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Introduction: Spinal cord injuries, traumas, natural aging, and strokes are the main causes of arm impairment or even a chronic disability for an increasing part of the population. Therefore, robotic devices can be essential tools to help individuals afflicted with hand deficit with the activities of daily living in addition to the possibility of restoring hand functions by rehabilitation. Because the surface electromyography (sEMG) control paradigm has recently emerged as an interesting intention control method in devices applied to rehabilitation, the concentration in this study has been devoted to sEMG-controlled hand robotic devices, including gloves and exoskeletons that are used for rehabilitation and for assistance in daily activities.

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.

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.