Best Match Procedures in Transition Phase Identification for Handgrip Test Analysis in Normal and Pathological Subjects

• Published in: IEEE transactions on instrumentation and measurement Vol. 57; no. 6; pp. 1086 - 1093
• Main Authors: Andria, G., Attivissimo, F., Giaquinto, N., Lanzolla, A.M.L., Sasanelli, N.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2008; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bioinformatics; Biomedical measurements; Biomedical signal analysis; Computer programs; Data analysis; Data mining; Filtering; Filtration; Force measurement; force sensor; handgrip; Information analysis; Instrumentation; Limbs; mathematical model; Mathematical models; Motors; Parkinson's disease; Pathology; Software; Software measurement; Testing; mathematical model; Biomedical signal analysis; force sensor; handgrip
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2007.915474

In this paper, the analysis of biomedical data characterizing motor activities of the limbs is carried out with the aim of extracting useful information for evaluating the state of subjects affected by Parkinson's disease. Initially, a simple and inexpensive system that measures the force of the palmar grip has been presented; then, a suitable software protocol that is able to record, manage, and interpret the acquired data has been realized. Afterward, a first analysis in the time domain has led to the identification of some biomedical parameters characterizing motor activities of the limbs; the study of these parameters and the analysis of the correlations between the acquired data have permitted the development of suitable models describing the time behavior of the palmar grip. In this paper, the authors propose a more detailed study that evaluates the filtering, phase noise effects, and performance of different methods used to estimate the characteristic parameters of the palmar grip.