Anthony Anderson

and 5 more

Introduction: People with transtibial limb loss frequently experience suboptimal gait outcomes. This is partly attributable to the absence of the biarticular gastrocnemius muscle, which plays a unique role in walking. Although a recent surge of biarticular prostheses aims to restore gastrocnemius function, the broad design space and lack of consensus on optimal hardware and control strategies present scientific and engineering challenges. Methods: This study introduces a robotic biarticular prosthesis emulator, comprising a uniarticular ankle-foot prosthesis and knee flexion exoskeleton, each actuated by a custom off-board system. Benchtop experiments were conducted to characterize the emulatorâ\euro™s mechatronic performance. Walking experiments with one transtibial amputee demonstrated the systemâ\euro™s capability for providing knee and ankle assistance. Results: The -3 dB bandwidths for the knee exoskeletonâ\euro™s torque and motor velocity controllers were measured at approximately 5 Hz and 100 Hz, respectively. A feedforward iterative learning controller reduced the root-mean-squared torque tracking error from 6.04 Nm to 0.99 Nm in hardware-in-the-loop experiments, an 84% improvement. User-preference-based tuning yielded a peak knee torque of approximately 20% of the estimated biological knee moment. Conclusions: This biarticular prosthesis emulator demonstrates significant potential as a versatile research platform that can offer valuable insights for the advancement of lower-limb assistive devices.

Anthony Anderson

and 3 more

People with unilateral transtibial amputation generally exhibit asymmetric gait, likely due to inadequate prosthetic ankle function. This results in compensatory behavior, leading to long-term musculoskeletal impairments (e.g., osteoarthritis in the joints of the intact limb). Powered prostheses can better emulate biological ankles, however, control methods are over-reliant on able-bodied data, require extensive amounts of tuning by experts, and cannot adapt to each user’s unique gait patterns. This work directly addresses all these limitations with a personalized and data-driven control strategy. Our controller uses a virtual setpoint trajectory within an impedance-inspired formula to adjust the dynamics of the robotic ankle-foot prosthesis as a function of gait phase. A single sensor measuring thigh motion is used to estimate the gait phase in real time. The virtual setpoint trajectory is modified via a data-driven iterative learning strategy aimed at optimizing ankle kinematic symmetry. The controller was experimentally evaluated on two people with transtibial amputation. The control scheme successfully increased ankle angle symmetry about the two limbs by 25.3% when compared to the passive condition. In addition, the symmetry controller significantly increased peak prosthetic ankle power output at push-off by 0.52 W/kg and significantly reduced biomechanical risk factors associated with osteoarthritis in the intact limb. This research demonstrates the benefits of personalized and data-driven symmetry controllers for robotic ankle-foot prostheses.