Intuitive Neuromyoelectric Control of a Dexterous Bionic Arm Using a Modified Kalman Filter
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Multi-articulate prosthetic hands are capable of performing dexterous hand movements. However, clinically available control strategies fail to provide users with intuitive, independent and proportional control over multiple degrees of freedom (DOFs) in real time. Here, we detail the use of a modified Kalman filter to provide intuitive, independent and proportional real-time control over six-DOF prosthetic hands such as the DEKA LUKE Arm. Input features include neural firing rates recorded from Utah Slanted Electrode Arrays and mean absolute value of intramuscular electromyographic (EMG) recordings. Ad-hoc modifications include thresholds and non-unity gains on the output of the Kalman filter. We demonstrate that both neural and EMG data can be combined effectively to improve control. We also highlight that thresholds significantly reduce unintended movements and promote more independent control of the different DOFs. Optimal threshold values can be determined quickly offline and translate to functional improvements online. In contrast to traditional pattern recognition control schemes, the modified Kalman filter allows users to continuously modulate their force output, which is critical for fine dexterity. In less than five minutes of training, amputees were able to successfully control in real time a multi-articulate prosthetic arm (hand and wrist) to perform various activities of daily living. Preliminary implementation of the modified Kalman filter onto a portable, take-home system further demonstrates feasibility of the algorithm as a way to explore the functional and psychological benefits associated with long-term, at-home control of dexterous prosthetic hands.
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