Showing papers by "Paolo Bonato published in 1998"

A statistical method for the measurement of muscle activation intervals from surface myoelectric signal during gait

Abstract: The aim of this work is to present an original double-threshold detector of muscle activation, specifically developed for gait analysis. This detector operates on the raw myoelectric signal and, hence, it does not require any envelope detection. Its performances are fixed by the values of 3 parameters, namely, false-alarm probability (P/sub fa/), detection probability, and time resolution. Double-threshold detectors are preferable to single-threshold ones because, for a fixed value of the P/sub fa/, they yield higher detection probability; furthermore, they allow the user to select the couple false alarm-detection probability with a higher degree of freedom, thus, adapting the performances of the detector to the characteristics of the myoelectric signal of interest and of the experimental situation. Here, first the authors derive the detection algorithm and describe different strategies for selecting its parameters, then they present the performances of the proposed procedure evaluated by means of computer simulations, and finally they report an example of application to myoelectric signals recorded during gait. The characterization of the proposed double-threshold detector demonstrates that, in most practical situations, the bias of the estimates of the on-off transitions is smaller than 10 ms, the standard deviation may be kept lower than 15 ms, and the percentage of erroneous patterns is below 5%. These results show that this detection approach is satisfactory in research applications as well as in the clinical practice.

Time-frequency and cross-time-frequency based techniques for the structural identification of systems

Abstract: We propose a new method for the structural identification of systems based on the time-frequency (TF) and the cross-time-frequency (XTF) distributions of the accelerometric signals recorded from the system during its normal service condition. By means of the technique proposed it is possible to extract the modal parameters of the system in an unequivocal manner. The performance of the method were tested by using numerical simulations of a linear system and an input constituted by a "sine sweep" excitation.

Analysis of the surface myoelectric signal by means of a cross-time-frequency based technique

Abstract: This paper presents a cross-time-frequency (XTF) based algorithm for the estimation of the instantaneous frequency (IF) of nonstationary stochastic processes and discusses its application to the analysis of the surface myoelectric signal (SMES) recorded during dynamic muscle contractions. This original algorithm derives from that previously proposed by Boashash and O'Shea (1993) and further investigated by Ristic and Boashash (see IEEE Trans. on Signal Processing vol.44, p.1549-53, 1996), which was originally proposed for the analysis of deterministic signals. We assessed the estimation error of the modified algorithm by means of a synthesized process with statistical properties similar to those of SMES. Bias and standard deviation of the IF estimates were obtained for signal to noise ratio (SNR) values ranging from 5 dB to 100 dB and for different frequency contents of the SMES. The algorithm was found satisfactory for research as well as for clinical applications. A sample application is shown where we utilize this method for the estimation of the IF of real SMES recorded from the extensors and flexors during repetitive isokinetic knee flexion-extension movements.