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  1. Following tetraplegia, independence for completing essential daily tasks, such as opening doors and eating, significantly declines. Assistive robotic manipulators (ARMs) could restore independence, but typically input devices for these manipulators require functional use of the hands. We created and validated a hands-free multimodal input system for controlling an ARM in virtual reality using combinations of a gyroscope, eye-tracking, and heterologous surface electromyography (sEMG). These input modalities are mapped to ARM functions based on the user’s preferences and to maximize the utility of their residual volitional capabilities following tetraplegia. The two participants in this study with tetraplegia preferred to use the control mapping with sEMG button functions and disliked winking commands. Non-disabled participants were more varied in their preferences and performance, further suggesting that customizability is an advantageous component of the control system. Replacing buttons from a traditional handheld controller with sEMG did not substantively reduce performance. The system provided adequate control to all participants to complete functional tasks in virtual reality such as opening door handles, turning stove dials, eating, and drinking, all of which enable independence and improved quality of life for these individuals. 
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    Free, publicly-accessible full text available June 3, 2025
  2. This paper unifies commonly used accelerated stochastic gradient methods (Polyak’s Heavy Ball, Nesterov’s Accelerated Gradient and Adaptive Moment Estimation (Adam)) as specific cases of a general lowpass regularized learning framework, the Automatic Stochastic Gradient Method (AutoSGM). For AutoSGM, we derive an optimal iteration-dependent learning rate function and realize an approximation. Adam is also an approximation of this optimal approach that replaces the iteration-dependent learning-rate with a constant. Empirical results on deep neural networks comparing the learning behavior of AutoSGM equipped with this iteration-dependent learning-rate algorithm demonstrate fast learning behavior, robustness to the initial choice of the learning rate, and can tune an initial constant learning-rate in applications where a good constant learning rate approximation is unknown. 
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    Free, publicly-accessible full text available April 14, 2025
  3. This paper presents a blind source separation algorithm to identify binary and sparse sources from convolutive mixtures with linear and time-invariant finite impulse responses. Our approach combines Bayesian algorithms for detecting source activity with a linear minimum mean-square error estimator to identify all the time samples when each source is active. The algorithm was implemented on simulated electromyo-grams to identify neural commands. Our algorithm identified more than 96% of the sources on average with 16 or more measurement channels and SNR >= 14dB. For the detected sources, this algorithm correctly identified more than 94% of the samples on average. This performance was significantly better than that of a competing algorithm available in the literature. 
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  4. Upper-limb amputees commonly cite difficulty of control as one of the main reasons why they abandon their prostheses. Combining myoelectric control with autonomous sensor-based control could improve prosthesis control. However, the cognitive and physical impact of shared control and semi-autonomous systems on users has yet to be fully explored. In this study we introduce a novel shared-control algorithm that blends proportional position control predicted from electromyography (EMG) with proportional position control predicted from an autonomous machine using infrared sensors embedded in the prosthetic hand’s fingers to detect the distance to objects. The user’s EMG control is damped in proportion to the machine’s prediction of an object’s position in relation to a given finger. The shared-control algorithm was validated using three intact individuals completing a holding task where they attempted to hold an object for as long as possible without dropping it. Shared control resulted in fewer object drops, 32% less cognitive demand, and 49% less physical effort (measured by EMG) relative to the participant’s EMG control alone. These results indicate that shared control can reduce the physiological burdens on the user as well as increase prosthetic control. 
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  8. Intuitive control of prostheses relies on training algorithms to correlate biological recordings to motor intent. The quality of the training dataset is critical to run-time performance, but it is difficult to label hand kinematics accurately after the hand has been amputated. We quantified the accuracy and precision of labeling hand kinematics for two different approaches: 1) assuming a participant is perfectly mimicking predetermined motions of a prosthesis (mimicked training), and 2) assuming a participant is perfectly mirroring their contralateral hand during identical bilateral movements (mirrored training). We compared these approaches in non-amputee individuals, using an infrared camera to track eight different joint angles of the hands in real-time. Aggregate data revealed that mimicked training does not account for biomechanical coupling or temporal changes in hand posture. Mirrored training was significantly more accurate and precise at labeling hand kinematics. However, when training a modified Kalman filter to estimate motor intent, the mimicked and mirrored training approaches were not significantly different. The results suggest that the mirrored training approach creates a more faithful but more complex dataset. Advanced algorithms, more capable of learning the complex mirrored training dataset, may yield better run-time prosthetic control. 
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