1086 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 57, NO. 6,JUNE 2008 Best Match Procedures in Transition Phase Identification for Handgrip Test Analysis in Normal and Pathological Subjects Gregorio Andria, Filippo Attivissimo, Nicola Giaquinto, Anna Maria Lucia Lanzolla, and Nicola Sasanelli Abstract—In this paper, the analysis of biomedical data char- acterizing 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. Index Terms—Biomedical signal analysis, force sensor, hand- grip, mathematical model. I. I NTRODUCTION G RIP STRENGTH is evaluated by occupational therapists and others in a range of clinical settings, particularly hand therapy and occupational rehabilitation [1]. It is fast and easy to perform and produces results that are simple to measure [2]. There are numerous measures of grip strength involving dif- ferent ranges of assessment protocols, testing positions, and methods of interpretation [3], [4]. For this reason, the authors propose the analysis of grip strength to evaluate neurological pathologies such as Parkinson’s disease. This is a degenerative disorder of the central nervous system, and its primary symptoms are bradyki- nesia (slowness in voluntary movement); tremors in the hands, fingers, forearm, or foot; rigidity or stiff muscles; and poor balance. In previous papers [5]–[7], the authors presented the mea- surement system realized by the Section of Biomedical Engi- Manuscript received May 22, 2006; revised December 11, 2007. G. Andria, F. Attivissimo, N. Giaquinto, and A. M. L. Lanzolla are with the Laboratory for Electric and Electronic Measurements, Department of Electrics and Electronics, Polytechnic of Bari, 70125 Bari, Italy (e-mail:andria@misure. poliba.it; attivissimo@misure.poliba.it; giaquinto@misure.poliba.it; lanzolla@ misure.poliba.it). N. Sasanelli is with the Section of Biomedical Engineering, Department of General and Specialist Surgery Science, University of Bari, 70124 Bari, Italy (e-mail: n.sasanelli@bioingegneria.uniba.it). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TIM.2007.915474 neering, Department of General and Specialist Surgery Science, University of Bari, Bari, Italy, in collaboration with the Labo- ratory for Electric and Electronic Measurements, Department of Electrics and Electronics, Polytechnic of Bari. The proposed instrument measures the strength of the palmar grip by means of a sensor based on traction/compression load cells. An ac- quisition system with a sampling frequency of 100 Hz records the measured data, allowing complete characterization of hand motor activities. A preliminary study in the time domain has been carried out to highlight the main variables that influence the grip strength measurement and to identify some biomechanical parameters characterizing the palmar grip performances [8]; these parame- ters have been analyzed from a statistical point of view with regard to their statistical significance, in particular, with the aim of distinguishing pathologic subjects from healthy subjects and of focusing the motor impairments or abnormalities [9]. In this paper, an in-depth study of the main factors influenc- ing the estimate of the characteristic biomedical parameters in the time domain is carried out. The analysis shows that both the contraction and release phases are useful quantities to indicate the pathological level of the patient. Thus, for a correct analysis of the data, a suitable postprocessing technique of the acquired biomedical samples is necessary. First, the influence of the noise and the consequences of filtering methods have been analyzed, with the aim of reducing the disturbance components caused by the acquisition system. Then, to obtain objective results that are useful for a correct statistical analysis, an accurate estimation of the meaningful parameters, such as rise and fall times and rise speed and fall speeds, has been carried out. For this aim, different methods have been proposed, and the relevant performances have been investigated. These methods allow a correct determination of the characteristic points that delimit the different phases. II. FACTORS AFFECTING DATA ANALYSIS The functional tests were performed on a set of 44 people: 20 healthy subjects (11 men and nine women; average age of 61 ± 4 years—control group) and 24 pathological subjects (15 men and nine women affected by Parkinson’s disease; av- erage age of 68 ± 6 years). The recorded data were analyzed in the time domain to identify the typical shape of the time–strength curves. The analysis of the acquired data showed that the signals related 0018-9456/$25.00 © 2008 IEEE