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Activity Classification with Inertial Sensors to Perform Gait Analysis

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Distributed Computing and Artificial Intelligence, 20th International Conference (DCAI 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 740))

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Abstract

The human gait can be analyzed to prevent injuries or gait disorders and make diagnoses or evaluate the progress during the rehabilitation therapies. This work aims to develop a system based on wearable inertial sensors to estimate flexion/extension angles of the lower limbs. Moreover, we have trained a classifier that allows the developed system to differentiate and classify autonomously between four activities of daily living. The proposed classifier is based on a feedforward neural network and has shown an accuracy of 98.33% with users that were not involved in the model’s training.

This work was supported by the Spanish Ministry of Universities through the Research and Doctorate Supporting Program FPU20/05137; by the Ministry of Universities and European Union, “financed by European Union - Next Generation EU” through Margarita Salas grant for the training of young doctors; by the Research Grants of the Miguel Hernández University of Elche through the grant 2022/PER/00002; by the Ministry of Science and Innovation through the project PID2019-108310RB-100; and by the Valencian Innovation Agency through the project GVRTE/2021/361542.

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Author information

Authors and Affiliations

  1. Robotics and Artificial Intelligence Group of the Bioengineering Institute, Miguel Hernández University, Avenida de la Universidad, s/n., 03202, Elche, Alicante, Spain

    David Martínez-Pascual, José. M. Catalán, José. V. García-Pérez & Nicolás García-Aracil

  2. INESCOP, C/ Alemania, 102., 03600, Elda, Alicante, Spain

    Mónica Sanchís & Francisca Arán-Ais

Authors
  1. David Martínez-Pascual
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  2. José. M. Catalán
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  3. José. V. García-Pérez
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  4. Mónica Sanchís
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  5. Francisca Arán-Ais
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  6. Nicolás García-Aracil
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Corresponding author

Correspondence to David Martínez-Pascual .

Editor information

Editors and Affiliations

  1. ETSII / CETINIA, Universidad Rey Juan Carlos, Madrid, Madrid, Spain

    Sascha Ossowski

  2. Politechnika Śląska Kielce, Kielce University of Technology, Santa Cruz, Poland

    Pawel Sitek

  3. Department of Informatics, Engineering School, University of Minho, Braga, Portugal

    Cesar Analide

  4. Departamento de Engenharia Informática, Polytechnic Institute of Porto, Porto, Portugal

    Goreti Marreiros

  5. BISITE, University of Salamanca, Salamanca, Salamanca, Spain

    Pablo Chamoso

  6. BISITE, University of Salamanca, Salamanca, Salamanca, Spain

    Sara Rodríguez

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Martínez-Pascual, D., Catalán, J.M., García-Pérez, J.V., Sanchís, M., Arán-Ais, F., García-Aracil, N. (2023). Activity Classification with Inertial Sensors to Perform Gait Analysis. In: Ossowski, S., Sitek, P., Analide, C., Marreiros, G., Chamoso, P., Rodríguez, S. (eds) Distributed Computing and Artificial Intelligence, 20th International Conference. DCAI 2023. Lecture Notes in Networks and Systems, vol 740. Springer, Cham. https://doi.org/10.1007/978-3-031-38333-5_8

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