Real-time cross-step detection using center-of-pressure based algorithm

Abstract

Abstract Background Gait event detection is crucial for assessment, evaluation and provision of biofeedback of during rehabilitation of walking. Existing online gait event detection algorithms mostly rely on add-on sensors, limiting their practicality. Instrumented treadmills offer a promising alternative by utilizing the Center of Pressure (CoP) signal for real-time gait event detection. However, current methods have limitations, particularly in detecting cross-step events during perturbed walking conditions. Methods We present and validate a real-time CoP-based algorithm to detect gait events and cross-steps, which combines thresholding and logic techniques. The algorithm was evaluated on CoP datasets from healthy participants, stroke survivors, and unilateral amputees that underwent perturbation-based balance assessments, encompassing different walking speeds. Real-time detected gait events were compared to offline identified counterparts in order to present related temporal delays and success rate. Results The proposed algorithm demonstrated high accuracy in detecting gait events during native gait, as well as cross-step events during perturbed walking conditions. It successfully recognized the majority of cross-steps, with a detection success rate of 94%. However, some misclassifications or missed events occurred, mainly due to the complexity of cross-step events. Temporal delays for heel-strikes during native gait and cross-step events averaged at 78 ms and 64 ms respectively, while toe-off delays were 126 ms and 111 ms respectively. Conclusion The proposed algorithm represents an advancement in gait event detection on instrumented treadmills. By leveraging the CoP signal, it successfully identifies gait events and cross-steps in real-time, providing valuable insights into human locomotion. The algorithm's ability to accommodate diverse CoP patterns enhance its applicability to a wide range of individuals and gait characteristics. The algorithm's performance was consistent across different populations, suggesting its potential for diverse clinical and research settings, particularly in the domains of gait analysis and rehabilitation practices.