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LIBRA 神经外骨骼:为发展中地区提供负担得起的假肢的混合实时控制和机电一体化设计。

The LIBRA NeuroLimb: Hybrid Real-Time Control and Mechatronic Design for Affordable Prosthetics in Developing Regions.

机构信息

Biomechanics and Applied Robotics Research Laboratory, Pontificia Universidad Católica del Perú, Lima 15088, Peru.

出版信息

Sensors (Basel). 2023 Dec 22;24(1):70. doi: 10.3390/s24010070.

Abstract

Globally, 2.5% of upper limb amputations are transhumeral, and both mechanical and electronic prosthetics are being developed for individuals with this condition. Mechanics often require compensatory movements that can lead to awkward gestures. Electronic types are mainly controlled by superficial electromyography (sEMG). However, in proximal amputations, the residual limb is utilized less frequently in daily activities. Muscle shortening increases with time and results in weakened sEMG readings. Therefore, sEMG-controlled models exhibit a low success rate in executing gestures. The LIBRA NeuroLimb prosthesis is introduced to address this problem. It features three active and four passive degrees of freedom (DOF), offers up to 8 h of operation, and employs a hybrid control system that combines sEMG and electroencephalography (EEG) signal classification. The sEMG and EEG classification models achieve up to 99% and 76% accuracy, respectively, enabling precise real-time control. The prosthesis can perform a grip within as little as 0.3 s, exerting up to 21.26 N of pinch force. Training and validation sessions were conducted with two volunteers. Assessed with the "AM-ULA" test, scores of 222 and 144 demonstrated the prosthesis's potential to improve the user's ability to perform daily activities. Future work will prioritize enhancing the mechanical strength, increasing active DOF, and refining real-world usability.

摘要

全球范围内,上肢截肢中有 2.5%是肱骨截肢,针对这种情况,机械和电子假肢都在不断发展。机械假肢通常需要补偿运动,这可能导致姿势不自然。电子假肢主要由表面肌电图 (sEMG) 控制。然而,在近端截肢中,残肢在日常活动中的使用频率较低。随着时间的推移,肌肉缩短会导致 sEMG 读数减弱。因此,sEMG 控制模型在执行手势方面成功率较低。LIBRA NeuroLimb 假肢就是为了解决这个问题而引入的。它具有三个主动自由度和四个被动自由度 (DOF),可提供长达 8 小时的运行时间,并采用混合控制系统,结合 sEMG 和脑电图 (EEG) 信号分类。sEMG 和 EEG 分类模型的准确率分别高达 99%和 76%,能够实现精确的实时控制。假肢可以在 0.3 秒内完成抓握动作,施加的捏力高达 21.26N。两名志愿者进行了训练和验证会话。通过“AM-ULA”测试评估,得分分别为 222 和 144,这表明假肢有潜力提高用户执行日常活动的能力。未来的工作将优先提高机械强度、增加主动自由度,并改进实际可用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f37c/10780857/cd2464af5a29/sensors-24-00070-g001.jpg

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