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Dexterous hand movement is possible due to closed loop control dependent on efferent motor output and afferent sensory feedback. This control strategy is significantly altered in those with upper limb amputation as sensations of touch and movement are inherently lost. For upper limb prosthetic users, the absence of sensory feedback impedes efficient use of the prosthesis and is highlighted as a major factor contributing to user rejection of myoelectric prostheses. Numerous sensory feedback systems have been proposed in literature to address this gap in prosthetic control; however, these systems have yet to be implemented for long term use. Methodologies for communicating prosthetic grasp and touch information are reviewed, including discussion of selected designs and test results. With a focus on clinical and translational challenges, this review highlights and compares techniques employed to provide amputees with sensory feedback. Additionally, promising future directions are discussed and highlighted.

Resistance training is used to develop muscular strength and hypertrophy. Large muscle forces, in relation to the muscle's maximum force-generating ability, are required to elicit these adaptations. Previous biomechanical analyses of multi-joint resistance exercises provide estimates of muscle force but not relative muscular effort (RME). The purpose of this investigation was to determine the RME during the squat exercise. Specifically, the effects of barbell load and squat depth on hip extensor, knee extensor, and ankle plantar flexor RME were examined. Ten strength-trained women performed squats (50-90% 1 repetition maximum) in a motion analysis laboratory to determine hip extensor, knee extensor, and ankle plantar flexor net joint moment (NJM). Maximum isometric strength in relation to joint angle for these muscle groups was also determined. Relative muscular effect was determined as the ratio of NJM to maximum voluntary torque matched for joint angle. Barbell load and squat depth had significant interaction effects on hip extensor, knee extensor, and ankle plantar flexor RME (p < 0.05). Knee extensor RME increased with greater squat depth but not barbell load, whereas the opposite was found for the ankle plantar flexors. Both greater squat depth and barbell load increased hip extensor RME. These data suggest that training for the knee extensors can be performed with low relative intensities but require a deep squat depth. Heavier barbell loads are required to train the hip extensors and ankle plantar flexors. In designing resistance training programs with multi-joint exercises, how external factors influence RME of different muscle groups should be considered to meet training objectives.

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As a contribution toward the goal of adaptable, intelligent artificial limbs, this work introduces a continuous actor-critic reinforcement learning method for optimizing the control of multi-function myoelectric devices. Using a simulated upper-arm robotic prosthesis, we demonstrate how it is possible to derive successful limb controllers from myoelectric data using only a sparse human-delivered training signal, without requiring detailed knowledge about the task domain. This reinforcement-based machine learning framework is well suited for use by both patients and clinical staff, and may be easily adapted to different application domains and the needs of individual amputees. To our knowledge, this is the first my-oelectric control approach that facilitates the online learning of new amputee-specific motions based only on a one-dimensional (scalar) feedback signal provided by the user of the prosthesis.