In recent years, there has been major interest in the exposure to physical therapy during rehabilitation. Several publications have demonstrated its usefulness in clinical/medical and human machine interface (HMI) applications. An automated system will guide the user to perform the training during rehabilitation independently. Advances in engineering have extended electromyography (EMG) beyond the traditional diagnostic applications to also include applications in diverse areas such as movement analysis. This paper gives an overview of the numerous methods available to recognize motion patterns of EMG signals for both isotonic and isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who would like to select the most appropriate methodology in classifying motion patterns, especially during different types of contractions. For feature extraction, the probability density function (PDF) of EMG signals will be the main interest of this study. Following that, a brief explanation of the different methods for pre-processing, feature extraction and classifying EMG signals will be compared in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

In many gait applications, the focal events are the stance and swing phases. Although detecting gait events using electromyography signals will help the development of assistive devices such as exoskeleton, orthoses, and prostheses, stance and swing phases have yet to be observed using electromyography signals. The core of this study is to propose a classification system for both stance and swing phases based on electromyography signals. This is to be done by extracting the patterns of electromyography signals from time domain features and feeding them into an artificial neural network classifier. In addition, a different number of input features and two prominent training algorithm of artificial neural network have been employed in this study. Eight subjects that participated in this study were divided into two categories namely, learned (first seven subjects) and unlearned data (the remaining one subject). It was observed that Levenberg-Marquardt algorithm with five time domain features performed better than other features with an average percentage of classification accuracy of 87.4%. This system was further tested with electromyography signals of learned and unlearned data to identify the stance and swing phases in order to detect the timing of heel strike and toe off. The mean absolute different values between artificial neural network and footswitch data for learned data were 16 ± 18 ms and 21 ± 18 ms for heel strike and toe off, respectively. For this case, no significant differences (p < 0.05) were observed in mean absolute different for heel strike and toe off detections. Besides, the mean absolute different values of unlearned data were shown to be acceptable, 35 ± 25 ms for heel strike and 49 ± 15 ms for toe off. By the end of this experiment, basing the examination of gait events with electromyography signals using artificial neural network is possible.

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In the past decade, advanced technologies in robotics have been explored to enhance the rehabilitation of post-stroke patients. Previous works have shown that gait assistance for post-stroke patients can be provided through the use of robotics technology in ancillary equipment, such as Ankle Foot Orthosis (AFO). An AFO is usually used to assist patients with spasticity or foot drop problems. There are several types of AFOs, depending on the flexibility of the joint, such as rigid, flexible rigid, and articulated AFOs. A rigid AFO has a fixed joint, and a flexible rigid AFO has a more flexible joint, while the articulated AFO has a freely rotating ankle joint, where the mechanical properties of the AFO are more controllable compared to the other two types of AFOs. This paper reviews the control of the mechanical properties of existing AFOs for gait assistance in post-stroke patients. Several aspects that affect the control of the mechanical properties of an AFO, such as the controller input, number of gait phases, controller output reference, and controller performance evaluation are discussed and compared. Thus, this paper will be of interest to AFO researchers or developers who would like to design their own AFOs with the most suitable mechanical properties based on their application. The controller input and the number of gait phases are discussed first. Then, the discussion moves forward to the methods of estimating the controller output reference, which is the main focus of this study. Based on the estimation method, the gait control strategies can be classified into subject-oriented estimations and phase-oriented estimations. Finally, suggestions for future studies are addressed, one of which is the application of the adaptive controller output reference to maximize the benefits of the AFO to users.

Cobalt particles have been introduced as a filler due to the advantages of embedding their magnetic and electrical properties in magnetorheological elastomer (MRE). In the present research, the rheology and resistance of MRE are experimentally evaluated. Isotropic and anisotropic MRE samples containing silicone rubber and cobalt particles were fabricated. The magnetic properties of MRE are conducted using a vibrating sample magnetometer (VSM). The morphological aspects of MRE are observed by using field emission scanning electron microscopy (FESEM) and characterized by energy-dispersive X-ray spectroscopy (EDX). Rheological properties under various magnetic field strengths were measured for the magnetic field, strain amplitude, and frequency sweep test by using a parallel-plate rheometer. Subsequently, the resistance of MRE is tested under different applied forces and magnetic fields. The MRE storage modulus depicted an enhancement in field-dependent modulus across all the applied magnetic fields. The electrical resistance generated from the sample can be manipulated by external magnetic fields and mechanical loads. The conductivity of MRE is due to the existence of cobalt arrangements observed by FESEM. By introducing cobalt as filler and obtaining satisfactory results, the study might open new avenues for cobalt to be used as filler in MRE fabrication for future sensing applications.

This study aimed to characterize the influence of Centella asiatica at 0.3% and 0.7% on antioxidant activities; mechanical and physical properties of chicken skin gelatin/CMC/ Centella asiatica film. Characterization of the blended films with 0.7% Centella asiatica extract shows higher antioxidant activities with a total phenolic content of 0.36 mg/g of GAE, DPPH of 89.26%, and reducing power of 0.80 nm compared to 0.3% Centella asiatica extract added where the total phenolic content was 0.29 mg/g of GAE, DPPH of 89.26% and reducing power of 0.80 nm. The addition of 0.3% of Centella extract provide higher value in tensile strength, elongation at break, melting point and transparency but lower in UV-light penetration and crystallinity of the films. While the addition of 0.7% Centella extract contributes to higher value in WVP and puncture test. In conclusion, the incorporation of Centella asiatica extracts on film greatly increased antioxidant levels and improved some of the mechanical and physical properties of the film blends.

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The widespread use of magnetorheological elastomer (MRE) materials in various applications has yet to be limited due to the fact that there are substantial deficiencies in current experimental and theoretical research on its microstructural durability behavior. In this study, MRE composed of silicon rubber (SR) and 70 wt% of micron-sized carbonyl iron particles (CIP) was prepared and subjected to stress relaxation evaluation by torsional shear load. The microstructure and particle distribution of the obtained MRE was evaluated by a field emission scanning electron microscopy (FESEM). The influence of constant low strain at 0.01% is the continuing concern within the linear viscoelastic (LVE) region of MRE. Stress relaxation plays a significant role in the life cycle of MRE and revealed that storage modulus was reduced by 8.7%, normal force has weakened by 27%, and stress performance was reduced by 6.88% along approximately 84,000 s test duration time. This time scale was the longest ever reported being undertaken in the MRE stress relaxation study. Novel micro-mechanisms that responsible for the depleted performance of MRE was obtained by microstructurally observation using FESEM and in-phase mode of atomic force microscope (AFM). Attempts have been made to correlate strain localization produced by stress relaxation, with molecular deformation in MRE amorphous matrix. Exceptional attention was focused on the development of molecular slippage, disentanglement, microplasticity, microphase separation, and shear bands. The relation between these microstructural phenomena and the viscoelastic properties of MRE was diffusely defined and discussed. The presented MRE is homogeneous with uniform distribution of CIP. The most significant recent developments of systematic correlation between the effects of microstructural deformation and durability performance of MRE under stress relaxation has been observed and evaluated.