Real-time Hand Motion Reconstruction System for Trans-Humeral Amputees Using EEG and EMG

Electromyographic (EMG) processing is an important research area with direct applications to prosthetics, exoskeletons and human-machine interaction. Current state of the art decoding methods require intensive training on a single user before it can be utilized, and have been unable to achieve both user-independence and real-time performance. This paper presents a real-time EMG classification method which generalizes across users without requiring an additional training phase. An EMG-embedded sleeve quickly positions and records from EMG surface electrodes on six forearm muscles. An optimized decision tree classifies signals from these sensors into five distinct movements for any given user using EMG energy synergies between muscles. This method was tested on 10 healthy subjects using leave-one-out validation, resulting in an overall accuracy of 79±6.6%, with sensitivity and specificity averaging 66% and 97.6%, respectively, over all classified motions. The high specificity values demonstrate the ability to generalize across users, presenting opportunities for large-scale studies and broader accessibility to EMG-driven applications.

Download Full-text

Real-Time Gesture Recognition for Controlling a Virtual Hand

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.463-464.1147 ◽

2012 ◽

Vol 463-464 ◽

pp. 1147-1150 ◽

Cited By ~ 5

Author(s):

Constantin Catalin Moldovan ◽

Ionel Staretu

Keyword(s):

Virtual Environments ◽

Real Time ◽

Three Dimensional ◽

Video Stream ◽

Human Hand ◽

Hand Motion ◽

Virtual Hand ◽

Hand Control ◽

Hand Gesture Detection ◽

Detection And Recognition

Object tracking in three dimensional environments is an area of research that has attracted a lot of attention lately, for its potential regarding the interaction between man and machine. Hand gesture detection and recognition, in real time, from video stream, plays a significant role in the human-computer interaction and, on the current digital image processing applications, this represent a difficult task. This paper aims to present a new method for human hand control in virtual environments, by eliminating the need of an external device currently used for hand motion capture and digitization. A first step in this direction would be the detection of human hand, followed by the detection of gestures and their use to control a virtual hand in a virtual environment.

Download Full-text

Handwriting Recognition in Free Space Using WIMU-Based Hand Motion Analysis

Journal of Sensors ◽

10.1155/2016/3692876 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

Shashidhar Patil ◽

Dubeom Kim ◽

Seongsill Park ◽

Youngho Chai

Keyword(s):

Real Time ◽

Motion Analysis ◽

Computer Games ◽

Free Space ◽

Handwriting Recognition ◽

Angular Rate ◽

Measurement Unit ◽

Hand Motion ◽

Motion Data ◽

3D Space

We present a wireless-inertial-measurement-unit- (WIMU-) based hand motion analysis technique for handwriting recognition in three-dimensional (3D) space. The proposed handwriting recognition system is not bounded by any limitations or constraints; users have the freedom and flexibility to write characters in free space. It uses hand motion analysis to segment hand motion data from a WIMU device that incorporates magnetic, angular rate, and gravity sensors (MARG) and a sensor fusion algorithm to automatically distinguish segments that represent handwriting from nonhandwriting data in continuous hand motion data. Dynamic time warping (DTW) recognition algorithm is used to recognize handwriting in real-time. We demonstrate that a user can freely write in air using an intuitive WIMU as an input and hand motion analysis device to recognize the handwriting in 3D space. The experimental results for recognizing handwriting in free space show that the proposed method is effective and efficient for other natural interaction techniques, such as in computer games and real-time hand gesture recognition applications.

Download Full-text