A Wearable Sensor-based System For Gait Analysis That Is Robust To Gait Variations

Abstract

Gait analysis is widely used to assess deficits following sports injuries or monitor recovery during rehabilitation [1]. While traditional technologies such as motion capture systems and instrumented force plates can be used to measure kinematics during relevant phases of gait, they are confined to lab environments. Advances in inertial measurement unit (IMU) technology provide alternative prospects for portable assessment of both gait events and kinematics. PURPOSE: Evaluate IMU-based system (wearable sensors and autonomous detection algorithms) for detecting gait events and monitoring range of motion (ROM) under controlled gait variations similar to those observed following an injury [1-3]. METHODS: 10 healthy participants (5 M, 5 F, 26.5 ± 2 y.o.) were instrumented with 6 IMUs (Delsys Inc., USA) placed on the sacrum and sternum and bilaterally on the lower/upper legs. Force sensitive resisters (FSR; placed under each foot) and motion capture (Vicon, UK) outcomes were used as gold standard for validating gait events and kinematic measures, respectively. Subjects walked on a treadmill with normal gait followed by gait alterations including: 1) reduced sagittal knee ROM by >30%, 2) reduced sagittal hip ROM by >20%, and 3) increased trunk obliquity by >10%. These gait variations were chosen for their relevance to sport-related injuries such as ACL tear or abdominal strain [1-3]. Our detection algorithms used lower leg IMU data to identify heel strike and toe off, with cycle duration as the time between heel strikes; and IMU data from upper/lower leg, upper leg/sacrum, and sternum to compute knee, hip, and trunk ROM respectively. RESULTS: Cycle duration was detected with <0.5% error with respect to FSRs, and heel strike and toe off were detected within <5% of gait duration across trials, subjects, and gait variations. Knee, hip and trunk ROM were within 5 degrees of those obtained using motion capture for both normal and altered gait. CONCLUSION: Our wearable IMU-based system can accurately detect gait events and calculate gait kinematics during a range of controlled gait variations similar to those resulting from sports injuries. 1] Gokeler et al. Int J Sports Phys Ther, 2013. 2] Young et al. Am J Sports Med, 2014. 3] Hewett et al. Exerc Sport Sci Rev, 2011. Acknowledgments: Work supported in part by NINDS R43HD100209.