Remote Gait Analysis Using Wearable Sensors Detects Asymmetric Gait Patterns in Patients Recovering from ACL Reconstruction

• Published in: Proceedings (International Conference on Wearable and Implantable Body Sensor Networks : Print) pp. 1 - 4
• Main Authors: Gurchiek, Reed D., Choquette, Rebecca H., Beynnon, Bruce D., Slauterbeck, James R., Tourville, Timothy W., Toth, Michael J., McGinnis, Ryan S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2019
• Subjects: Acceleration; accelerometer; Accelerometers; ACL reconstruction; electromyography; gait analysis; Legged locomotion; machine learning; Muscles; Surgery; Thigh; wearable sensor; Wearable sensors
• ISSN: 2376-8894
• DOI: 10.1109/BSN.2019.8771038

This paper presents an automated, wearable sensor-based remote gait analysis method and demonstrates its utility by evaluating gait in patients recovering from anterior cruciate ligament (ACL) reconstruction. Patients wore a single wearable sensor over the rectus femoris of each leg to collect over 15 hours of 3-axis accelerometer and surface electromyography data during daily life. A support vector machine classifier was used to identify four second windows of walking activity. Across all subjects 10,451 strides were extracted from these windows and categorized as occurring during either fast or slow walking. Muscle activation and 3-axis thigh acceleration time-series for each stride were resampled as a percentage of stride cycle and ensemble curves from both legs were compared using correlation as an index of gait pattern symmetry. Our results suggest the proposed method successfully identifies time-series gait asymmetries between affected and unaffected legs when comparing patients early post-surgery ( < \pmb 6 weeks) and later ( > \pmb {14} weeks) in recovery for each walking speed. These results point toward future use of this approach as a digital biomarker for rehabilitation progress in this population.