There was no objective evidence that restoring movement representation by neurorehabilitation with virtual reality alleviated phantom limb pain. This study revealed quantitatively that restoring movement representation with virtual reality rehabilitation using a bimanual coordination task correlated with alleviation of phantom limb pain.

Amputees usually perceive vivid awareness of their lost body parts after the amputation (phantom limbs). Phantom limb pain (PLP) is intense pain that is felt in the phantom limb. The mechanism of PLP is still unclear, but the major hypothesis is that it is derived from dysfunction of the brain. There are a few neurorehabilitation techniques using a mirror or virtual reality (VR) that present the visual image of a phantom limb to the patients, and this produce the movement perception of their phantom limb. Here, we developed a multimodal (visual, auditory, and tactile) VR system to obtain the perception of voluntary phantom limb movements. We applied this system to five PLP patients for three tactile feedback conditions as a pilot study. In conclusion, four of the five patients reported pain amelioration, up to 86% decrease in the tactile feedback condition. In addition, our results demonstrated that the best suited condition of feedback-sense modalities depends on the patient. These results suggest that this system can be applied to a rehabilitation platform to offer flexible neurorehabilitation regimens for each patient.

This paper aims to provide a specific example of how OpenAI's ChatGPT can be used in a few-shot setting to convert natural language instructions into a sequence of executable robot actions (Fig. 1). Generating programs for robots from natural language instructions is an attractive goal, but the practical application using ChatGPT is still in its early stages, and there is no established methodology yet. Here, we have designed easy-to-customize input prompts for ChatGPT that meet common requirements in many practical applications, including: 1) easy integration with robot execution systems or visual recognition programs, 2) applicability to various environments, and 3) the ability to provide long-step instructions while minimizing the impact of ChatGPT's token limit. Specifically, the prompts encourage ChatGPT to 1) output a sequence of predefined robot actions with explanations in a readable JSON format, 2) represent the operating environment in a formalized style, and 3) infer and output the updated state of the operating environment as the result of each operation, which will be input with the next instruction to allow ChatGPT to work based solely on the memory of the latest operations. Through experiments, we confirmed that the proposed prompts allow ChatGPT to act in accordance with the requirements in various environments. Additionally, we observed that ChatGPT's conversational ability allows users to adjust its output with natural language feedback, which is crucial for developing an application that is both safe and robust while providing a user-friendly interface. Users can easily customize the prompts as templates. The contribution of this paper is to provide and publish the prompts, which are generic enough to be easily modified to fit the requirements of each experimenter, thereby providing practical knowledge to the robotics research community. Our prompts and source code for using them are open-source and publicly available at https://github.com/microsoft/ChatGPT-Robot-Manipulation-Prompts. Fig. 1. This paper shows practical prompts for ChatGPT to generate for translating a sequences of executable robot actions from multi-step human instructions in various environments.

BackgroundPrevious studies have tried to relieve deafferentation pain (DP) by using virtual reality rehabilitation systems. However, the effectiveness of multimodal sensory feedback was not validated. The objective of this study is to relieve DP by neurorehabilitation using a virtual reality system with multimodal sensory feedback and to validate the efficacy of tactile feedback on immediate pain reduction.MethodsWe have developed a virtual reality rehabilitation system with multimodal sensory feedback and applied it to seven patients with DP caused by brachial plexus avulsion or arm amputation. The patients executed a reaching task using the virtual phantom limb manipulated by their real intact limb. The reaching task was conducted under two conditions: one with tactile feedback on the intact hand and one without. The pain intensity was evaluated through a questionnaire.ResultsWe found that the task with the tactile feedback reduced DP more (41.8 ± 19.8 %) than the task without the tactile feedback (28.2 ± 29.5 %), which was supported by a Wilcoxon signed-rank test result (p < 0.05).ConclusionsOverall, our findings indicate that the tactile feedback improves the immediate pain intensity through rehabilitation using our virtual reality system.Electronic supplementary materialThe online version of this article (doi:10.1186/s12984-016-0161-6) contains supplementary material, which is available to authorized users.