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.

Electronic noses (e-noses), artificial sensor systems generally consisting of chemical sensor arrays for the detection of volatile compound profiles, have potential applications in respiratory medicine. We assessed within-day and between-day repeatability of an e-nose made from 32 sensors in patients with stable chronic obstructive pulmonary disease (COPD). We also compared between-day repeatability of an e-nose, fraction of exhaled nitric oxide (FENO) and pulmonary function testing. Within-day and between-day repeatability for the e-nose was assessed in two breath samples collected 30 min and seven days apart, respectively. Repeatability was expressed as an intraclass correlation coefficient (ICC). All sensors had ICC above 0.5, a value that is considered acceptable for repeatability. Regarding within-day repeatability, ICC ranged from 0.75 to 0.84 (mean = 0.80 ± 0.004). Sensors 6 and 19 were the most reproducible sensors (both, ICC = 0.84). Regarding between-day repeatability, ICC ranged from 0.57 to 0.76 (mean = 0.68 ± 0.01). Sensor 19 was the most reproducible sensor (ICC = 0.76). Within-day e-nose repeatability was greater than between-day repeatability (P < 0.0001). Between-day repeatability of FENO (ICC = 0.91) and spirometry (ICC range = 0.94-0.98) was greater than that of e-nose (mean ICC = 0.68). In patients with stable COPD, the e-nose used in this study has acceptable within-day and between-day repeatability which varies between different sensors.

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