Convolutional long short-term memory model for recognizing postures from wearable sensor

Junqi Zhao, Esther Obonyo

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations

Abstract

This research investigates the feasibility and viability of applying Deep Neural Networks (DNN) to improve performance with respect to posture recognition based on multi-channel motion data from Wearable Sensors (WS). The authors use the recognition of posture that can be linked to risk of Musculoskeletal Disorder (MSD)- among construction workers as the testbed. The proposed approach is based on the use of a DNN model integrating Convolutional Neural Network (CNN) and Long short-term memory (LSTM) that can achieve automated feature engineering and sequential pattern detection. The model performance was evaluated using datasets collected from four construction workers. The proposed model outperformed baseline CNN and LSTM models. Under the personalized modelling approach, it improved recognition performance by 3% from the benchmark Machine Learning models; the improvement is 2% for generalized modelling approach. The proposed model achieves high-performance posture recognition, which facilitates the MSD prevention in construction through monitoring injury-related postures.

Original languageEnglish (US)
JournalCEUR Workshop Proceedings
Volume2394
StatePublished - Jan 1 2019
Event26th International Workshop on Intelligent Computing in Engineering, EG-ICE 2019 - Leuven, Belgium
Duration: Jun 30 2019Jul 3 2019

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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