# Gait Phase Identification and Damping Control for Knee Orthosis Using Time Series Forest Classifier
###### Abstract  
Knee orthosis plays a vital role in enhancing the wellbeing and quality of life of individuals suffering from knee arthritis. This study explores a machine-learning-based methodology for predicting a user's gait subphase using inertial measurement units (IMUs) for a semiactive orthosis. A musculoskeletal simulation is employed with the help of existing experimental motion-capture data to obtain essential metrics related to the gait cycle, which are then normalized and scaled. A meticulous data capture methodology using foot switches is used for precise synchronization with IMU data, resulting in comprehensive labeled subphase datasets. The integration of simulation results and labeled datasets provides activation data for effective knee flexion damping following which multiple supervised machine learning algorithms are trained and evaluated for performances. The time series forest classifier emerged as the most suitable algorithm, with an accuracy of 86 percent, against randomized convolutional kernel transform, K-neighbor time series classifier, and long short-term memory-fully convolutional network, with accuracies of 68, 76, and 78, respectively, showcasing exceptional performance scores, thereby rendering it an optimal choice for identifying gait subphases and achieving the desired level of damping for magnetorheological brake-mounted knee orthosis based on simulated results. knee orthosis; inertial measurement unit; machine learning +
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knee moment and its relationship with gait provides valuable insights for guiding treatment and rehabilitation strategies to address specific gait abnormalities and improve overall mobility [6]. Machine learning, a subset of artificial intelligence, is focused on enabling computer systems to improve their performance on specific tasks without being explicitly programmed. This field involves the utilization of algorithms that learn from data through mathematical and statistical methodologies, allowing them to recognize patterns and relationships. By leveraging these acquired patterns and relationships, machine learning algorithms can make predictions, uncover hidden insights, and accomplish various tasks [7]. Gait subphase detection entails the utilization of machine learning techniques to identify and categorize various subphases within the gait cycle. These subphases are specific windows of time where the detection and activation of the MR brake occur to control knee flexion and assess knee stress based on the knee moment parameter. By analyzing gait data using machine learning algorithms, it becomes possible to automatically recognize and distinguish various subphases, such as heel strike, toe-off, swing phase, and stance phase, among others. This approach provides a more precise and comprehensive understanding of gait patterns, offering potential advancements in areas like clinical assessment, rehabilitation, and biomechanical research involving human movement [8]. [PERSON] et al. described a unique method for identifying gait phases by utilizing an optical approach with passive markers. It uses fuzzy logic to automatically recognize different subphases of gait, resulting in a precise and reliable measurement useful for analyzing gait abnormalities in patients and for the use in the development of control strategies for active lower-extremity prosthetics and orthotics [9]. [PERSON] et al. proposed a distributed classifier for gait phase detection in an active knee orthosis for pediatric subjects with neurological diseases. The classifier utilizes a hierarchical weighted decision framework applied to scalar hidden Markov models trained on kinematic data from the shank and thigh. Results show that the classifier based on angular velocities achieves higher sensitivity and specificity. Utilizing additional linear acceleration data does not improve performance, suggesting a need for a gait phase detection algorithm based on gyroscopes placed on the shank and thigh for the orthosis implementation [10].