A Smart Chair to Monitor Sitting Posture by Capacitive Textile Sensors

Abstract

In this paper, a smart office chair with movable textile sensors to monitor sitting position during the workday is presented. The system consists of a presence textile capacitive sensor with different levels of activation with a signal conditioning device. The proposed system was integrated into an office chair to detect postures that could provoke musculoskeletal disorders or discomfort. The microcontroller measured the capacitance by means of a cycle count method and provided the position information in real time. The information could be analysed to set up warnings to prevent incorrect postures or the necessity to move. Five participants assumed a series of postures, and the results showed the workability of the proposed smart chair. The chair can be provided as a new tool for companies, hospitals, or other institutions to detect incorrect postures and monitor the postures of people with reduced mobility. This tool can optimise control procedures or prevent occupational risks.

Smart Sensing Chairs for Sitting Posture Detection, Classification, and Monitoring: A Comprehensive Review

“…Another study proposed an "eCushion" device that incorporated an "eTextile" pressure sensor array that could also detect seven different sitting postures with 85.90% accuracy [52]. Additionally, Martínez-Estrada et al [26] developed 10 detachable textile pressure sensors (PreCaTex) (shown in Figure 5b) that were placed at strategic points around a chair.…”

“…Mutlu et al in 2007 [37] integrated 19 different FSRs into a seating cushion and used the Simple Logistic Regression ML algorithm to achieve 78% accuracy in classifying 10 different postures. Martínez-Estrada et al [26] placed six textile sensors on a backrest and an additional four sensors on a seating cushion in order to classify eight sitting postures, as shown in Figure 7a. Tsai et al [40] used 13 pressure sensors to classify 10 sitting postures and was able to achieve an accuracy of 99.10% using the SVM algorithm.…”

“…In the context of posture classification, these systems are configured to establish specific data thresholds during the testing phase, which are used to align sensor readings with corresponding sitting postures. Our review documented several studies that effectively utilized rule-based systems for posture recognition [26,27,32,61].…”

Smart-Sensing Chairs for Sitting Posture Detection, Classification and Monitoring: A Comprehensive Review

“…Another study proposed a "eCushion" device which incorporated an "eTextile" pressure sensor array that could detect 7 different sitting postures at 85.9% accuracy [34]. Additionally, Martínez-Estrada et al [32] developed 10 detachable textile pressure sensor (PreCaTex) (shown in Figure 5b) which were placed at strategic points around the chair.…”

“…Mutlu et al in 2007 [53] integrated 19 different FSRs into the seating cushion and used the Simple Logistic Regression ML algorithm to achieve 78% accuracy in classifying 10 different postures. Martínez-Estrada et al [32] placed 6 textile sensors on the backrest and an additional 4 sensors on the seating cushion in order to classify 8 sitting postures as show in Figure 7a. Tsai et al [54] used 13 pressure sensors to classify 10 sitting postures and was able to achieve an accuracy of 99.10% using the SVM ML algorithm.…”