Human activity recognition and classification are some of the most interesting research fields, especially due to the rising popularity of wearable devices, such as mobile phones and smartwatches, which are present in our daily lives. Determining head motion and activities through wearable devices has applications in different domains, such as medicine, entertainment, health monitoring, and sports training. In addition, understanding head motion is important for modern-day topics, such as metaverse systems, virtual reality, and touchless systems. The wearability and usability of head motion systems are more technologically advanced than those which use information from a sensor connected to other parts of the human body. The current paper presents an overview of the technical literature from the last decade on state-of-the-art head motion monitoring systems based on inertial sensors. This study provides an overview of the existing solutions used to monitor head motion using inertial sensors. The focus of this study was on determining the acquisition methods, prototype structures, preprocessing steps, computational methods, and techniques used to validate these systems. From a preliminary inspection of the technical literature, we observed that this was the first work which looks specifically at head motion systems based on inertial sensors and their techniques. The research was conducted using four internet databases—IEEE Xplore, Elsevier, MDPI, and Springer. According to this survey, most of the studies focused on analyzing general human activity, and less on a specific activity. In addition, this paper provides a thorough overview of the last decade of approaches and machine learning algorithms used to monitor head motion using inertial sensors. For each method, concept, and final solution, this study provides a comprehensive number of references which help prove the advantages and disadvantages of the inertial sensors used to read head motion. The results of this study help to contextualize emerging inertial sensor technology in relation to broader goals to help people suffering from partial or total paralysis of the body.

Several topologies of three-phase low-harmonic diode rectifiers equipped with inductors, capacitors and diodes, based on an original solution, are presented. Inductors and capacitors are used in conjunction with the three-phase diode rectifier bridge to improve the waveform of the currents drawn from the utility grid. For the basic variant of the three-phase rectifier, representative characteristics and design elements are presented. Starting from this configuration, an AC/DC converter with small holding current and a four-quadrant frequency converter are proposed. The operation of the proposed converters is analysed and, on this basis, design considerations are reviewed. Several possible applications of the converter variants are mentioned. Analytically obtained results have bee experimentally verified.

The recognition of the head movements is a challenging task in the Human-Computer Interface domain. The medical, automotive, or computer games domains are only several fields where this task can find practical applicabilities. Currently, the head movement recognition is performed using complex systems based on video information or using an IMU sensor with nine freedom degrees. In this paper, we describe a new approach for recognizing head movements using a new type of IMU sensor with six freedom degrees placed on top of a headphone pair. The system aims to provide an easy control method for people suffering from tetraplegia for a specific set of activities. The system collects data from the inertial sensor placed on top of the headphone to analyze and then extract the features for head movement recognition. We did construct and evaluated eight predictive models of classifying head movements activity to determine which one is the best fit for the proposed head movement recognition system. The comparison and performance evaluation provided by each predictive model lead to demonstrate the performances delivered by our new system for head movements recognition.

While in brain computer interface (BCI) field the research is focused basically on finding improved processing methods leading to both high classification rates and high bit transfer rates, in this paper the same BCIs performances are addressed but, this time, with the emphasis set on the subject-specific discriminative cognitive tasks selection process. In this respect, a set of twelve electroencephalographic (EEG)-discriminative mental tasks was proposed to be studied in conjunction with four different subjects. For each subject, a particular set of four mental tasks was selected. The classification performances corresponding to these particular sets of tasks were obtained using some standard processing methods (i.e., the autoregressive model of the EEG signals and a multilayer perceptron classifier trained with back-propagation algorithm). The superior classification rates achieved for the selected sets compared to other set of mental tasks commonly used in the 4-class BCI studies (i.e. the set proposed by Keirn and Aunon) promote the idea of subject-oriented mental tasks selection process as a necessary preliminary step in any high-quality BCI application.

The aim of this paper is to build a system for human detection based on facial recognition. The state-of-the-art face recognition algorithms obtain high recognition rates base on demanding costs - computational, energy and memory. The use of these classical algorithms on an embedded system cannot achieve such performances due to the existing constrains: computational power and memory. Our objective is to develop a cheap, real time embedded system able to recognize faces without any compromise on system's accuracy. The system is designed for automotive industry, smart house application and security systems. To achieve superior performance (higher recognition rates) in real time, an optimum combination of new technologies was used for detection and classification of faces. The face detection system uses skeletal-tracking feature of Microsoft Kinect sensor. The face recognition, more precisely - the training of neural network, the most computing-intensive part of the software, is achieved based on the Windows Azures cloud technology.

This paper proposes and evaluates a new simple method of recognizing head movements in order to empower a tetraplegic to easily control his wheelchair. The input for the recognition system is one based on the capacitive signals acquired from a wearable system consisting of four capacitive sensors embedded in a necktie. The proposed non-contact wearable system is a very simple and cheap one, being accessible, and, above all, it is very easy to use. All processing and analyses were done offline and the obtained competitive results make our approach a very promising and feasible solution from both, economic and constructive point of view. A key advantage of the system resides in its high simplicity which considerably reduces the computational burden, making it suitable for real-time processing. Also, due to the capacitive sensors' characteristics and their arrangement, the head gesture recognition process becomes a straight one, leading to very high recognition accuracy.