School of Electrical Engineering, Pontificia Universidad Católica de Valparaíso, Av. Brasil 2147, Valparaíso 2362804, Chile.
Sensors (Basel). 2022 Apr 7;22(8):2825. doi: 10.3390/s22082825.
Human gait analysis is a standard method used for detecting and diagnosing diseases associated with gait disorders. Wearable technologies, due to their low costs and high portability, are increasingly being used in gait and other medical analyses. This paper evaluates the use of low-cost homemade textile pressure sensors to recognize gait phases. Ten sensors were integrated into stretch pants, achieving an inexpensive and pervasive solution. Nevertheless, such a simple fabrication process leads to significant sensitivity variability among sensors, hindering their adoption in precision-demanding medical applications. To tackle this issue, we evaluated the textile sensors for the classification of gait phases over three machine learning algorithms for time-series signals, namely, random forest (RF), time series forest (TSF), and multi-representation sequence learner (Mr-SEQL). Training and testing signals were generated from participants wearing the sensing pants in a test run under laboratory conditions and from an inertial sensor attached to the same pants for comparison purposes. Moreover, a new annotation method to facilitate the creation of such datasets using an ordinary webcam and a pose detection model is presented, which uses predefined rules for label generation. The results show that textile sensors successfully detect the gait phases with an average precision of 91.2% and 90.5% for RF and TSF, respectively, only 0.8% and 2.3% lower than the same values obtained from the IMU. This situation changes for Mr-SEQL, which achieved a precision of 79% for the textile sensors and 36.8% for the IMU. The overall results show the feasibility of using textile pressure sensors for human gait recognition.
人体步态分析是一种用于检测和诊断与步态障碍相关疾病的标准方法。由于成本低、便携性高，可穿戴技术越来越多地应用于步态和其他医学分析中。本文评估了使用低成本自制纺织压力传感器来识别步态阶段。将十个传感器集成到弹力裤中，实现了一种廉价且普及的解决方案。然而，如此简单的制造工艺会导致传感器之间的灵敏度存在显著差异，这阻碍了它们在需要高精度的医疗应用中的采用。为了解决这个问题，我们针对三个用于时间序列信号的机器学习算法评估了纺织传感器在步态阶段分类方面的性能，即随机森林（RF）、时间序列森林（TSF）和多表示序列学习者（Mr-SEQL）。训练和测试信号是由穿着传感裤的参与者在实验室条件下的测试运行中生成的，以及从附着在同一条裤子上的惯性传感器生成的，用于比较目的。此外，还提出了一种新的注释方法，该方法使用普通网络摄像头和姿势检测模型来创建此类数据集，该方法使用预定义规则生成标签。结果表明，纺织传感器能够成功检测步态阶段，RF 和 TSF 的平均精度分别为 91.2%和 90.5%，仅比从 IMU 获得的相同值低 0.8%和 2.3%。对于 Mr-SEQL 情况则有所不同，它为纺织传感器实现了 79%的精度，而 IMU 的精度为 36.8%。总体结果表明，使用纺织压力传感器进行人体步态识别是可行的。
