case_images: PMC9114778_01
case_images: Classifier training was performed with all distal components attached during the load A condition and with a weight equal to the participant's clinically-prescribed prosthetic hand during the load B condition.
case_images: fbioe-10-876836-g001.jpg
case_images: PMC9114778_01_fbioe-10-876836-g001.jpg
case_images: F1
case_images: TASKA Hand (Figure 1). The participant reported that the socket alone was well-fitted over the limb, but when the components were attached to the distal end of the socket (i.e., the TASKA Hand, wrist rotator, batteries, and on/off switch), the fit noticeably deteriorated. A low-profile, high-frequency, linear 16HL7 ultrasound transducer weighing 11 g was mounted on the socket via a custom 3D printed bracket (Figure 1). We therefore conducted classifier training using two separate loading conditions (Figure 1).
case_images: We imposed discrete arm positions and movement patterns during classifier training (shown here relative to the left arm). During the static condition, the participant held her hand for 5 seconds within seven different positions designed to cover a majority of the reachable workspace. During the dynamic condition, the participant moved her hand for 5 seconds between these positions in four different movement patterns. We also considered a continuous dynamic condition, in which the participant moved her hand throughout all positions during a single 20-sec movement pattern. The names of each position and pattern were based on a functional activity for that arm configuration. Corresponding positions and movement patterns were also established relative to the right arm (i.e., flipped horizontally).
case_images: fbioe-10-876836-g002.jpg
case_images: PMC9114778_01_fbioe-10-876836-g002.jpg
case_images: F2
case_images: We considered three classifier training strategies to account for changes to arm position during real-world prosthesis use (Figure 2). The Rainbow Test was developed for this study to evaluate grasp control over a wider variety of arm positions than is required by the BBT or tBBT (Figure 4, Supplementary Figure S2).
case_images: The TASKA hand was configured to include tripod grasp, index finger point, and rest. Each grasp was initiated in response to a different muscle activation pattern, including wrist flexion for tripod, wrist extension for point, and a relaxed muscle state for rest. Representative ultrasound images are included for each muscle activation pattern.
case_images: fbioe-10-876836-g003.jpg
case_images: PMC9114778_01_fbioe-10-876836-g003.jpg
case_images: F3
case_images: We configured the TASKA hand to perform the three grasps executed in classifier training, including tripod, point, and rest (Figure 3). We defined the rest grasp such that the thumb, index, and middle fingers of the TASKA hand were partially extended and the ring and little fingers were in a closed position (Figure 3). We manually identified the start of each movement based on the acceleration signals (Supplementary Figure S3), and these time differences were averaged over 5 cycles.
case_images: The three functional tasks included the Box and Blocks Test, the Targeted Box and Blocks Test, and the Rainbow Test.
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case_images: PMC9114778_01_fbioe-10-876836-g004.jpg
case_images: F4
case_images: The set-up consists of two 10-inch square compartments separated by a six-inch-tall partition (Figure 4). Test (tBBT) is a modified version of the BBT involving only 16 blocks. The blocks are placed in a four-by-four grid in the compartment on the side of the arm being tested (Figure 4). The blocks are numbered 1 to 16, beginning with the innermost block on the bottom row and moving across each row. Note that we made a minor modification to the tBBT apparatus by removing both compartments' outer walls (Figure 4). The Rainbow Test was developed for this study to evaluate grasp control over a wider variety of arm positions than is required by the BBT or tBBT (Figure 4, Supplementary Figure S2).