Accurate and Real-Time Hierarchical Ensemble Network for Activity Classification in Construction Worker

Accurate and real-time locomotion classification is crucial for exoskeletons to assist construction workers in completing multiple tasks. However, state-of-the-art algorithms for classifying multiple activities face multifaceted challenges in both accuracy and real-time capability. In addition, adva...

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Veröffentlicht in:IEEE journal of biomedical and health informatics Jg. 29; H. 8; S. 5479 - 5492
Hauptverfasser: Zuo, Guoyu, Wu, Qifei, Gao, Wenbin, Li, Cheng, Sun, Liangkun, Yu, Shuangyue
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.08.2025
Schlagworte:
Accuracy
Activity classification
Adult
Algorithms
Assistive devices
Classification algorithms
Construction Industry
construction worker
Deep learning
Exoskeleton Device
Exoskeletons
Feature extraction
hierarchical ensemble network
Humans
IMU configuration
Inference algorithms
Locomotion - physiology
Male
Neural Networks, Computer
Performance evaluation
Real-time systems
Signal processing algorithms
Signal Processing, Computer-Assisted
Wearable Electronic Devices
wearable exoskeletons
Young Adult
ISSN:2168-2194, 2168-2208, 2168-2208
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Zusammenfassung:Accurate and real-time locomotion classification is crucial for exoskeletons to assist construction workers in completing multiple tasks. However, state-of-the-art algorithms for classifying multiple activities face multifaceted challenges in both accuracy and real-time capability. In addition, advanced studies typically provide a single solution based on certain sensor combinations, which may have an indirect impact on different assistive devices (e.g., an algorithm using feet IMUs is not suited for bilateral portable hip exoskeletons or unilateral knee exoskeletons), limiting its practicality and applicability in diverse applications. To fill these two gaps, first, we developed a novel hierarchical ensemble network framework that can accurately and real-time classify 11 typical lower limb activities of construction workers. Second, building upon this hierarchical ensemble network framework, we developed 6 configurations wearing IMU sensors on different body segments, which are potentially used for different wearable devices. Experimental results with leave-one-out cross-validation obtained from 10 able-bodied subjects validated the effectiveness of the proposed algorithm. Compared to the baseline ANN-based algorithm, our algorithm under 6 configurations on average was able to improve accuracy, precision, recall, and F1-score by 4.97%, 3.40%, 4.97%, and 5.31%, respectively, and reduce the number of parameters and inference time by 71.86% and 47.85%, respectively. This study showcases multiple solutions with different wearable sensor configurations, offering high accuracy and strong real-time performance for classifying multiple activities, which can be deployed to controllers for multiple types of assistive devices targeting construction workers.
Bibliographie:ObjectType-Article-1
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2025.3561380