IMU-based gait analysis in lower limb prosthesis users: Comparison of step demarcation algorithms

• Published in: Gait & posture 2018-07, Vol.64, p.30-37
• Main Authors: Bastas, Gerasimos, Fleck, Joshua J., Peters, Richard A., Zelik, Karl E.
• Format: Article
• Language: English
• Subjects: Acceleration; Accelerometer; Accelerometry - methods; Adult; Algorithms; Amputation; Artificial Limbs; Female; Gait; Gait - physiology; Humans; Inertial measurement unit; Lower Extremity - physiopathology; Male; Middle Aged; Prosthesis; Reproducibility of Results; Walking - physiology; Walking Speed - physiology; Young Adult
• ISSN: 0966-6362; 1879-2219
• DOI: 10.1016/j.gaitpost.2018.05.025

•Tested 33 lower-limb prosthetic users (LLPUs): 17 transtibial, 16 transfemoral.•Compared 3 step demarcation algorithms based on IMU tracking of lumbopelvic motion.•Choice of algorithm affected the quality / interpretation of spatiotemporal metrics.•Fore-aft acceleration zero-crossing algorithm outperformed peak detection algorithm.•Zero-crossing step demarcation algorithm is preferable for LLPU gait analysis. Inertial Measurement Unit (IMU)-based gait analysis algorithms have previously been validated in healthy controls. However, little is known about the efficacy, performance, and applicability of these algorithms in clinical populations with gait deviations such as lower limb prosthesis users (LLPUs). To compare the performance of 3 different IMU-based algorithms to demarcate steps from LLPUs. We used a single IMU sensor affixed to the midline lumbopelvic region of 17 transtibial (TTA), 16 transfemoral (TFA) LLPUs, and 14 healthy controls (HC). We collected acceleration and angular velocity data during overground walking trials. Step demarcation was evaluated based on fore-aft acceleration, detecting either: (i) maximum acceleration peak, (ii) zero-crossing, or (iii) the peak immediately preceding a zero-crossing. We quantified and compared the variability (standard deviation) in acceleration waveforms from superposed step intervals, and variability in step duration, by each algorithm. We found that the zero-crossing algorithm outperformed both peak detection algorithms in 65% of TTAs, 81% of TFAs, and 71% of HCs, as evidenced by lower standard deviations in acceleration, more consistent qualitative demarcation of steps, and more normally distributed step durations. The choice of feature-based algorithm with which to partition IMU waveforms into individual steps can affect the quality and interpretation of estimated gait spatiotemporal metrics in LLPUs. We conclude that the fore-aft acceleration zero-crossing serves as a more reliable feature for demarcating steps in the gait patterns of LLPUs.