well documented. This work proposes a new solution for fitness workout supervision exploiting machine learning techniques, in particular Linear Discriminant Analysis for analyzing data coming from wearable Inertial Measurement Units. Efforts have been done in order to reduce the computational requirements, thus assuring compatibility in perspective of embedded implementation. The experimental tests carried out to assess the proposed approach performance showed an accuracy in exercise detection over 93% and error in exercise counting less than 6%. © 2019 IEEE.

Nyckelord

data classification

embedded systems

machine learning

mHealth

wearables

Discriminant analysis

Health

Learning algorithms

Learning systems

Computational requirements

Embedded implementation

Inertial measurement unit

Linear discriminant analysis

Machine learning techniques

Unsupervised training

Wearable sensors

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