Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487, USA.
College of Nursing, University of Alabama, Tuscaloosa, AL 35487, USA.
Sensors (Basel). 2021 May 30;21(11):3790. doi: 10.3390/s21113790.
Ankle injuries may adversely increase the risk of injury to the joints of the lower extremity and can lead to various impairments in workplaces. The purpose of this study was to predict the ankle angles by developing a footwear pressure sensor and utilizing a machine learning technique. The footwear sensor was composed of six FSRs (force sensing resistors), a microcontroller and a Bluetooth LE chipset in a flexible substrate. Twenty-six subjects were tested in squat and stoop motions, which are common positions utilized when lifting objects from the floor and pose distinct risks to the lifter. The kNN (k-nearest neighbor) machine learning algorithm was used to create a representative model to predict the ankle angles. For the validation, a commercial IMU (inertial measurement unit) sensor system was used. The results showed that the proposed footwear pressure sensor could predict the ankle angles at more than 93% accuracy for squat and 87% accuracy for stoop motions. This study confirmed that the proposed plantar sensor system is a promising tool for the prediction of ankle angles and thus may be used to prevent potential injuries while lifting objects in workplaces.
踝关节损伤可能会增加下肢关节受伤的风险，并可能导致工作场所的各种障碍。本研究旨在通过开发一种鞋内压力传感器并利用机器学习技术来预测踝关节角度。鞋内压力传感器由六个 FSR（力感应电阻器）、一个微控制器和一个蓝牙 LE 芯片组组成，安装在柔性基板上。26 名受试者进行了深蹲和弯腰动作测试，这些动作是从地板上抬起物体时常用的姿势，对举重者有明显的风险。使用 kNN（k-最近邻）机器学习算法创建了一个代表性模型来预测踝关节角度。为了验证，使用了商业 IMU（惯性测量单元）传感器系统。结果表明，所提出的鞋内压力传感器在深蹲时可预测踝关节角度的准确率超过 93%，在弯腰时的准确率为 87%。本研究证实，所提出的足底传感器系统是预测踝关节角度的一种有前途的工具，因此可能用于预防工作场所中抬起物体时的潜在伤害。
