AxoPy: A Python Library for Implementing Human-Computer Interface Experiments

Lyons, Kenneth R.; Margolis, Benjamin W. L.

doi:10.21105/joss.01191

Cited by 12 publications

(8 citation statements)

References 4 publications

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“…The experiment was implemented in Python 3 and all sessions were run on a standard laptop computer (16 GB, i7 @ 2.70 GHz). Data collection, processing, and the real-time myoelectric control interface were implemented using the axopy library [25] and customwritten code. Model training and inference were performed using the scikit-learn library [26].…”

Section: Methodsmentioning

confidence: 99%

Discrete action control for prosthetic digits

Krasoulis

Nazarpour

2020

Preprint

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Objective We aim to develop a paradigm for simultaneous and independent control of multiple degrees of freedom (DOFs) for upper-limb prostheses. Approach We introduce action control, a novel method to operate prosthetic digits with surface electromyography (EMG) based on multi-label, multi-class classification. At each time step, the decoder classifies movement intent for each controllable DOF into one of three categories: open, close, or stall (i.e., no movement). We implemented a real-time myoelectric control system using this method and evaluated it by running experiments with one unilateral and two bilateral amputees. Participants controlled a six-DOF bar interface on a computer display, with each DOF corresponding to a motor function available in multi-articulated prostheses. Main results We show that action control can significantly and systematically outperform the state-of-the-art method of position control via multi-output regression in both task-and non-task-related measures. Improvements in median task performance ranged from 20.14% to 62.32% for individual participants. Analysis of a post-experimental survey revealed that all participants rated action higher than position control in a series of qualitative questions and expressed an overall preference for the former. Significance Action control has the potential to improve the dexterity of upper-limb prostheses. In comparison with regression-based systems, it only requires discrete instead of real-valued ground truth labels, typically collected with motion tracking systems. This feature makes the system both practical in a clinical setting and also suitable for bilateral amputation. This work is the first demonstration of myoelectric digit control in bilateral upper-limb amputees. Further investigation and pre-clinical evaluation are required to assess the translational potential of the method.Discrete action control for prosthetic digits

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Section: Methodsmentioning

confidence: 99%

Discrete action control for prosthetic digits

Krasoulis

Nazarpour

2020

Preprint

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“…The experiment was performed on a laptop computer (2.3 GHz i5-6200U CPU, 8 GB RAM, Lenovo, China). Real-time experimental software was implemented in Python using the AxoPy library [46].…”

Section: Myoelectric Control Interface (Mci) Taskmentioning

confidence: 99%

Delaying feedback during pre-device training facilitates the retention of novel myoelectric skills: a laboratory and home-based study

et al. 2023

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Objective. The objective of this study was to assess the impact of delayed feedback training on the retention of novel myoelectric skills, and to demonstrate the use of this training approach in the home environment. Approach. We trained limb-intact participants to use a motor learning-based upper-limb prosthesis control scheme called abstract decoding. A delayed feedback paradigm intended to prevent within-trial adaptation and to facilitate motor learning was used. We conducted two multi-day experiments. Experiment 1 was a laboratory-based study consisting of two groups trained over a four-day period with concurrent or delayed feedback. An additional follow-up session took place after 18 days to assess the retention of motor skills. Experiment 2 was a home-based pilot study that took place over five consecutive days to investigate delayed feedback performance when using bespoke training structures. Main Results. Approximately 35,000 trials were collected across both experiments. Experiment 1 found that the retention of motor skills for the delayed feedback group was significantly better than that of their concurrent feedback counterparts. In addition, the delayed feedback group improved their retention of motor skills across days, whereas the concurrent feedback group did not. Experiment 2 demonstrated that by using a bespoke training protocol in an environment that is more conducive to learning, it is possible for participants to become highly accurate in the absence of feedback. Significance. These results show that with delayed feedback training, it is possible to retain novel myoelectric skills. Using abstract decoding participants can activate four distinct muscle patterns without using complex algorithms. The accuracy achieved in the pilot study supports the feasibility of motor learning-based upper-limb prosthesis control after home-based myoelectric training.

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“…The experiment was implemented in Python (v3.7) and all sessions were run on a standard laptop computer (16 GB, i7 2.70 GHz). Data collection, processing, and the real-time myoelectric control interface were implemented using the axopy library (v0.2.3) [30] and custom-written code. Model training and inference were performed using the scikit-learn library (v0.22) [31].…”

Section: J Implementationmentioning

confidence: 99%

Discrete Action Control for Prosthetic Digits

Krasoulis

Nazarpour

2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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We aim to develop a paradigm for simultaneous and independent control of multiple degrees of freedom (DOFs) for upper-limb prostheses. To that end, we introduce action control, a novel method to operate prosthetic digits with surface electromyography (EMG) based on multi-output, multi-class classification. At each time step, the decoder classifies movement intent for each controllable DOF into one of three categories: open, close, or stall (i.e., no movement). We implemented a real-time myoelectric control system using this method and evaluated it by running experiments with one unilateral and two bilateral amputees. Participants controlled a six-DOF bar interface on a computer display, with each DOF corresponding to a motor function available in multi-articulated prostheses. We show that action control can significantly and systematically outperform the state-of-the-art method of position control via multi-output regression in both taskand non-task-related measures. Using the action control paradigm, improvements in median task performance over regression-based control ranged from 20.14% to 62.32% for individual participants. Analysis of a post-experimental survey revealed that all participants rated action higher than position control in a series of qualitative questions and expressed an overall preference for the former. Action control has the potential to improve the dexterity of upper-limb prostheses. In comparison with regression-based systems, it only requires discrete instead of real-valued ground truth labels, typically collected with motion tracking systems. This feature makes the system both practical in a clinical setting and also suitable for bilateral amputation. This work is the first demonstration of myoelectric digit control in bilateral upper-limb amputees. Further investigation and preclinical evaluation are required to assess the translational potential of the method.

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AxoPy: A Python Library for Implementing Human-Computer Interface Experiments

Cited by 12 publications

References 4 publications

Discrete action control for prosthetic digits

Discrete action control for prosthetic digits

Delaying feedback during pre-device training facilitates the retention of novel myoelectric skills: a laboratory and home-based study

Discrete Action Control for Prosthetic Digits

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