A privacy-preserving and unobtrusive sitting posture recognition system via pressure array sensor and infrared array sensor for office workers. (August 2022)

• Record Type: Journal Article
• Authors: Zhang, Xiangying; Fan, Junming; Peng, Tao; Zheng, Pai; K. M. Lee, C.; Tang, Renzhong
• Is Part Of: Advanced engineering informatics. Volume 53(2022)
• Publication Date: 2022-08
• Subjects: Sitting posture recognition -- Pressure array sensor -- Infrared array sensor -- Ergonomics -- Deep learning; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2022.101690
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: Proposing a privacy-preserving and unobtrusive recognition system. Developing a deep learning model based on multimodal data. Realizing robust recognition of ergonomics-based sitting postures. Abstract: Sitting posture recognition is essential in preventing work-related musculoskeletal disorders (WMSDs). WMSDs are of huge concern for office workers whose working process is averagely 81.8% sedentary. Prevailing studies have utilized cameras, wearables, and pressure sensors to recognize sitting postures. The cameras and wearables can achieve accurate recognition results, while personal privacy concerns and inconvenience for long-term use impede their adoption. Meanwhile, the pressure sensors are privacy-preserving and convenient. However, they cannot accurately recognize the sitting posture with different states of the trunk, head, upper extremity, and lower extremity. Considering the pros and cons of those approaches, this study proposes a novel privacy-preserving and unobtrusive sitting posture recognition system, which combines a pressure array sensor with another privacy-preserving sensing technology, i.e., an infrared array (IRA) sensor. Moreover, a deep learning-based sitting posture recognition algorithm is developed, which adopts a feature-level fusion strategy and does not require a complex handcrafted feature extraction process. Based on the ergonomics studies, ten daily sitting postures with the states of different body parts are selected. This system achieved an overall 90.6% accuracy using the leave-subject-out validation approach based on the self-collected dataset from 21 subjects. It has a great potential for privacy-preserving and unobtrusive related applications for sitting posture management.