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Recognition of Daily Human Activities Using Accelerometer and sEMG Signals

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Intelligent Decision Technologies 2019

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 143))

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Abstract

Human activity recognition (HAR) is an important technology for ambient-assisted living, sport and fitness activities, and health care of elderly people. HAR is usually achieved in two steps: acquisition of body signals and classification of performed activities. This paper presents an investigation on the optimal setup for recognizing daily activities using a wearable system designed to acquire surface electromyography (sEMG) and accelerometer signals through wireless sensor nodes placed on the upper limbs of the human body. To evaluate the optimal number of accelerometer and sEMG signals for detecting the user’s activities, data recorded from a few subjects were used to train and test an automatic classifier for recognizing the type of exercise being performed. In this evaluation, that was performed on eight different exercises executed by four subjects, the automatic classifier achieved an overall accuracy ranging from 10.6% to 93.0% according to different selections and combinations of the signals acquired from the sensing nodes.

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Acknowledgements

This work was supported by a Università Politecnica delle Marche Research Grant.

Author information

Authors and Affiliations

  1. DII – Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Via Brecce Bianche 12, I-60131, Ancona, Italy

    Giorgio Biagetti, Paolo Crippa, Laura Falaschetti & Claudio Turchetti

  2. Clinica Neurologica - Dipartimento di Medicina Sperimentale e Clinica, Università Politecnica delle Marche, Via Conca 71, I-60020, Ancona, Italy

    Simona Luzzi

Authors
  1. Giorgio Biagetti
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  2. Paolo Crippa
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  3. Laura Falaschetti
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  4. Simona Luzzi
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  5. Claudio Turchetti
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Corresponding author

Correspondence to Paolo Crippa .

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Editors and Affiliations

  1. Department of Information Systems, Gdynia Maritime University, Gdynia, Poland

    Ireneusz Czarnowski

  2. Bournemouth University and KES International, Poole, Dorset, UK

    Robert J. Howlett

  3. University of Canberra, Canberra, ACT, Australia

    Lakhmi C. Jain

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Biagetti, G., Crippa, P., Falaschetti, L., Luzzi, S., Turchetti, C. (2019). Recognition of Daily Human Activities Using Accelerometer and sEMG Signals. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2019. Smart Innovation, Systems and Technologies, vol 143. Springer, Singapore. https://doi.org/10.1007/978-981-13-8303-8_4

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