Investigating the performance of an amplitude-independent algorithm for detecting the hand muscle activity of stroke survivors.

To make robotic hand devices controlled by surface electromyography (sEMG) signals feasible and practical tools for assisting patients with hand impairments, the problems that prevent these devices from being widely used have to be overcome. The most significant problem is the involuntary amplitude variation of the sEMG signals due to the movement of electrodes during forearm motion. Moreover, for patients who have had a stroke or another neurological disease, the muscle activity of the impaired hand is weak and has a low signal-to-noise ratio (SNR). Thus, muscle activity detection methods intended for controlling robotic hand devices should not depend mainly on the amplitude characteristics of the sEMG signal in the detection process, and they need to be more reliable for sEMG signals that have a low SNR. Since amplitude-independent muscle activity detection methods meet these requirements, this paper investigates the performance of such a method on people who have had a stroke in terms of the detection of weak muscle activity and resistance to false alarms caused by the involuntary amplitude variation of sEMG signals; these two parameters are very important for achieving the reliable control of robotic hand devices intended for people with disabilities. A comparison between the performance of an amplitude-independent muscle activity detection algorithm and three amplitude-dependent algorithms was conducted by using sEMG signals recorded from six hemiparesis stroke survivors and from six healthy subjects. The results showed that the amplitude-independent algorithm performed better in terms of detecting weak muscle activity and resisting false alarms.

Enhancing Photocurrent Performance Based on Photoanode Thickness and Surface Plasmon Resonance Using Ag-TiO2 Nanocomposites in Dye-Sensitized Solar Cells.

This study investigated the different thicknesses of TiO2 photoanode films and the effect of surface plasmon resonance (SPR) of Ag-TiO2 nanocomposites on the current-voltage (I-V) performance of dye-sensitized solar cells (DSSC). The TiO2 layer was deposited using the doctor blade technique and the thickness of the TiO2 films was controlled by using a different number of Scotch tape layers. The silver nanoparticles (AgNP) were synthesised using a chemical reduction method and the concentration of sodium citrate as a reducing agent was varied from 4 to 12 mM to study the effect of citrate ion on the size of the nanoparticles. Ag-TiO2 nanopowder was prepared by adding pure anatase TiO2 powder into AgNP colloidal solution. The mixture was left to dry for 24 h to obtain Ag-TiO2 powder for paste preparation. The three-layer Scotch tape, with thickness of 14.38 µm, achieved a high efficiency of 4.14%. This results showed that three layers was the optimal thickness to improve dye loading and to reduce the charge recombination rate. As for the Ag-TiO2 nanocomposites, 10 mM of AgNP, with a mean diameter of 65.23 nm and high efficiency of 6.92%, proved that SPR can enhance the absorption capability of dye and improve the photon-to-electron generation.

Characterization of surface electromyography using time domain features for determining hand motion and stages of contraction.

Surface electromyography (SEMG) signals can provide important information for prosthetic hand control application. In this study, time domain (TD) features were used in extracting information from the SEMG signal in determining hand motions and stages of contraction (start, middle and end). Data were collected from ten healthy subjects. Two muscles, which are flexor carpi ulnaris (FCU) and extensor carpi radialis (ECR) were assessed during three hand motions of wrist flexion (WF), wrist extension (WE) and co-contraction (CC). The SEMG signals were first segmented into 132.5 ms windows, full wave rectified and filtered with a 6 Hz low pass Butterworth filter. Five TD features of mean absolute value, variance, root mean square, integrated absolute value and waveform length were used for feature extraction and subsequently patterns were determined. It is concluded that the TD features that were used are able to differentiate hand motions. However, for the stages of contraction determination, although there were patterns observed, it is determined that the stages could not be properly be differentiated due to the variability of signal strengths between subjects.