Accurate Ambulatory Gait Analysis in Walking and Running Using Machine Learning Models

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 28; no. 1; pp. 191 - 202
• Main Authors: Zhang, Huanghe, Guo, Yi, Zanotto, Damiano
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ambulatory gait analysis; Correlation coefficients; Cost analysis; Data models; Equipment costs; Feet; Foot; Footwear; Gait; Gait recognition; Insoles; instrumented footwear; Instruments; Laboratories; Laboratory equipment; Learning algorithms; Legged locomotion; Machine learning; machine learning regression; Mathematical models; Parameter estimation; Portable equipment; Regression analysis; Running; Sensors; Support vector machines; Task analysis; Treadmills; Velocity; Walking; Wearable technology
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2019.2958679

Wearable sensors have been proposed as alternatives to traditional laboratory equipment for low-cost and portable real-time gait analysis in unconstrained environments. However, the moderate accuracy of these systems currently limits their widespread use. In this paper, we show that support vector regression (SVR) models can be used to extract accurate estimates of fundamental gait parameters (i.e., stride length, velocity, and foot clearance), from custom-engineered instrumented insoles (SportSole) during walking and running tasks. Additionally, these learning-based models are robust to inter-subject variability, thereby making it unnecessary to collect subject-specific training data. Gait analysis was performed in N=14 healthy subjects during two separate sessions, each including 6-minute bouts of treadmill walking and running at different speeds (i.e., 85% and 115% of each subject's preferred speed). Gait metrics were simultaneously measured with the instrumented insoles and with reference laboratory equipment. SVR models yielded excellent intraclass correlation coefficients (ICC) in all the gait parameters analyzed. Percentage mean absolute errors (MAE%) in stride length, velocity, and foot clearance obtained with SVR models were 1.37%±0.49%, 1.23%±0.27%, and 2.08%±0.72% for walking, 2.59%±0.64%, 2.91%±0.85%, and 5.13%±1.52% for running, respectively. These findings provide evidence that machine learning regression is a promising new approach to improve the accuracy of wearable sensors for gait analysis.