Residual ankle muscles (i.e., previously antagonistic ankle muscles) of transtibial amputees are a potential source for continuous feedforward control of powered ankle prostheses using proportional myoelectric control. The ability for transtibial amputees to use their residual ankle muscles for two control input degrees of freedom (i.e., two independent myoelectric control input sources) for direct neural control depends on the ability for amputees to generate varying magnitudes of reciprocal activation and coactivation using their residual ankle muscles, which is not well understood. In this paper, we aimed to fill this knowledge gap. We asked 12 transtibial amputees to control the 2-D movement of a computer cursor using continuous proportional myoelectric control via their residual plantar flexor and residual dorsiflexor muscles to define their reachable 2-D control input space. The x-y position of the computer cursor was directly proportional to the independent continuous myoelectric control signals from the residual lateral gastrocnemius (x-axis) and the residual tibialis anterior (y-axis) where the limits of each axis were 0%-100% maximum voluntary activation of the corresponding residual muscle. Our results show that the reachable control input space varied widely across amputee subjects ranging from 38% to 81% of the maximum possible control input space. The cumulative time for the amputee subjects to saturate their reachable control input space ranged from 1.95 to 6.85 min. The amputee subjects used different residual muscle activation patterns and coordination strategies to expand their reachable control input space depending on their ability to perform coactivation and reciprocal activation using their residual plantar flexor and dorsiflexor muscles. The future development of powered lower limb prostheses using direct continuous proportional myoelectric control via residual muscles (e.g., for direct voluntary control of prosthesis joint impedance) should consider how an amputee user's immediately accessible residual muscle activation patterns and reachable 2-D control input space may affect their learning and performance.
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