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.

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.

The current paper proposed and investigated the head motion recognition idea based on four capacitive sensors and deep learning models. The proposed system was designed to empower a tetraplegic person to control a remote device or an intelligent wheelchair. The capacitive sensors were placed around the neck using a necktie, which each volunteer who participated in this experiment was easy to use. The results show that the best-proposed deep learning model can determine each activity with a classification rate equal to 89.29% using capacitive raw data. During the experiments the deep learning models provided accuracy values in the range of 56.25% to 89.29%.