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 language | English (US) |
|---|---|
| Journal | CEUR Workshop Proceedings |
| Volume | 2394 |
| State | Published - Jan 1 2019 |
| Event | 26th International Workshop on Intelligent Computing in Engineering, EG-ICE 2019 - Leuven, Belgium Duration: Jun 30 2019 → Jul 3 2019 |
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All Science Journal Classification (ASJC) codes
- Computer Science(all)
Cite this
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Convolutional long short-term memory model for recognizing postures from wearable sensor. / Zhao, Junqi; Obonyo, Esther Adhiambo.
In: CEUR Workshop Proceedings, Vol. 2394, 01.01.2019.Research output: Contribution to journal › Conference article
TY - JOUR
T1 - Convolutional long short-term memory model for recognizing postures from wearable sensor
AU - Zhao, Junqi
AU - Obonyo, Esther Adhiambo
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85069161664&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069161664&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85069161664
VL - 2394
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
SN - 1613-0073
ER -