Sleep disorder is a medical disease of the sleep patterns, which commonly suffered by the elderly. Sleep disorders diagnosis and treatment are considered to be challenging due to a time-consuming and inconvenient process for the patient. Moreover, the use of Polysomnography (PSG) in sleep disorder diagnosis is a high-cost process. Therefore, we propose an efficient classification method of sleep disorder by merely using electrocardiography (ECG) signals to simplify the sleep disorders diagnosis process. Different from many current related studies that applied a five-minute epoch to observe the main frequency band of the ECG signal, we perform a pre-processing technique that suitable for the 30-seconds epoch of the ECG signal. By this simplification, the proposed method has a low computational cost so that suitable to be implemented in an embedded hardware device. Structurally, the proposed method consists of five stages: (1) pre-processing, (2) spectral features extraction, (3) sleep stage detection using the Decision-Tree-Based Support Vector Machine (DTB-SVM), (4) assess the sleep quality features, and (5) sleep disorders classification using ensemble of bagged tree classifiers. We evaluate the effectiveness of the proposed method in the task of classifying the sleep disorders into four classes (insomnia, Sleep-Disordered Breathing (SDB), REM Behavior Disorder (RBD), and healthy subjects) from the 51 patients of the Cyclic Alternating Pattern (CAP) sleep data. Based on experimental results, the proposed method presents 84.01% of sensitivity, 94.17% of specificity, 86.27% of overall accuracy, and 0.70 of Cohen’s kappa. This result indicates that the proposed method able to reliably classify the sleep disorders merely using the 30-seconds epoch ECG in order to address the issue of a multichannel signal such as the PSG.

Smart technologies are necessary for ambient assisted living (AAL) to help family members, caregivers, and health-care professionals in providing care for elderly people independently. Among these technologies, the current work is proposed as a computer vision-based solution that can monitor the elderly by recognizing actions using a stereo depth camera. In this work, we introduce a system that fuses together feature extraction methods from previous works in a novel combination of action recognition. Using depth frame sequences provided by the depth camera, the system localizes people by extracting different regions of interest (ROI) from UV-disparity maps. As for feature vectors, the spatial-temporal features of two action representation maps (depth motion appearance (DMA) and depth motion history (DMH) with a histogram of oriented gradients (HOG) descriptor) are used in combination with the distance-based features, and fused together with the automatic rounding method for action recognition of continuous long frame sequences. The experimental results are tested using random frame sequences from a dataset that was collected at an elder care center, demonstrating that the proposed system can detect various actions in real-time with reasonable recognition rates, regardless of the length of the image sequences.

The ${\Theta }^{+}$ was searched for via the $K^{+}p\to {\pi }^{+}X$ reaction using the 1.2 GeV/$c$ $K^{+}$ beam at the K6 beam line of the KEK-PS 12 GeV Proton Synchrotron. In the missing mass spectrum of the $K^{+}p\to {\pi }^{+}X$ reaction, no clear peak structure was observed. Therefore a 90% C.L. upper limit of 3.5 μb/sr was derived for the differential cross section averaged over $2^{°}$ to ${22}^{°}$ in the laboratory frame of the $K^{+}p\to {\pi }^{+}{\Theta }^{+}$ reaction. This upper limit is much smaller than the theoretical calculation for the $t$-channel process where a $K^{0*}$ is exchanged. From the present result, either the $t$-channel process is excluded or the coupling constant of g${}_{K*N\Theta }$ is quite small.

Nature consists of numerous solutions to overcome challenges in designing artificial systems. Various actuation mechanisms have been implemented in microrobots to mimic the motion of microorganisms. Such bio-inspired designs have contributed immensely to microscale developments. Among the actuation mechanisms, magnetic actuation is widely used in bio-inspired microrobotic systems and related propulsion mechanisms used by microrobots to navigate inside a magnetic field and are presented in this review. In addition, the considered robots are in microscale, and they can swim inside a fluidic environment with a low Reynolds number. In relation to microrobotics, mimicry of bacteria flagella, sperm flagella, cilia, and fish are significant. Due to the fact that these biological matters consist of different propulsion mechanisms, the effect of various parameters was investigated in the last decade and the review presents a summary that enhances understanding of the working principle of propulsion mechanisms. In addition, the effect of different parameters on the various speeds of the existing microrobots was analyzed to identify their trends. So, the swimming speeds of the microrobots show an upward trend with increasing body length, frequency, magnetic flux density, and helix angle. Microfabrication techniques play a significant role in the microscale because the device designs are highly dependent on the availability of the techniques. The presented microrobots were manufactured by 3D/4D photolithography and rapid prototyping techniques. Proper materials enable effective fabrication of microrobots using the mentioned techniques. Therefore, magnetically active material types, matrix materials, biocompatible and biodegradable materials are presented in this study. Utilizing biocompatible and biodegradable materials avoids adverse effects to the organs that could occur otherwise. In addition, magnetic field generation is significant for the propulsion of such microrobots. We conclude the review with an overview of the biomimicry of microrobots and magnetically actuated robot propulsion.

Addressing the problems facing the elderly, whether living independently or in managed care facilities, is considered one of the most important applications for action recognition research. However, existing systems are not ready for automation, or for effective use in continuous operation. Therefore, we have developed theoretical and practical foundations for a new real-time action recognition system. This system is based on Hidden Markov Model (HMM) along with colorizing depth maps. The use of depth cameras provides privacy protection. Colorizing depth images in the hue color space enables compressing and visualizing depth data, and detecting persons. The specific detector used for person detection is You Look Only Once (YOLOv5). Appearance and motion features are extracted from depth map sequences and are represented with a Histogram of Oriented Gradients (HOG). These HOG feature vectors are transformed as the observation sequences and then fed into the HMM. Finally, the Viterbi Algorithm is applied to recognize the sequential actions. This system has been tested on real-world data featuring three participants in a care center. We tried out three combinations of HMM with classification algorithms and found that a fusion with Support Vector Machine (SVM) had the best average results, achieving an accuracy rate (84.04%).