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Recurrent network based automatic detection of chronic pain protective behavior using MoCap and sEMG data

Wang, Chongyang ; Olugbade, Temitayo A. ; Mathur, Akhil ; De C. Williams, Amanda C. ; Lane, Nicholas D. ; Bianchi-Berthouze, Nadia

Proceedings of the 23rd International Symposium on Wearable Computers, 2019, p.225-230

New York, NY, USA: ACM

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  • Título:
    Recurrent network based automatic detection of chronic pain protective behavior using MoCap and sEMG data
  • Autor: Wang, Chongyang ; Olugbade, Temitayo A. ; Mathur, Akhil ; De C. Williams, Amanda C. ; Lane, Nicholas D. ; Bianchi-Berthouze, Nadia
  • Assuntos: Applied computing -- Life and medical sciences -- Health informatics ; Human-centered computing -- Ubiquitous and mobile computing
  • É parte de: Proceedings of the 23rd International Symposium on Wearable Computers, 2019, p.225-230
  • Descrição: In chronic pain physical rehabilitation, physiotherapists adapt exercise sessions according to the movement behavior of patients. As rehabilitation moves beyond clinical sessions, technology is needed to similarly assess movement behaviors and provide such personalized support. In this paper, as a first step, we investigate automatic detection of protective behavior (movement behavior due to pain-related fear or pain) based on wearable motion capture and electromyography sensor data. We investigate two recurrent networks (RNN) referred to as stacked-LSTM and dual-stream LSTM, which we compare with related deep learning (DL) architectures. We further explore data augmentation techniques and additionally analyze the impact of segmentation window lengths on detection performance. The leading performance of 0.815 mean F1 score achieved by stacked-LSTM provides important grounding for the development of wearable technology to support chronic pain physical rehabilitation during daily activities.
  • Editor: New York, NY, USA: ACM
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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