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Article Dans Une Revue Sensors Année : 2020

A Lean and Performant Hierarchical Model for Human Activity Recognition Using Body-Mounted Sensors

Résumé

Here we propose a new machine learning algorithm for classification of human activities by means of accelerometer and gyroscope signals. Based on a novel hierarchical system of logistic regression classifiers and a relatively small set of features extracted from the filtered signals, the proposed algorithm outperformed previous work on the DaLiAc (Daily Life Activity) and mHealth datasets. The algorithm also represents a significant improvement in terms of computational costs and requires no feature selection and hyper-parameter tuning. The algorithm still showed a robust performance with only two (ankle and wrist) out of the four devices (chest, wrist, hip and ankle) placed on the body (96.8% vs. 97.3% mean accuracy for the DaLiAc dataset). The present work shows that low-complexity models can compete with heavy, inefficient models in classification of advanced activities when designed with a careful upstream inspection of the data.
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Dates et versions

hal-02732978 , version 1 (02-06-2020)

Identifiants

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Isaac Debache, Lorène Jeantet, Damien Chevallier, Audrey Bergouignan, Cédric Sueur. A Lean and Performant Hierarchical Model for Human Activity Recognition Using Body-Mounted Sensors. Sensors, 2020, 20, ⟨10.3390/s20113090⟩. ⟨hal-02732978⟩
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