What OBJECTIVE method is your doctor and therapist using to determine EXACTLY your gait disabilities? So they can provide EXACT REHAB PROTOCOLS! Oh, you don't have a functioning stroke doctor or therapist because they are doing nothing to objectively determine your problems or have fixes for them? So why are you seeing them if they are that incompetent?
Cross-step detection using center-of-pressure based algorithm for real-time applications
Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 161 (2024)
Abstract
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.
Introduction
Gait analysis plays a crucial role in understanding human locomotion and assessing the effectiveness of rehabilitation therapies [1,2,3]. Accurate and reliable real-time detection of gait events, such as heel strikes (HS) and toe offs (TO), is essential for monitoring various gait parameters as well as for providing real-time biofeedback during gait training. Such immediate feedback allows researchers and clinicians to evaluate data in real-time, as well as to make on-the-fly adjustments and interventions. In rehabilitation, it is crucial to tailor exercises to an individual's gait pattern and abilities to maximize the effectiveness of the training. This ensures that the exercises are both appropriate and beneficial for the individual's specific needs. Real-time gait event detection also plays a key role in prosthetics, enabling responsive control for artificial limbs and resulting in a more natural walking experience. Additionally, it is essential for fall detection systems for the elderly, triggering timely preventive measures. Overall, real-time gait event detection enhances customization and optimization in rehabilitation programs, ensuring effective and personalized treatment. Conventionally, online gait event detection algorithms rely on various sensors (Inertial Measurement Units – IMU, pressure insoles, angular sensors or optical tracking systems) attached to the lower limbs or body to capture the kinematics of movement [4,5,6,7]. Particularly, IMU systems, whether using a single or multiple units, have the capability to detect gait events, though their success depends on several factors such as the quality of the IMU units, signal processing techniques, individual's gait behaviour or suitable sensor positioning [8, 9]. However, these methods can be often burdensome for participants, have potential issues with synchronization and often limit the practicality of the procedure setup, especially for everyday use in a clinical environment [10].
In recent years, instrumented treadmills equipped with force transducers to measure Center of Pressure (CoP) during walking, including single or split-belt treadmill variations, have gained popularity as a valuable tool for gait analysis and training [11,12,13,14]. Here, the CoP represents the point where the vertical ground reaction force is applied and can provide valuable insights into gait dynamics [15,16,17,18]. Leveraging the CoP signal, researchers have developed real-time algorithms for detecting gait events and gait subphases without the need for additional human add-on sensors [16, 19,20,21]. Additionally, force signals from the instrumented split-belt treadmills have been used by the researchers to employ a thresholding method based on force data for detecting gait events [22, 23]. However, it's important to note that this technique is applicable solely during native (unperturbed) gait, where each leg makes contact with each belt of the treadmill. Conversely, in the case of a cross-step, both feet are in contact with only one belt, rendering the conventional method ineffective.
Two related studies have examined the CoP-based algorithm, one involving healthy participants and the other focusing on subjects with amputations [16, 20]. In the both studies, participants performed their native gait, revealing asymmetrical butterfly-shaped CoP signals assessed in people with amputations [20] as opposed to the unimpaired gait in healthy subjects [16]. The acquired signals were analyzed to assess specific gait characteristics such as step length, width, time, and durations of double and single support. However, these studies did not include analysis and evaluation of perturbed walking conditions.
One particular challenge for real-time gait event detection algorithms is accurate identification of stepping responses during perturbation-based balance training (PBT). PBT has emerged as a valuable approach for improving balance control and reducing the risk of falls in the elderly and neurologically impaired [24,25,26]. During PBT, individuals experience controlled perturbations that challenge their stability, leading to different reactive balance strategies. These strategies involve adjustments in step length and width to regain dynamic stability. Among these adjustments, cross-steps have been identified as reactive balance responses following outward perturbations, where individuals cross their legs during the gait cycle to stabilize the body and restore equilibrium [27,28,29]. Therefore, accurately detecting cross-steps poses a significant challenge to traditional algorithms, as cross-steps disrupt the expected "butterfly-shaped" pattern of CoP movement following externally or internally elicited perturbation [27, 28]. While these studies have explored gait abnormalities caused by pathology or varying step widths and lengths, the specific scenario of crossing legs during walking has not been thoroughly investigated. Consequently, there is a gap in contemporary methods that can reliably examine cross-step events. Currently, the lack of real-time algorithms capable of detecting and quantifying cross-steps without the need for wearable sensors is a notable limitation hindering the use of real-time biofeedback during gait training.
The aim of this study was to develop a real-time algorithm that utilizes the CoP signal from a single-belt instrumented treadmill to accurately detect HS and TO events during both native and perturbed gait, specifically addressing cross-step events. We conducted extensive experiments to evaluate the algorithm's reliability and accuracy on diverse populations, including healthy participants, subjects with unilateral transtibial amputations, and individuals after stroke, across different walking speeds.
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