On-device lumbar-pelvic movement detection using dual-IMU: a DNN-based approach

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Lumbar-pelvic movements (LPMs) are generally performed in the clinical setting to identify limitations in a range of movements. Continuous monitoring of these movements can provide real-time feedback to both patients and medical experts with the potential of identifying activities that may precipitate symptoms of low back pain (LBP) as well as improving therapy by providing a personalised approach. Recent advances in mobile computing technology and wearable sensors have paved the way for developing mobile physical activity monitoring applications with more advanced and complex algorithms, such as deep neural network (DNN) based models. However, there is a lack of prior studies that focus on real-time LPMs detection with multimodal sensory data. Meanwhile, most research in the area of body movement detection do not consider the potential transition logic of the constituent low-level body movements (e.g., LPMs) within their corresponding high-level physical activity. This information could significantly increase accuracy of detection results. Further, current studies mainly perform deep learning-based movement detection in the cloud (or a backend server) that could increase network bandwidth and response time. To address these limitations, this paper proposes a novel LPMs detection approach using an enhanced and adapted hybrid DNN model, which includes a convolutional neural network followed by a long-short term memory recurrent network (CNN-LSTM), and performs detection locally on the mobile device. The results of a comparative evaluation of the proposed model and baseline models are described. We also introduce a set of domain-specific pre-defined rules, based on the transition logic information, to reconstruct the detection outputs to further improve the detection accuracy.

Original languageEnglish
Pages (from-to)62241-62254
Number of pages14
JournalIEEE Access
Publication statusPublished - 21 Apr 2021


  • Adaptation models
  • Computational modeling
  • Deep Neural Networks
  • Domain Adaptation
  • Lumbar-Pelvic Movement Detection
  • Mobile handsets
  • Mobile Platform
  • Monitoring
  • On-device processing
  • Performance evaluation
  • Real-time systems
  • Sensors
  • Temporal-wise Attention

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