Simultaneous Locomotion Mode Classification and Continuous Gait Phase Estimation for Transtibial Prostheses

Recognizing and identifying human locomotion is a critical step to ensuring fluent control of wearable robots, such as transtibial prostheses. In particular, classifying the intended locomotion mode and estimating the gait phase are key. In this work, a novel, interpretable, and computationally efficient algorithm is presented for simultaneously predicting locomotion mode and gait phase. Using able-bodied (AB) and transtibial prosthesis (PR) data, seven locomotion modes are tested including slow, medium, and fast level walking (0.6, 0.8, and 1.0 m/s), ramp ascent/descent (5 degrees), and stair ascent/descent (20 cm height). Overall classification accuracy was 99.1\(\%\) and 99.3\(\%\) for the AB and PR conditions, respectively. The average gait phase error across all data was less than 4\(\%\). Exploiting the structure of the data, computational efficiency reached 2.91 \(\mu\)s per time step. The time complexity of this algorithm scales as \(O(N\cdot M)\) with the number of locomotion modes \(M\) and samples per gait cycle \(N\). This efficiency and high accuracy could accommodate a much larger set of locomotion modes (\(\sim\) 700 on Open-Source Leg Prosthesis) to handle the wide range of activities pursued by individuals during daily living.