 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 recognitioncontrol 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.
Background: Multi-articulate prostheses 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. New Method: We detail the use of a modified Kalman filter (MKF) to provide intuitive, independent and proportional control over six-DOF prostheses 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 a Kalman filter. Results: We demonstrate that both neural and EMG data can be combined effectively . We also highlight that modifications can be optimized to significantly improve performance relative to an unmodified Kalman filter. Thresholds significantly reduced unintended movement and promoted more independent control of the different DOFs. Gain were significantly greater than one and served to amplify participant effort. Optimal modifications can be determined quickly offline and translate to functional improvements online. Using a portable take-home system, participants performed various activities of daily living. Comparison with Existing Methods: In contrast to pattern recognition, the MKF allows users to continuously modulate their force output, which is critical for fine dexterity. The MKF is also computationally efficient and can be trained in less than five minutes . Conclusions: The MKF can be used to explore the functional and psychological benefits associated with long-term, at-home control of dexterous prosthetic hands.
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Background:
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prosthetic hands
prostheses
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real time. Here, we
real-time. New Method: We
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(MKF)
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real-time
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prosthetic hands
prostheses
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LUKE
"LUKE"
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the
a
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Results:
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to improve control
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thresholds significantly reduce unintended movements and promote
modifications can be optimized to significantly improve performance relative to an unmodified Kalman filter. Thresholds significantly reduced unintended movement and promoted
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Optimal threshold values
Gain were significantly greater than one and served to amplify participant effort. Optimal modifications
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Using a portable take-home system, participants performed various activities of daily living. Comparison with Existing Methods:
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traditional pattern recognitioncontrol schemes, the modified Kalman filter
pattern recognition, the MKF
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In
The MKF is also computationally efficient and can be trained in
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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
. Conclusions: The MKF can be used