Background: The limb position effect is a multi-faceted problem, associated with decreased upper-limb prosthesis control acuity following a change in arm position. The many factors contributing to this problem can arise from distinct environmental or physiological sources. Despite their differences in origin, the effect of each factor manifests similarly as increased variability in the detected control signal. This variability can cause incorrect decoding of user intent, leading to dropped items or inability to use the prosthesis during activities of daily living. The general approach of previous research has attempted to limit the impact of the factors or better capture the variability with data abundance. In this paper we take an alternative approach and investigate the effect of reducing the variability of the control signal by improving the consistency of muscle activity with user training. Methods: Participants underwent 4 days of myoelectric training with either concurrent or delayed feedback in a single arm position. During this time, they were trained to control a two-dimensional cursor using muscles in the forearm. At the end of training participants underwent a zero feedback retention test in multiple limb positions. In doing so, we tested how well the skill learned in a single limb position generalized to untrained positions. Results: We found that delayed feedback training led to more consistent muscle activity across both the trained and untrained limb positions. Analysis of patterns of activation in the delayed feedback group suggest a structured change in muscle activity occurs across arm positions. The structured changes allowed us to quantify the limb position effect by comparing trained to untrained arm positions. Different limb positions changed mean ECR and FCR muscle activity in the range of -4.3% to +18.7%. All participants were able to counter the limb position effect if given concurrent feedback, confirming our results align with existing findings. Conclusions: Our results demonstrate that myoelectric user-training can lead to the retention of motor skills that are more robust to limb position changes. This work highlights the importance of reducing motor variability with practice, prior to examining the underlying structure of muscle changes associated with limb position. These findings will be useful for the majority of myoelectric prosthesis control systems and will create better quality input data leading to more robust machine-learning based prosthesis control systems.

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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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