Recognizing and identifying human locomotion is a critical step to ensuring fluent control of wearable robots, such as transtibial prostheses. In particular, classifying the 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) data and transtibial prosthesis (PR) data collected via a bypass adapter, 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 µs per time step. The time complexity of this algorithm scales as O(N•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 (~ 700 on the Open-Source Leg Prosthesis) to handle the wide range of activities pursued by individuals during daily living.

Powered lower-limb prostheses have shown promise in helping individuals with amputation regain functionality that passive prostheses cannot provide. However, the best method for controlling these devices in coordination with their users is still an open research topic. While powered devices can replicate normative joint kinematics and kinetics, active control also holds the potential to shape system-level characteristics such as the center of mass (CoM) that play an important role in balance. Controlling the prosthesis based on these system-level, or task-space, variables would further represent a new way of coordinating the user and their device.This paper explores the initial implementation of task-space control for a powered ankle prosthesis, characterizing the emergent outcomes of this new coordination strategy. One able-bodied subject walked using a bypass adapter while prosthesis torques were commanded based on reference ground reaction force (GRF) and CoM trajectories. The subject could walk comfortably and continuously at their preferred walking speed, achieving normative ankle torques and joint trajectories despite not tracking explicit joint-level references in stance.

For robotic transtibial prosthesis control, the global tibia kinematics can be used to monitor gait cycle progression and command smooth and continuous actuation. In this work, these global tibia kinematics define a phase variable impedance controller (PVIC), which is implemented as the nonvolitional base controller within a hybrid volitional control framework (PVI-HVC). The gait progression estimation and biomechanic performance of one able-bodied individual walking on a robotic ankle prosthesis via a bypass adapter are compared for three control schemes: benchmark passive controller, PVIC, and PVI-HVC. The different actuation of each had a direct effect on the global tibia kinematics, but the average deviation between the estimated and ground truth gait percentages were 1.6%, 1.8%, and 2.1%, respectively, for each controller. Both PVIC and PVI-HVC produced good agreement with able-bodied kinematic and kinetic references. As designed, PVI-HVC results were similar to those of PVIC when the user used low volitional intent, but yielded higher peak plantarflexion, peak torque, and peak power when the user commanded high volitional input in late stance. This additional torque and power also allowed the user to volitionally and continuously achieve activities beyond level walking, such as ascending ramps, avoiding obstacles, standing on tip-toes, and tapping the foot. In this way, PVI-HVC offers the kinetic and kinematic performance of the PVIC during level ground walking, along with the freedom to volitionally pursue alternative activities.

Phase variable control based on global tibia kinematics holds promise for predicting gait cycle progression to continuously control robotic transtibial prostheses. Calibration of the phase variable is critical to ensure its monotonic behavior, to approach a linear relationship with gait percentage, and to accurately predict the percentage of gait. This paper compares four calibration approaches using data from 22 able-bodied subjects walking at 14 speeds [1]. The typical pure centering (PC) approach employed for thigh-based phase variables is not viable, yielding monotonic phase progression in fewer than half of the cases. An optimization (OPT) approach found monotonic calibrations in 305/308 cases with high linearity (average R2 of 0.91). Critical point centering (CPC) approximates the OPT performance, with 274/308 monotonic calibrations and an average R2 of 0.85, whereas the related vertical weighted average (VWA) approach was only slightly better than PC. All four approaches are similarly accurate in predicting gait percentage, staying within 5% at least 92.7% of the time.