Smart textiles provide an opportunity to simultaneously record various electrophysiological signals, e.g., ECG, from the human body in a non-invasive and continuous manner. Accurate processing of ECG signals recorded using textile sensors is challenging due to the very low signal-to-noise ratio (SNR). Signal processing algorithms that can extract ECG signals out of textile-based electrode recordings, despite low SNR are needed. Presently, there are no textile ECG datasets available to develop, test and validate these algorithms. In this paper we attempted to model textile ECG signals by adding the textile sensor noise to open access ECG signals. We employed the linear predictive coding method to model different features of this noise. By approximating the linear predictive coding residual signals using Kernel Density Estimation, an artificial textile ECG noise signal was generated by filtering the residual signal with the linear predictive coding coefficients. The synthetic textile sensor noise was added to the MIT-BIH Arrhythmia Database (MITDB), thus creating Textile-like ECG dataset consisting of 108 trials (30 min each). Furthermore, a Python code for generating textile-like ECG signals with variable SNR was also made available online. Finally, to provide a benchmark for the performance of R-peak detection algorithms on textile ECG, the five common R-peak detection algorithms: Pan & Tompkins, improved Pan & Tompkins (in Biosppy), Hamilton, Engelse, and Khamis, were tested on textile-like MITDB. This work provides an approach to generating noisy textile ECG signals, and facilitating the development, testing, and evaluation of signal processing algorithms for textile ECGs.

Functional electrical stimulation (FES), applied to paralyzed or paretic muscles, can facilitate arm-free standing in patients with neurological disorders. At the same time, it has been shown that the able-bodied central nervous system regulates standing balance using a control strategy similar to proportional-integral-derivative (PID) control. The objective of this study was to investigate the capability of a PID controller for regulating an FES system applied to the ankle flexors for maintaining standing balance. We compared the performance of this controller to voluntary balance control of able-bodied individuals with compromised visual, vestibular and proprioceptive senses. According to our experimental results, the proposed PID control strategy outperformed the disrupted voluntary control of the ankle flexors while it did not require larger control efforts, in terms of the ankle torque. Therefore, this control strategy could be considered in for use in neurological patients, including spinal cord injured individuals, after further validations in the future.

In eye-gaze-based human-computer interfaces, the most commonly used mechanism for generating activation commands (i.e., mouse clicks) is dwell time (DT). While DT can be relatively efficient and easy to use, it is also associated with the possibility of generating unintentional activation commands -- an issue that is known as the Midas' touch problem. To address this problem, we proposed to use a "tooth-clicker" (TC) device as a mechanism for generating activation commands independently of the activity of the eyes.

Usually friction is considered to be a velocity-dependent phenomenon. As a result, most of the available friction models describe friction only as a function of velocity. However, there is also positional dependency which is routinely ignored. Experiments reported in this paper confirm previously published findings of others that friction is position dependent, and that this dependency should not be ignored. Therefore, it is necessary to develop models that describe friction as a function of both position and velocity. Furthermore, available models of nonlinear friction are usually developed by investigating simple cases, such as sliding of two flat surfaces or rolling of a perfectly rounded body on a flat surface. These models do not accurately describe friction generated by mechanisms that have numerous sliding and rolling parts. In this paper, a new spectral-based modeling technique capable of describing nonlinear friction as a function of position and velocity is proposed. This technique identifies the specific contribution to the overall friction of every moving part of the mechanism.

In this paper a new method of modeling nonlinear friction as a function of both position and velocity is introduced. The proposed empirical model uses spectral analysis to identify the main sources of position dependent friction in complex mechanisms. In addition, the model describes the contribution to the overall friction of every moving part of the mechanism. In the paper, the proposed spectral-based modeling technique is used to describe the static friction of the first joint of PUMA 560 robot. Development of a complete friction model, which describes both static and sliding friction of complex mechanisms as a function of position and velocity, is the topic of our future work.

The paper describes a novel approach to accurate positioning control of mechanical devices with nonlinear (stick-slip) friction. The controller applies narrow torque pulses to achieve the desired displacement of the mechanism. The pulse shapes are computed through fuzzy logic approximation of the dependence between the desired displacement and the pulse shape. The stability conditions of the proposed controller are derived taking into account an influence of random variation of the friction properties. A detailed experimental study of the system response to the torque pulses of different shapes and a detailed controller design are presented for a direct-drive manipulator setup. It is experimentally demonstrated that the developed controller achieves positioning precision up to the limits of the position encoder resolution.