ABSTRACT
Non-contact vital sign detection technique provides an effective usage in health monitoring applications. A vital sign detector was designed based on microwave bio-radar technique. Using Doppler principle, continuous wave bioradar was designed for tiny body movement detection, short-time Fourier transform and interpolation algorithm were adopted for heart and respiration rate extraction, embedded system was used for system integration, real-time signal processing software was designed on it. Experiments were done by using simulation device and human body for research and performance evaluation. The result shows that the proposed prototype can be used for single target vital signs detection at the distance of 90 cm, and the heart rate result shows a 96% recognition rate.
Subject(s)
Humans , Equipment Design , Physical Examination , Methods , Radar , Vital SignsABSTRACT
This article reported researches on the pattern recognition of finger key-press gestures based on surface electromyographic (SEMG) signals. All the gestures were defined referring to the PC standard keyboard, and totally 16 sorts of key-press gestures relating to the right hand were defined. The SEMG signals were collected from the forearm of the subjects by 4 sensors. And two kinds of pattern recognition experiments were designed and implemented for exploring the feasibility and repeatability of the key-press gesture recognition based on SEMG signals. The results from 6 subjects showed, by using the same-day templates, that the average classification rates of 16 defined key-press gestures reached above 75.8%. Moreover, when the training samples added up to 5 days, the recognition accuracies approached those obtained with the same-day templates. The experimental results confirm the feasibility and repeatability of SEMG-based key-press gestures classification, which is meaningful for the implementation of myoelectric control-based virtual keyboard interaction.
Subject(s)
Humans , Algorithms , Electromyography , Methods , Fingers , Gestures , Movement , Physiology , Muscle, Skeletal , Physiology , Pattern Recognition, Automated , Methods , Signal Processing, Computer-AssistedABSTRACT
Objective To identify the model parameters of surface Electromyography (sEMG) by comparison between simulated and recorded signals. Methods A physiological model of sEMG signal was established basing on several logical hypothetical conditions, such as motor unit action potentials (MUAP), motor unit recruitment and firing behavior caused by excitation, architecture of volume conductor and other simulated factors. According to the matched shapes between the simulated and recorded sEMG signals, a group of model parameters was obtained; according to the similar power spectrum variations of real sEMG signals, decreased muscle fiber conduction velocity (MFCV) was applied to simulate the sEMG signals of the fatigued muscle. Results The experimental results showed that the simulated superimposed MUAP shapes could be matched with the recorded MUAPs satisfactorily by adjusting some proper physiological parameters of the model. When the MFCV of each fiber was assumed to decrease, the mean and median frequency (MNF, MDF) of the simulated sEMG signals declined, and this phenomenon was very similar to that of the recorded sEMG signals and could be used to interpret the muscle fatigue process. Conclusion This model provides an effective approach to simulate real sEMG signals, and the simulated signals can also be used to help the analysis of recorded sEMG signals.