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In the colloquy concerning human rights, equality, and human health, mental illness and therapy regarding mental health have been condoned. Mental disorder is a behavioral motif that catalyzes the significant anguish or affliction of personal functioning. The symptoms of a mental disorder may be tenacious, degenerative, or transpire as a single episode. Brain sickness is often interpreted as a combination of how a person thinks, perceives, contemplates and reacts. This may be analogous to a specific region or workings of the brain frequently in a social context. Anxiety disorders, psychotic disorders, personality disorders, mood disorders, eating disorders, and many more are examples of mental disorders, while complications include social problems, suicides, and cognitive impairment. These days, mental disorders are quotidian worldwide, and clinically consequential levels of derangement rise adversely. The purpose of this paper is to aid in prognosis of the type of mental disorder by analyzing the brainwaves such as Alpha ($\alpha$), Beta ($\beta$), Gamma ($\gamma$), Theta ($𝜃$), Delta ($\delta$) with the help of big data analysis and the Internet of Medical Things (IoMT). IoMT helps in gathering the required data and data transmission, while big data analysis helps in predicting the type of disorder.

The dynamic pull-in instability of a microstructure is a vast research field and its analysis is of great significance for ensuring the effective operation and reliability of micro-electromechanical systems (MEMS). A fractal modification for the traditional MEMS system is suggested to be closer to the real state as a practical application in the air with impurities or humidity. In this paper, we establish a fractal model for a class of electrostatically driven microstructure resonant sensors and find the phenomenon of pull-in instability caused by DC bias voltage and AC excitation voltage. The variational iteration method has been extended to obtain approximate analytical solutions and the pull-in threshold value for the fractal MEMS system. The result obtained from this method shows good agreement with the numerical solution. The simple and efficient operability is demonstrated through theoretical analysis and results comparisons.

In recent years, a robust increasing interest has been observed in wearable devices featuring smart health, smart fitness, and human–machine interaction applications. While we gained some advances on use of surface electromyography (sEMG) signals recorded from upper extremities for controlling external devices, only limited attempt has been made to track the status of targeted muscles and forecast muscle fatigue onset. In this study, we address use of sEMG signals acquired from upper extremities to predict onset of muscle fatigue using deep belief networks (DBNs) as a learning mechanism. We demonstrate that a deep architecture can learn from raw data and provide comparable performance to feature-based approaches. Experimental results show that the DBNs model investigated in this study achieves an average classification accuracy of 85.3% without any subject-oriented calibration and achieves a best case accuracy of 97.60%. A transient-to-fatigue state is introduced before the fatigue onsets as an early warning state. The aim of this paper is to evaluate the performance of the popular deep models in real fatigue detection applications. The model provides a promising result compared with state-of-art works without any feature selection process, which could potentially generate better features while reducing the requirement for expertise in data.

Monitoring human gait is essential to quantify gait issues associated with fall-prone individuals as well as other gait-related movement disorders. Being portable and cost-effective, ambulatory gait analysis using inertial sensors is considered a promising alternative to traditional laboratory-based approach. The current study aimed to provide a method for predicting the spatio-temporal gait parameters using the wrist-worn inertial sensors. Eight young adults were involved in a laboratory study. Optical motion analysis system and force-plates were used for the assessment of baseline gait parameters. Spatio-temporal features of an Inertial Measurement Unit (IMU) on the wrist were analyzed. Multi-variate correlation analyses were performed to develop gait parameter prediction models. The results indicated that gait stride time was strongly correlated with peak-to-peak duration of wrist gyroscope signal in the anterio-posterior direction. Meanwhile, gait stride length was successfully predicted using a combination model of peak resultant wrist acceleration and peak sagittal wrist angle. In conclusion, current study provided the evidence that the wrist-worn inertial sensors are capable of estimating spatio-temporal gait parameters. This finding paves the foundation for developing a wrist-worn gait monitor with high user compliance.

Evaluation of kinetic asymmetry during walking has been important to both researchers and clinicians. Wearable devices such as accelerometers are inexpensive, easily accessible tools provide valuable information in gait analysis and offer the potential to assess asymmetry without restriction to cost-ineffective laboratory settings. The aim of this study is to investigate the feasibility of using an accelerometer in assessment of force asymmetry in gait. To this end, the relationship between asymmetry measured from force platforms and a skin-mounted accelerometer on the lower back was studied during normal walking as well as five different levels of self-induced simulated asymmetry. Results show that there is a positive overall correlation between the asymmetry indices measured by the two methods ($r=0.73$, $p<0.0001$). Future study is needed to investigate factors such as age, gender, and anthropometric properties that can help develop a predictive model.

Raising children is challenging and requires lots of care. Parents always have to provide proper care to their children in time, like hydration and clothing. However, it is difficult to always stay alert or be aware of the care required at proper moments. One reason is that parents nowadays are busy. This especially applies to single parent, or the one who needs to raise multiple children.

This paper presents the use of an integrated multi-sensors together with a mobile application to help keep track of unusual situations concerning a child. By monitoring the changes in surrounding temperature, motions, and air pressure acquired from the sensors, our mobile application can infer the physiological needs of the children with the heat equilibrium assumption. As the thermal environment in the human body is mainly governed by the heat balance equation, we fuse all available sensor readings to the equation so as to estimate the change in situation of a child over a certain period of time. Our system can then notify the parents of the necessary care, including hydration, dining, clothing and ear barotrauma relieving. The proposed application can greatly relieve some of the mental load and pressure of the parents in taking care of children.

Unintentional falls cause serious health problem and high medical cost, particularly among the elders. Efficient fall detection can ensure fallen subjects with timely rescue, less pain and lower health-care expense. However, the accuracy of the present fall detection system with single accelerometer does not meet the requirement of practical application. In this paper, a fall detection method using three wearable triaxial accelerometers and a decision-tree classifier is proposed. The three triaxial accelerometers are, respectively mounted on the head, the waist and the ankle to capture the acceleration signals of human movement. A Kalman filter is adopted to estimate the body tilt angle. After the features are extracted, the trained decision-tree model is used to predict the fall. The efficiency improvement is evidenced by the scripted and unscripted lateral fall experiments, involving five young healthy volunteers (three males and two females; age: 23.3 ± 1 years). The classification of fall and activities of daily living (ADL) achieve recall, precision and F-value of 93.1%, 95.9%, and 94.5%, respectively, and the system detects all falls during the extended unscripted trials. The experimental results indicate that the complementary movement information coming from three accelerometers can enhance the performance of fall detection. The proposed method is efficient, and it has remarkable improvements in comparison to the method of using one or two accelerometers.

Despite the success of previous research in emotion recognition using electroen-cephalogram (EEG), the traditional EEG devices used in those research had limited practicability to be employed in a naturalistic and real-world situation. Accordingly, a new wearable EEG mounted with dry electrodes has been recently developed, yet the feasibility to use it for emotion detection has not been confirmed. In this work, we present a preliminary study of emotion recognition in music listening using the new EEG device. Spectral features were extracted from the selected six electrodes (Fp1, Fp2, C1, C2, T3, and T4) of the EEG and the support vector machine was employed to classify binary classes of arousal and valence. Our empirical results demonstrate the promise of using the new EEG to recognize emotional states of a human during music listening.