A Compact Control System and A Myoelectric Control Method for Multi-DOFs Prosthetic Hand

Yang, Bin; Jiang, Li; Hu, Jianhao; Chongyang, Li; Liu, Hong

doi:10.1109/rcar47638.2019.9044103

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Although HIT V prosthesis was designed for offline use through an onboard embedded system and battery [44], for the purposes of the experiments presented here and facilitated data collection, the prosthesis was tethered to an external benchtop DC power supply and a laptop which performed grasp pattern prediction and data acquisition. We developed a customized prosthesis controller for acquiring the in-hand sensor signals, controlling six actuators, and communicating with the PC, EMG controller, and proximity sensors controller.…”

Section: G Experimental Apparatusmentioning

confidence: 99%

Fingertip Proximity-Based Grasping Pattern Prediction of Transradial Myoelectric Prosthesis

Yang

Shi

Liu

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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For transradial amputees, especially those with insufficient residual muscle activity, it is challenging to quickly obtain an appropriate grasping pattern for a multigrasp prosthesis. To address this problem, this study proposed a fingertip proximity sensor and a grasping pattern prediction method base on it. Rather than exclusively utilizing the EMG of the subject for the grasping pattern recognition, the proposed method used fingertip proximity sensing to predict the appropriate grasping pattern automatically. We established a five-fingertip proximity training dataset for five common classes of grasping patterns (spherical grip, cylindrical grip, tripod pinch, lateral pinch, and hook). A neural network-based classifier was proposed and got a high accuracy (96%) within the training dataset. We assessed the combined EMG/proximity-based method (PS-EMG) on six able-bodied subjects and one transradial amputee subject while performing the "reach-and-pick up" tasks for novel objects. The assessments compared the performance of this method with the typical pure EMG methods. Results indicated that able-bodied subjects could reach the object and initiate prosthesis grasping with the desired grasping pattern on average within 1.93 s and complete the tasks 7.30% faster on average with the PS-EMG method, relative to the pattern recognition-based EMG method. And the amputee subject was, on average, 25.58% faster in completing tasks with the proposed PS-EMG method relative to the switch-based EMG method. The results showed that the proposed method allowed the user to obtain the desired grasping pattern quickly and reduced the requirement for EMG sources.

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Section: G Experimental Apparatusmentioning

confidence: 99%