SKILL EVALUATION OF MOVEMENT BASED ON HUMAN LIMB ELECTRICAL IMPEDANCE

The purpose of this work is to propose a skill evaluation of tennis movement based on forearm electrical impedance and its measurement method. We try to evaluate the pattern of movement using the pattern of impedance waveform and the stability of movement using the reproducibility of impedance waveform. It can be intuitively evaluated reproducibility and mobility of serve movement using waveform of forearm electrical impedance (Z). Consequently 4 parameters which show reproducibility and mobility using waveform of Z was defined. We made a trial about principal component analysis of Z during serve and got a scatter diagram of first second principal component about 9 subjects including skilful and beginner players. Then the principal component analysis devided subjects into skilful and beginner players. The measurement method of Z used the four electrodes technique based on constant current(50 k Hz,500 uA). Z changes with the changes of cross sectional area of muscle and blood volume caused by the change of joint angle and acceleration. Hence Z has information of movement. The results of this experiment was as follows. In waveforms of Z during serve with a ball and without a ball, each waveform of a skillful player was very similar to each other, but each waveform of a beginner player was not. Z of a skillful player varied more than that Z of a beginner player, because the movement of a skillful player is larger than that of a beginner player. The advantages of this method are as follows. There is no spatial and temporal limitation for measurement. The subject is scarcely restricted in movement. Although we must select appropriate locations of electrodes in each type of movement, this method can be expected to have applications for various sports

A graph-based mathematical morphology reader

This survey paper aims at providing a "literary" anthology of mathematical morphology on graphs. It describes in the English language many ideas stemming from a large number of different papers, hence providing a unified view of an active and diverse field of research

Binary Particle Swarm Optimization based Biclustering of Web usage Data

Web mining is the nontrivial process to discover valid, novel, potentially useful knowledge from web data using the data mining techniques or methods. It may give information that is useful for improving the services offered by web portals and information access and retrieval tools. With the rapid development of biclustering, more researchers have applied the biclustering technique to different fields in recent years. When biclustering approach is applied to the web usage data it automatically captures the hidden browsing patterns from it in the form of biclusters. In this work, swarm intelligent technique is combined with biclustering approach to propose an algorithm called Binary Particle Swarm Optimization (BPSO) based Biclustering for Web Usage Data. The main objective of this algorithm is to retrieve the global optimal bicluster from the web usage data. These biclusters contain relationships between web users and web pages which are useful for the E-Commerce applications like web advertising and marketing. Experiments are conducted on real dataset to prove the efficiency of the proposed algorithms

STV-based Video Feature Processing for Action Recognition

In comparison to still image-based processes, video features can provide rich and intuitive information about dynamic events occurred over a period of time, such as human actions, crowd behaviours, and other subject pattern changes. Although substantial progresses have been made in the last decade on image processing and seen its successful applications in face matching and object recognition, video-based event detection still remains one of the most difficult challenges in computer vision research due to its complex continuous or discrete input signals, arbitrary dynamic feature definitions, and the often ambiguous analytical methods. In this paper, a Spatio-Temporal Volume (STV) and region intersection (RI) based 3D shape-matching method has been proposed to facilitate the definition and recognition of human actions recorded in videos. The distinctive characteristics and the performance gain of the devised approach stemmed from a coefficient factor-boosted 3D region intersection and matching mechanism developed in this research. This paper also reported the investigation into techniques for efficient STV data filtering to reduce the amount of voxels (volumetric-pixels) that need to be processed in each operational cycle in the implemented system. The encouraging features and improvements on the operational performance registered in the experiments have been discussed at the end