19K11428:Anthropomorphic robot hand

—Prosthetic hands are artificial extensions that help people who have lost their hands or arms to regain normal activity. One of the main requirements is that it should be as close as possible to the natural hand. Human hand flexibility is largely due to our highly evolved hand structure. To achieve those structures, we have adopted a human-like design concept. We designed the prosthetic hand based on the salient features of the human anatomy, making it have the same structural features as human hands: artificial joints, ligaments and Extensor hood, that should be very similar to the real hands. We tested to control prosthetic hand based on detected coordinates by Leap Motion. Finally, we could succeed in artificial hands to achieve same flexibility as characteristics of the human hand.

19K11428: 解剖学に基づいた電動義手




Sensors & Materials 2019

Quadcopter Robot Control Based on Hybrid Brain–Computer Interface System [PDF]

Chao Chen, Peng Zhou, Abdelkader Nasreddine Belkacem, Lin Lu, Rui Xu, Xiaotian Wang, Wenjun Tan, Zhifeng Qiao, Penghai Li, Qiang Gao, and Duk Shin

(Received July 12, 2019; Accepted November 5, 2019)

Keywords: hybrid brain computer interface (hBCI), common spatial pattern (CSP), hierarchical support vector machine (hSVM)

A hybrid brain–computer interface (hBCI) has recently been proposed to address the limitations of existing single-modal brain computer interfaces (BCIs) in terms of accuracy and information transfer rate (ITR) by combining more than one modality. The hBCI system also showed promising prospects for patients because the design of a human-centered smart robot control system may allow the performance of multiple tasks with high efficiency. In this paper, we present a hybrid multicontrol system that simultaneously uses electroencephalography (EEG) and electrooculography (EOG) signals. After the preprocessing phase, we used a common spatial pattern (CSP) algorithm to extract EEG and EOG features from motor imagery and eye movements. Moreover, a support vector machine (SVM) was used to solve a multiclass problem and complete flight operations through the asynchronous hBCI control of a four-axis quadcopter (e.g., takeoff, forward, backward, rightward, leftward, and landing). Online decoding of experimental results showed that 97.14, 95.23, 98.09, and 96.66% average accuracies, and 45.80, 43.99, 46.78, and 45.34 bits/min average ITRs were achieved in the control of a quadcopter. These online experimental results showed that the proposed hybrid system might be better in terms of completing multidirection control tasks to increase the multitasking and dimensionality of a BCI.

Corresponding author: Qiang Gao and Duk Shin


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