User's Body Motion Research Articles

A review on emg signal classification for human computer interaction

Human Computer Interaction (HCI) systems have become an increasingly essential aspect of our everyday lives as the importance of computerized devices in society grows. The efficient use of the available information flow of computer, communication, and display technologies is determined by HCI. In recent years, there has been a lot of buzz about developing intuitive interfaces that can detect and convert the user's body motions into computer instructions. Various biomedical signals (biosignals) that may be obtained from a specific tissue, organ, or cell system like the nervous system can be utilized for neural connection with computers. Electroencephalogram (EEG), Electrooculogram (EOG), and Electromyogram are some examples (EMG). Physically handicapped people benefit greatly from such methods. Many efforts have been made to create HCI using EMG signals from gesture. The development of continuous EMG signal categorization for graphical controller, which allows the physically handicapped to utilize word processing applications and other personal computer software, as well as the internet, is presently underway in the field of EMG signal processing and controller. It also allows for the manipulation of robotic devices, prosthetic limbs, I/O for virtual reality games, and fitness equipment, among other things. The majority of developmental research is focused on the use of neural networks to recognize patterns. The EMG controller may be configured to recognize gestures via signal analysis of muscle action potential groups. The aim of this review article is to examine the different methods and algorithms used for EMG signal categorization in order to convert EMG signals into computer commands.

Appearance, motion, and embodiment: unpacking avatars by fine‐grained communication analysis

SummaryThanks to the maturity of motion tracking technologies, it became easy and inexpensive to use avatars that reflect the user's facial and body motions in real time. There is a possibility that those real‐time avatars can serve as a substitute for videos in distant communication. We conducted two experiments to confirm positive effects of avatars on distant communication. In the first experiment, the subjects watched a conversation partner's video, avatar, photo, or nothing when speaking to the partner. Because the video and avatar delivered the partner's motion while the video and photo delivered the partner's appearance, we were able to observe the effects of the motion and appearance separately. As a result, we found that presenting the motion via a video or an avatar increased the degree of the smoothness of speaking to the partner, but the appearance did not significantly affect such an influence. In the second experiment, we compared the avatar and teleoperated robot to find how a physical embodiment influences the degree of the smoothness of speech. Teleoperated robots resemble avatars in transmitting the user's body motions without disclosing the user's appearance but differ in reflecting these motions onto a robot that has a physical embodiment. We predicted that the robot produces smoother speech because the physical embodiment might make the body motions recognizable. However, the robot decreased the degree of the smoothness of speech against our expectation. The results of these experiments showed that real‐time avatars that present only body motions are enough to smooth distant communication. Copyright © 2014 John Wiley & Sons, Ltd.

A training machine with dynamic load-control function based on muscle activity information

An advanced physical training machine for the elderly is proposed. This machine has a self-tuning loading-level function based on the muscular condition of the user. The loading force is generated by an electric motor. A controller calculates the proper loading force for the user at every moment based on the user's muscle activity level. Muscle activity is measured by a muscle stiffness sensor, which was developed as a man–machine interface in a wearable device to assist a disabled person's body motions. By using this sensor, a reliable measurement of muscle activity is available, and the easy installation does not disturb the user's body motions. An experimental training machine was prototyped, and its excellent abilities in both controlling the loading force and measuring the muscle activity throughout the periods of the training is demonstrated. The experimental results are reported in this article.