In this study we presented and validated a real-time gait event detection algorithm that utilizes CoP signal from the single-belt instrumented treadmill. By focusing on the CoP signal, the proposed algorithm eliminates the need for additional sensors attached to the participant's lower extremities or body, simplifying the setup process for the everyday rehabilitation therapy. The algorithm was designed to overcome the limitations of current CoP-based gait event detection methods, which may not identify cross-step events. The results of the study demonstrated the effectiveness and reliability of the proposed algorithm in accurately detecting cross-step events, as well as other gait events during native gait. Validation was performed on different datasets assessed during native and perturbed walking requiring cross-steps in healthy and impaired populations.
The distinctive pattern of cross-step performance, characterized by a deviation from the butterfly-shaped CoP movement observed in the native gait, formed the basis for developing the algorithm. The algorithm was successful when recognizing gait events during native walking patterns of all participants. Furthermore, it successfully recognized the majority of cross-steps, with a high average accuracy rate of 94%. However, there were some cases where gait events within the cross-step period were misclassified or missed entirely, resulting in a failure rate of approximately 6%. These misclassifications could be attributed to the complexity of individual cross-step events, which disrupted the CoP pattern and posed a challenge to the algorithm. It is important to note that in instances of misclassifications, the algorithm continues its operation without interruption. Rather, it simply overlooks the occurrence of a gait event and awaits the subsequent one, thereby demonstrating high level of robustness, which is needed particularly when utilized in real-time biofeedback provision during gait training.
The delays in detecting gait events were analyzed for both native gait and cross-step events. The algorithm exhibited a temporal delay in detecting gait events compared to their actual occurrences, which is a common characteristic of real-time gait event detection algorithms [6, 7, 22, 30]. The delays related to heel strikes during native gait were smaller than during cross-step events, suggesting that the algorithm could effectively capture the distinct features of cross-step events. However, the delays for toe-off events were bigger during both native gait and cross-step events, which is a result of the algorithm’s parameter selection.
The algorithm has variety of parameters that implicitly define the accuracy and reliability of detecting gait events and cross-steps. Here, if the CoP is very fluctuant due to various possible reasons: variable pathological gait as also reported in [10], precision and resolution of the force plates and the CoP filtering [31]; the parameters in the proposed algorithm such as threshold ratio need to be appropriate not to false detect a local extrema of the CoP. There are also low-pass filters embedded in the measuring amplifiers or additionally added in the data acquisition software in order to cut away higher degree of oscillations of the CoP, however, real-time filtering on the other hand introduces time delay into the system by itself. Similar issue was reported in [22], where more conservative CoP peak detection sensitivity introduces temporal delay of 0.1 s between true CoP peak occurrence and the online detected one.
The algorithm's performance was evaluated on different populations, including healthy participants, subjects with unilateral transtibial amputation, and individuals after stroke. The results demonstrated that the algorithm was effective across these populations, with no significant difference in the cross-step detection success rate among different pathologies. This finding suggests that the algorithm can be applied in diverse clinical and research settings, making it a potentially valuable tool for gait analysis and training in clinical rehabilitation.
Limitations
The present study on the proposed real-time CoP-based algorithm that detects gait events and cross-steps has several limitations. To ensure successful operation of the algorithm, at least one of the CoP (ML or AP) signals must exhibit deterministic behaviour, clearly displaying signal peaks. In cases where the CoP signals are non-deterministic and the CoP oscillates unpredictably during stepping, the algorithm's functioning may be compromised. However, once a predictable CoP pattern is established, the algorithm is designed to persistently operate. Additionally, if the CoP signal lacks clear expression, causing the algorithm to fail in detecting gait events, those events will be skipped until the CoP pattern becomes sufficiently evident. This limitation highlights the dependency of the algorithm's performance on the clarity of the CoP signal, indicating that it may not accurately capture all gait events in situations where the CoP expression is indistinct. Another limitation of this study is related to the dataset used for algorithm evaluation. We included a variety of gait measurements from our database, where participants performed cross-steps during studies on dynamic balance responses following perturbations. Here, the datasets across different groups included varying walking speeds and perturbation amplitudes ranging from 5–15% of body weight. This variability in the dataset made it challenging to conduct direct statistical analyses. However, the diversity in the datasets proves beneficial for the analysis of the algorithm itself, as it exposes the algorithm to a wider range of CoP behaviours and allows for a comprehensive evaluation.