Cross-step detection using center-of-pressure based algorithm for real-time applications.

BACKGROUND

Gait event detection is crucial for assessment, evaluation and provision of biofeedback 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 CoP-based algorithm to detect gait events and cross-steps in real-time, which combines thresholding and logic techniques. The algorithm was evaluated on CoP datasets from healthy participants (age range 21-61 years), stroke survivors (age range 20-67 years), and people with unilateral transtibial amputation (age range 28-63 years) that underwent perturbation-based balance assessments, encompassing different walking speeds. Detected gait events from a simulated real-time processing operation were compared to offline identified counterparts in order to present related temporal absolute mean errors (AME) 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. AME for heel strikes (HS) during native gait and cross-step events averaged at 78 ms and 64 ms respectively, while toe off (TO) AME were 126 ms and 111 ms respectively. A statistically significant difference in the algorithm's success rate score in detecting gait events during cross-step intervals was observed across various walking speeds in a sample of 12 healthy participants, while there was no significant difference among groups.

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 the simulated real-time processing operation, 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.