IEEE Trans Biomed Eng. 2023 May;70(5):1673-1682. doi: 10.1109/TBME.2022.3225065.
Due to prolonged incorrect sitting posture, upper body musculoskeletal disorders (UBMDs) are largely widespread among sedentary workers. Monitoring employees' sitting behaviors could be of great help in minimizing UBMDs' occurrence. In addition, being primarily influenced by psycho-physical stress conditions, respiratory rate (RR) would be a further useful parameter to delineate the workers' state of health. Wearable systems have emerged as a viable option for sitting posture and RR monitoring since enable continuous data collecting with no posture disturbances. Nevertheless, the main limits are poor fit, cumbersomeness, and movement restriction resulting in discomfort for the user. In addition, only few wearable solutions can track both these parameters contextually. To address these problems, in this study a flexible wearable system composed of seven modular sensing elements based on fiber Bragg grating (FBG) technology and designed to be worn on the back has been proposed to recognize the most common sitting postures (i.e., kyphotic, upright and lordotic) and estimate RR. The assessment was performed on ten volunteers showing good performances in postures recognition via Naïve Bayes classificator (accuracy >96.9%) and agreement with the benchmark in RR estimation (MAPE ranging between 0.74% and 3.83%, MODs close to zero, and LOAs between 0.76 bpm and 3.63 bpm). The method was then successfully tested on three additional subjects under different breathing conditions. The wearable system could offer great support for a better understanding of the workers' posture attitudes and contribute to gathering RR information to depict an overall picture of the users' state of health.
由于长时间坐姿不正确，久坐的上班族中，上半身肌肉骨骼疾病（UBMD）的发病率很高。监测员工的坐姿行为对于最大限度地减少 UBMD 的发生非常有帮助。此外，呼吸频率（RR）主要受心理生理应激条件的影响，因此也是一个有用的参数，可以用来描绘工人的健康状况。由于可实现连续数据采集且不会干扰姿势，可穿戴系统已成为坐姿和 RR 监测的可行选择。然而，其主要限制是佩戴不舒适，贴合度差，且活动受限。此外，仅有少数可穿戴解决方案可以同时跟踪这两个参数。为了解决这些问题，本研究提出了一种由七个基于光纤布拉格光栅（FBG）技术的模块化传感元件组成的灵活可穿戴系统，设计用于佩戴在背部，以识别最常见的坐姿（即脊柱后凸、直立和前凸）并估计 RR。通过 Naive Bayes 分类器（准确率>96.9%）对十名志愿者进行了评估，该分类器在坐姿识别方面表现良好，并且与 RR 估计的基准值具有很好的一致性（平均绝对百分比误差（MAPE）在 0.74%到 3.83%之间，均方根差（MODs）接近 0，平均绝对误差（LOAs）在 0.76 bpm 到 3.63 bpm 之间）。然后，该方法在另外三名不同呼吸条件下的受试者中成功进行了测试。该可穿戴系统可以为更好地了解工人的姿势态度提供很大支持，并有助于收集 RR 信息，以描绘用户健康状况的全貌。
