Division of Ergonomics, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, 141 57 Huddinge, Sweden.
Unit of Occupational Medicine, Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden.
Sensors (Basel). 2024 Jun 27;24(13):4173. doi: 10.3390/s24134173.
Hand-intensive work is strongly associated with work-related musculoskeletal disorders (WMSDs) of the hand/wrist and other upper body regions across diverse occupations, including office work, manufacturing, services, and healthcare. Addressing the prevalence of WMSDs requires reliable and practical exposure measurements. Traditional methods like electrogoniometry and optical motion capture, while reliable, are expensive and impractical for field use. In contrast, small inertial measurement units (IMUs) may provide a cost-effective, time-efficient, and user-friendly alternative for measuring hand/wrist posture during real work. This study compared six orientation algorithms for estimating wrist angles with an electrogoniometer, the current gold standard in field settings. Six participants performed five simulated hand-intensive work tasks (involving considerable wrist velocity and/or hand force) and one standardised hand movement. Three multiplicative Kalman filter algorithms with different smoothers and constraints showed the highest agreement with the goniometer. These algorithms exhibited median correlation coefficients of 0.75-0.78 for flexion/extension and 0.64 for radial/ulnar deviation across the six subjects and five tasks. They also ranked in the top three for the lowest mean absolute differences from the goniometer at the 10th, 50th, and 90th percentiles of wrist flexion/extension (9.3°, 2.9°, and 7.4°, respectively). Although the results of this study are not fully acceptable for practical field use, especially for some work tasks, they indicate that IMU-based wrist angle estimation may be useful in occupational risk assessments after further improvements.
手工密集型工作与各种职业中的手部/腕部和其他上半身区域的与工作相关的肌肉骨骼疾病（WMSD）密切相关，包括办公室工作、制造业、服务业和医疗保健。解决 WMSD 的普遍性需要可靠和实用的暴露测量方法。传统方法，如电子角度计和光学运动捕捉，虽然可靠，但昂贵且不适合现场使用。相比之下，小型惯性测量单元（IMU）可能为在实际工作中测量手部/腕部姿势提供一种具有成本效益、时间效率和用户友好的替代方案。本研究比较了六种用于估计手腕角度的定向算法与当前现场设置中的金标准电子角度计。六名参与者执行了五个模拟手工密集型工作任务（涉及相当大的手腕速度和/或手部力量）和一个标准化手部动作。三种具有不同平滑度和约束的乘法卡尔曼滤波器算法与角度计的一致性最高。这三种算法在六个被试者和五个任务中对屈曲/伸展的中位数相关系数为 0.75-0.78，对桡骨/尺骨偏斜的中位数相关系数为 0.64。它们在 10%、50%和 90%腕部屈曲/伸展百分位数处与角度计的平均绝对差异最小，分别排名第三（分别为 9.3°、2.9°和 7.4°）。尽管本研究的结果不完全适用于实际现场使用，特别是对于某些工作任务，但它们表明基于 IMU 的手腕角度估计在进一步改进后可能对职业风险评估有用。
