上肢康复机器人本体末端运动范围数据

研究内容：基于数据驱动强化学习方法的智能认知评估与康复系统：首先，开发脑电、近红外、步态特征、智能传感等多信息融合的可穿戴认知评估设备，以及开发康复机器人、眼动/平衡训练、步态训练设备，并结合力触觉反馈、虚拟现实等手段，实现多模态认知评估、多任务康复训练技术手段的物理集成；更重要的，针对不同患者、家庭/社区/医院不同场景，通过线上线下多源异构数据分析融合，建立个体认知评估模型，通过强化学习，在整个监测和介入时间进行模型参数的迭代优化，并提高泛化和预测能力，对康复训练手段和处方进行精准推荐和动态调整，最终形成自动化评估与康复训练的闭环系统。 数据内容：上肢康复机器人本体末端运动范围数据；数据采集地点：北京市；数据采集时间：2022.10；设备名称：上肢康复机器人；运行环境：无；数据类型：文本；预估数据量/记录数：1条；数据格式：TXTResearch Content: Intelligent Cognitive Assessment and Rehabilitation System Based on Data-driven Reinforcement Learning Method: Firstly, develop multi-modal wearable cognitive assessment devices integrating electroencephalogram (EEG), near-infrared spectroscopy (NIRS), gait features, intelligent sensing and other information, as well as rehabilitation robots, eye movement/balance training, and gait training equipment. Combined with haptic feedback, virtual reality and other means, realize the physical integration of multi-modal cognitive assessment and multi-task rehabilitation training technologies. More importantly, for different patients and various scenarios including families, communities and hospitals, establish individual cognitive assessment models via online and offline multi-source heterogeneous data analysis and fusion. Through reinforcement learning, iteratively optimize model parameters throughout the entire monitoring and intervention period, enhance generalization and prediction capabilities, accurately recommend and dynamically adjust rehabilitation training measures and prescriptions, and finally form a closed-loop system for automated assessment and rehabilitation training. Data Content: Range of motion data of the end effector of the upper limb rehabilitation robot; Data collection location: Beijing; Data collection time: October 2022; Equipment name: Upper Limb Rehabilitation Robot; Operating environment: None; Data type: Text; Estimated data volume/number of records: 1 record; Data format: TXT

搜集汇总

背景与挑战

背景概述

该数据集包含上肢康复机器人本体末端的运动范围数据，采集于2022年10月的北京市，采用TXT格式存储。数据来源于上肢康复机器人设备，属于文本类型，用于支持认知康复相关研究。