Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 640301, Taiwan.
Bachelor Program in Interdisciplinary Studies, National Yunlin University of Science and Technology, Yunlin 640301, Taiwan.
Sensors (Basel). 2023 Jun 25;23(13):5894. doi: 10.3390/s23135894.
Prolonged sitting with poor posture can lead to various health problems, including upper back pain, lower back pain, and cervical pain. Maintaining proper sitting posture is crucial for individuals while working or studying. Existing pressure sensor-based systems have been proposed to recognize sitting postures, but their accuracy ranges from 80% to 90%, leaving room for improvement. In this study, we developed a sitting posture recognition system called SPRS. We identified key areas on the chair surface that capture essential characteristics of sitting postures and employed diverse machine learning technologies to recognize ten common sitting postures. To evaluate the accuracy and usability of SPRS, we conducted a ten-minute sitting session with arbitrary postures involving 20 volunteers. The experimental results demonstrated that SPRS achieved an impressive accuracy rate of up to 99.1% in recognizing sitting postures. Additionally, we performed a usability survey using two standard questionnaires, the System Usability Scale (SUS) and the Questionnaire for User Interface Satisfaction (QUIS). The analysis of survey results indicated that SPRS is user-friendly, easy to use, and responsive.
长时间坐姿不良可能会导致各种健康问题，包括上背痛、下背痛和颈痛。无论是工作还是学习，保持正确的坐姿对个人而言都至关重要。现有的基于压力传感器的系统已经被提出用于识别坐姿，但它们的准确率在 80%到 90%之间，仍有改进的空间。在这项研究中，我们开发了一种称为 SPRS 的坐姿识别系统。我们确定了椅子表面上的关键区域，这些区域捕捉到了坐姿的基本特征，并采用了多种机器学习技术来识别十种常见的坐姿。为了评估 SPRS 的准确性和可用性，我们让 20 名志愿者进行了十分钟的任意坐姿测试。实验结果表明，SPRS 在识别坐姿方面的准确率高达 99.1%。此外，我们使用两个标准问卷，即系统可用性量表（SUS）和用户界面满意度问卷（QUIS），进行了可用性调查。对调查结果的分析表明，SPRS 易于使用、响应迅速且用户友好。
