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 three parameters, namely, false-alarm probability (Pfa), detection probability, and time resolution. Double-threshold detectors are preferable to single-threshold ones because, for a fixed value of the Pfa, 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. In this paper, first we derive the detection algorithm and describe different strategies for selecting its parameters, then we present the performances of the proposed procedure evaluated by means of computer simulations, and finally we 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.

A New Tool for the Automatic Detection of Muscular Voluntary Contractions in the Analysis of Electromyographic Signals

“…For future work, we intend to add new important features for the EMG analysis, different types of activation detection algorithms (e.g. double-threshold detector Bonato et al, 1998 or adaptive threshold methods), simultaneously run the activations detection algorithm for all measured EMG signals and allow for individual preferences in the signal filtering steps before applying the activation detection operations. We also aim to perform more tests with the developed algorithms, not only for accuracy and speed performance, but also concerning the possibility of adding noise to the EMG signals and analyze the influence in the detected activations.…”

Optimizing interoperability between video-oculographic and electromyographic systems

“…On the other hand, we can characterize a fixation by applying explicit spatial and temporal criteria. We can detect a fixation if the eye remains within a radius of 1° during a temporal fixation threshold of 200 ms, which is the de facto standard [19]. The gaze-tracking system measures eye-movement spatial dispersion and speed to infer fixation status (Figure 2(a)) and differentiate between fixation and saccadic movements of the eye.…”

“…We selected the activation detection algorithm after an evaluation study of some of the available algorithms. We compared six different algorithms, namely, Hodges and Bui [36], Bonato et al [19], Lidierth [37], Abbink et al [38], and approximated generalized likelihood ratio (AGLR) step and AGLR ramp [39].…”

“…Depending on the signal strength, many involuntary selections can occur because of noise or involuntary muscle activations when high-sensitivity levels are used [19][20]. In addition, certain environments can also introduce artifacts due to movement.…”

BIOSIGNALS EVENTS DETECTION - A Morphological Signal-independent Approach

“…A comparative study regarding several methods for EMG signals onset detection, including those proposed by Hodges (Hodges and Bui, 1996) and Bonato (Bonato et al, 1998), is reported by Staude (Staude et al, 2001). Another possible application is related to the detection of general transient events.…”