Gyroscopes have been proposed as sensors for ambulatory gait analysis and functional electrical stimulation systems. These applications often require detection of the initial contact (IC) of the foot with the floor and/or final contact or foot off (FO) from the floor during outdoor walking. Previous investigations have reported the use of a single gyroscope placed on the shank for detection of IC and FO on level ground and incline walking. This paper describes the evaluation of a gyroscope placed on the shank for determination of IC and FO in subjects ascending and descending a set of stairs. Performance was compared with a reference pressure measurement system. The absolute mean difference between the gyroscope and the reference was less than 45 ms for IC and better than 135 ms for FO for both activities. Detection success was over 93%. These results provide preliminary evidence supporting the use of a gyroscope for gait event detection when walking up and down stairs.

The goal of this preliminary study was to investigate the feasibility of using singular value decomposition (SVD) to eliminate the M-wave from the surface electromyogram (EMG) of an electrically stimulated paretic muscle in order to extract the volitional response. An SVD-based algorithm combining the subspaces method and a subsequent filtering is presented. It was evaluated with EMG signals registered from surface of electrically stimulated muscles with simulated paresis and its performance was compared with a conventional fixed filter. The filtering strategy proposed showed a good performance in static conditions where there were no traces of the M-wave. In dynamic conditions, the SVD-based algorithm was robust but with some remaining M-wave traces. It would be as a consequence of modifications in the data matrix and, therefore, in the subspaces generator columns and the singular values. In general, the fixed filter was very sensitive to input signal disturbances. In all of these conditions there was a greater power reduction for the SVD-based filter than for the fixed filter.

A Brain Computer Interface is a system that provides an artificial communication between the human brain and the external world. The paradigm based on event related evoked potentials is used in this work. Our main goal was to efficiently solve a binary classification problem: presence or absence of P300 in the registers. Genetic Algorithms and Support Vector Machines were used in a wrapper configuration for feature selection and classification. The original input patterns were provided by two channels (Oz and Fz) of resampled EEG registers and wavelet coefficients. To evaluate the performance of the system, accuracy, sensibility and specificity were calculated. The wrapped wavelet patterns show a better performance than the temporal ones. The results were similar for patterns from channel Oz and Fz, together or separated.

Short-term (5 min) heart rate variability analysis (HRV) demand for monitoring people's health and well-being status is increasing due to portable sensors and mobile applications. Five minutes analysis is extensive compared to that used in measurements of glucose, blood pressure, temperature, etc. To reduce costs and achieve optimal performance in personal health assessment, ultra-short-term monitoring less than 5 min is suggested. The aim of this work is to establish the minimum ultra-short-term HRV analysis time required for LF/HF (LH) (frequency domain sympathy-vagal balance indicator) and SD21 (Poincare graphic domain sympathy-vagal balance indicator) to be equivalent to those obtained by 5 min HRV. From electrocardiograms (ECGs), processing of 23 subjects in the experiment and series of 60, 90, 120, 180, 240 and 300 s (5 min) were obtained. The HRV time series was calculated and each LH and SD21 indicator in each of the ultra-short series was compared with the 300 s (Gold Standard), using different concordance analysis methods, such us Pearson correlation, Bland Altman, Cohen Delta. The ultra-short-term HRV LH indicator is equivalent to short-term HRV (5 min) in series equal or greater than 180 s while SD21 was equivalent to series equal or greater than 120 s.