Classification of hand grasp using perimeter change of the forearm

2012 ◽  
Author(s):  
Hwiyong Choi ◽  
Sangyoon Lee
Keyword(s):  
Hand Grasp

scholarly journals Classification of right-hand grasp movement based on EMOTIV Epoc+

2017 ◽  
Author(s):  
T. A. M. L. Tobing ◽  
Prawito ◽  
S. K. Wijaya
Keyword(s):  
Right Hand ◽  
Hand Grasp

scholarly journals Classification of Electroencephalogram Data from Hand Grasp and Release Movements for BCI Controlled Prosthesis

2016 ◽  
Vol 26 ◽  
pp. 374-381 ◽  
Author(s):  
Gerrit Lange ◽  
Cheng Yee Low ◽  
Khairunnisa Johar ◽  
Fazah Akthar Hanapiah ◽  
Fadhlan Kamaruzaman
Keyword(s):  
Hand Grasp

HAND GRASP FUNCTION ANALYSIS BASED ON VF SET

2013 ◽  
Vol 10 (03) ◽  
pp. 1350026 ◽  
Author(s):  
QIANG ZHAN ◽  
XIANCAN LIU
Keyword(s):  
Function Analysis ◽  
Degree Of Freedom ◽  
Human Hand ◽  
Virtual Finger ◽  
Detailed Classification ◽  
Hand Grasp ◽  
Motion Types

This paper presents a method based on virtual finger (VF) set to analyze the grasp function of bionic hand. In order to exactly represent different function units of human hand or bionic hand a more detailed classification of VF is proposed to divide VF into three types: VFFE, VFAA and VFPalm, of which a VF set describing hand grasp can be composed. The VF set cannot only describe the degree of freedom (DOF) distribution of human hand or bionic hand but also indicate which fingers take part in grasp and what motion types the fingers are. The VF sets of eight classic bionic hands and five basic human hand grasp postures are provided. The inclusion relationship between the VF set of a bionic hand and those of the basic human hand grasp postures is used to analyze the grasp function of a bionic hand. As examples the grasp functions of several classic bionic hands, such as i-LIMB Hand, HIT/DLR Hand, etc. are analyzed.

Analysis of Human-Machine Design of Hand-Grasp Apparatus

2013 ◽  
Vol 823 ◽  
pp. 96-99
Author(s):  
Jiu Mei Zhang
Keyword(s):  
Bone Structure ◽  
Point Of View ◽  
Machine Design ◽  
Hand Tools ◽  
Related Factors ◽  
Hand Grasp ◽  
Object Of Study ◽  
Design Ideas ◽  
Design Factors

Hand-held tools for the main object of study, start with the classification of the analysis of hand tools, specifically talking about the hand-held tools, human-machine design factors, including the two major aspects:1.Starting from the perspective of the"human" and point out that the hand structure of the human body, including muscle and bone structure is hand tools most closely the human-machine design factors. 2.From the "Tools" point of view, pointed out that product-related factors include the size and shape is the most important human-machine design factors of hand-held tool. Finally, hand tools must be designed to transfer to the center of the design and products of common concern, human-centered design will bring new design ideas and methods to hand-held tool designed. So ergonomics in the design of handheld products have broad application prospects.

EXTRACTION OF NEURAL CONTROL COMMANDS USING MYOELECTRIC PATTERN RECOGNITION: A NOVEL APPLICATION IN ADULTS WITH CEREBRAL PALSY

2014 ◽  
Vol 24 (07) ◽  
pp. 1450022 ◽  
Author(s):  
JIE LIU ◽  
XIAOYAN LI ◽  
CHRISTINA MARCINIAK ◽  
WILLIAM ZEV RYMER ◽  
PING ZHOU
Keyword(s):  
Pattern Recognition ◽  
Cerebral Palsy ◽  
Classification Accuracy ◽  
Neural Control ◽  
Surface Emg ◽  
Hand Rehabilitation ◽  
Control Information ◽  
Hand Grasp ◽  
Movement Intention

This study investigates an electromyogram (EMG)-based neural interface toward hand rehabilitation for patients with cerebral palsy (CP). Forty-eight channels of surface EMG signals were recorded from the forearm of eight adult subjects with CP, while they tried to perform six different hand grasp patterns. A series of myoelectric pattern recognition analyses were performed to identify the movement intention of each subject with different EMG feature sets and classifiers. Our results indicate that across all subjects high accuracies (average overall classification accuracy > 98%) can be achieved in classification of six different hand movements, suggesting that there is substantial motor control information contained in paretic muscles of the CP subjects. Furthermore, with a feature selection analysis, it was found that a small number of ranked EMG features can maintain high classification accuracies comparable to those obtained using all the EMG features (average overall classification accuracy > 96% with 16 selected EMG features). The findings of the study suggest that myoelectric pattern recognition may be a useful control strategy for promoting hand rehabilitation in CP patients.

Note on the spectral classification of carbon stars in the photographic infrared region

1966 ◽  
Vol 24 ◽  
pp. 21-23
Author(s):  
Y. Fujita
Keyword(s):  
Infrared Region ◽  
Spectral Classification ◽  
Carbon Stars

We have investigated the spectrograms (dispersion: 8Å/mm) in the photographic infrared region fromλ7500 toλ9000 of some carbon stars obtained by the coudé spectrograph of the 74-inch reflector attached to the Okayama Astrophysical Observatory. The names of the stars investigated are listed in Table 1.

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