A framework for daily activity monitoring and fall detection based on surface electromyography and accelerometer signals.

As an essential branch of context awareness, activity awareness, especially daily activity monitoring and fall detection, is important to healthcare for the elderly and patients with chronic diseases. In this paper, a framework for activity awareness using surface electromyography and accelerometer (ACC) signals is proposed. First, histogram negative entropy was employed to determine the start- and end-points of static and dynamic active segments.

A measurement-domain adaptive beamforming approach for ultrasound instrument based on distributed compressed sensing: Initial development.

High efficient acquisition of the sensor array signals and accurate reconstruction of the backscattering medium are important issues in ultrasound imaging instrument. This paper presents a novel measurement-domain adaptive beamforming approach (MABF) based on distributed compressed sensing (DCS) which seeks to simultaneously measure signals that are each individually sparse in some domain(s) and also mutually correlated with much few measurements under the Nyquist rate. Instead of sampling conventional backscattering signals at the Nyquist rate, few linear projections of the returned signal with random vectors are taken as measurements, which can reduce the amount of samples per channel greatly and makes the real-time transmission of sensor array data possible.

Novel approach for time-varying bispectral analysis of non-stationary EEG signals.

novel parametric method, based on the non-Gaussian AR model, is proposed for the partition of on-stationary EEG data into a finite set of third-order stationary segments. With the assumption of piecewise third-order stationarity of the signal, a series of parametric bispectral estimations of the non-stationary EEG data can be performed so as to describe the time-varying non-Gaussian nonlinear characteristics of the observed EEG signals. A practical method based on the fitness of third-order statistics of the signal by using the non-Gaussian AR model, together with an algorithm with CMI is presented.

Effective Approach for Detecting the Single Trial ERP Using Rational Gaussian Neural Network.

A novel approach is proposed to deal with the problem of detecting the single trial ERP using a modified RBF neural network, rational Gaussian network. The Gaussian RBF is normalized to obtain optimal behavior of noise suppression even at low SNR. The performance of the proposed scheme is also evaluated with both MSE and the tracking ability.

Analysis of time-varying coherence of EEG during face recognition based on harmonic transform.

Distinct cortical activity during face recognition was reported by a number of studies. Classical coherence analysis reflects the synchronization between two random processes in certain frequency under the assumption that the signals are stationary. In EEG study, the coherence analysis is mainly used to analyze the coupling and drive-response relation about the brain activities in different regions.