DOI: 10.5061/dryad.rv15dv4ks
title: Myoelectric prosthesis control using recurrent convolutional neural network regression mitigates the limb position effect

description: Many myoelectric upper limb prosthesis controllers use pattern
 recognition, a method that learns and recognizes patterns of
 electromyographic (EMG) signals produced by the user’s residual limb
 muscles to predict and execute device movements. Such control becomes
 unreliable in high limb positions—a problem known as the limb position
 effect. Pattern recognition often uses a classification algorithm; simple
 to implement, but limits user-initiated control to only one device
 movement at a time, at a single speed. To combat position-related control
 deficiencies and classification controller constraints, we developed and
 tested two recurrent convolutional neural network (RCNN) pattern
 recognition-based solutions: (1) an RCNN classification controller that
 uses EMG plus positional inertial measurement unit (IMU) signals to offer
 one-speed, sequential movement control; and (2) an RCNN regression
 controller that uses the same data capture technique to offer simultaneous
 control of multiple movements and device movement velocity. We assessed
 both RCNN controllers by comparing them to a commonly used linear
 discriminant analysis classification controller (LDA-Baseline).
 Participants without upper limb impairment were recruited to perform
 multipositional tasks while wearing a simulated prosthesis. Both RCNN
 classification and regression controllers showed improved functional task
 performance over LDA-Baseline, in 11 and 38 out of 115 metrics,
 respectively. This work contributes an RCNN regression-based controller
 that provides accurate, simultaneous, and proportional movements to
 EMG-based technologies including prostheses, exoskeletons, and even
 muscle-activated video games.
description: # Myoelectric Prosthesis Control using Recurrent Convolutional Neural
 Network Regression Mitigates the Limb Position Effect Dataset DOI:
 [10.5061/dryad.rv15dv4ks](10.5061/dryad.rv15dv4ks) ## Description of the
 data and file structure This dataset contains calculated metrics from 16
 non-disabled participants, each testing two myoelectric prosthesis control
 strategies: 1) either a recurrent convolutional neural network-based
 classification model (RCNN-Class) or a recurrent convolutional neural
 network-based regression model (RCNN-Reg), and 2) a linear discriminant
 analysis classification baseline (LDA-Baseline).  ### Files and variables
 #### File: RCNN\_vs\_LDA-Baseline.csv **Description:**  ##### Variables *
 ParticipantID: Randomly assigned 3-digit participant identification
 number. * ControlStrategy: Which control strategy was used for the given
 trial: linear discriminant analysis baseline classification (LDA-Baseline)
 or recurrent convolutional neural network classification with transfer
 learning (RCNN-TL). * ParticipantGroup: The group that the participant was
 assigned to, indicating what RCNN-based control strategy they would use:
 RCNN-TL. * Task: Which task was performed for the given trial: the Pasta
 Box Task (Pasta), the Refined Clothespin Relocation Test down trials
 (RCRT_down), or the Refined Clothespin Relocation Test up trials
 (RCRT_up). * TrialID: Trial identification number. * SuccessRate
 (%): Percent of trials that are error-free, in percent. * TrialDuration
 (s): Elapsed time for each trial, in seconds. * PhaseDuration (s): Elapsed
 time for each phase, in seconds. Phase Duration was calculated for each
 phase (Reach, Grasp, Transport, and Release) in each movement (Movement1,
 Movement2, and Movement3). * RelativePhaseDuration (%): Elapsed time for
 each phase, relative to the elapsed time for a
 Reach-Grasp-Transport-Release movement, in percent. Relative Phase
 Duration was calculated for each phase (Reach, Grasp, Transport, and
 Release) in each movement (Movement1, Movement2, and Movement3). *
 PeakHandVelocity (mm/s): Maximum velocity of the hand while moving, in
 millimeters per second. Peak Hand Velocity was calculated for each
 movement segment (ReachGrasp and TransportRelease) in each movement
 (Movement1, Movement2, and Movement3). * HandDistanceTravelled (mm): Total
 distance travelled by the hand while moving, in millimeters. Hand Distance
 Travelled was calculated for each movement segment (ReachGrasp and
 TransportRelease) in each movement (Movement1, Movement2, and Movement3).
 * HandTrajectoryVariability (mm): How much the hand movement path varies
 between trials, in millimeters. Hand Trajectory Variability was calculated
 for each movement segment (ReachGrasp and TransportRelease) in each
 movement (Movement1, Movement2, and Movement3). Hand Trajectory
 Variability was also only calculated once for each participant-control
 strategy-task combination. Therefore, only the last trial of such
 combination contains a value. * TotalGripApertureMovement (mm): Total
 amount of grip aperture variation, in millimeters. Total Grip Aperture
 Movement was calculated for each phase (Reach, Grasp, Transport, and
 Release) in each movement (Movement1, Movement2, and Movement3). * Number
 of Grip Aperture Adjustments: Number of times that grip aperture variation
 commences or changes direction. Number of Grip Aperture Adjustments was
 calculated for each phase (Reach, Grasp, Transport, and Release) in each
 movement (Movement1, Movement2, and Movement3).  * Number of Wrist
 Rotation Adjustments: Number of times that wrist rotation angle variation
 commences or changes direction. Number of Wrist Rotation Adjustments was
 calculated for each phase (Reach, Grasp, Transport, and Release) in each
 movement (Movement1, Movement2, and Movement3). * Grip Aperture Plateau
 (s): Amount of time during which the grip aperture remains open before
 closing to grasp a task object, in seconds. Grip Aperture Plateau was
 calculated for each ReachGrasp movement segment in each movement
 (Movement1, Movement2, and Movement3). * Simultaneous Wrist-Shoulder
 Movements (%): Percent of the phase during which the wrist rotation is
 controlled while the shoulder is moving, in percent. Simultaneous
 Wrist-Shoulder Movements was calculated for Reach and Transport phases in
 each movement (Movement1, Movement2, and Movement3).  * Total Muscle
 Activity: Total amount of muscle activity expended. Total Muscle Activity
 was calculated for each phase (Reach, Grasp, Transport, and Release) in
 each movement (Movement1, Movement2, and Movement3). * NASA-Task Load
 Index (NASA-TLX): Workload demand resulting from each controller. The
 NASA-TLX examined 6 dimensions: Mental Demand, Physical Demand, Temporal
 Demand, Performance, Effort, and Frustration. * Usability Survey:
 Usability of each controller. The Usability Survey examined 4 dimensions:
 Intuitiveness, Effectiveness in Pasta, Effectiveness in RCRT, and
 Reliability. ## Human subjects data All participants provided explicit
 written consent for their de-identified data to be shared in the public
 domain. Prior to data sharing, all personally identifiable information
 (PII) was removed to ensure participant anonymity. Electromyographic (EMG)
 and inertial measurement unit (IMU) data were anonymized by assigning
 randomized participant codes. The resulting dataset contains only
 non-identifiable signal data and task labels, ensuring compliance with
 ethical standards for human subject research and data sharing.