School of Engineering, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, Canada.
School of Computer Science, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, Canada.
Sensors (Basel). 2023 Jan 13;23(2):942. doi: 10.3390/s23020942.
Current methods for ergonomic assessment often use video-analysis to estimate wrist postures during occupational tasks. Wearable sensing and machine learning have the potential to automate this tedious task, and in doing so greatly extend the amount of data available to clinicians and researchers. A method of predicting wrist posture from inertial measurement units placed on the wrist and hand via a deep convolutional neural network has been developed. This study has quantified the accuracy and reliability of the postures predicted by this system relative to the gold standard of optoelectronic motion capture. Ten participants performed 3 different simulated occupational tasks on 2 occasions while wearing inertial measurement units on the hand and wrist. Data from the occupational task recordings were used to train a convolutional neural network classifier to estimate wrist posture in flexion/extension, and radial/ulnar deviation. The model was trained and tested in a leave-one-out cross validation format. Agreement between the proposed system and optoelectronic motion capture was 65% with κ = 0.41 in flexion/extension and 60% with κ = 0.48 in radial/ulnar deviation. The proposed system can predict wrist posture in flexion/extension and radial/ulnar deviation with accuracy and reliability congruent with published values for human estimators. This system can estimate wrist posture during occupational tasks in a small fraction of the time it takes a human to perform the same task. This offers opportunity to expand the capabilities of practitioners by eliminating the tedium of manual postural assessment.
目前的人体工程学评估方法通常使用视频分析来估计职业任务中的手腕姿势。可穿戴传感器和机器学习有可能实现这项繁琐任务的自动化，从而大大扩展临床医生和研究人员可用的数据量。已经开发出一种通过深度卷积神经网络对手腕和手上的惯性测量单元进行预测手腕姿势的方法。本研究通过与光电运动捕捉的金标准相比，量化了该系统预测的姿势的准确性和可靠性。10 名参与者在两次佩戴手部和腕部惯性测量单元的情况下进行了 3 种不同的模拟职业任务。职业任务记录的数据用于训练卷积神经网络分类器，以估计手腕的屈伸和桡尺偏角姿势。该模型采用留一交叉验证的方式进行训练和测试。提出的系统与光电运动捕捉的一致性为 65%，κ 值为 0.41，屈伸为 60%，κ 值为 0.48。该系统在屈伸和桡尺偏角的预测中具有准确性和可靠性，与已发表的人类估计值相当。该系统可以在人类执行相同任务所需时间的一小部分时间内估计职业任务中的手腕姿势。这为消除手动姿势评估的繁琐工作，为从业者提供了扩展能力的机会。
