Many children with attention deficit hyperactivity disorder (ADHD) perform poorly in their academic studies. They also have difficulties in their social lives due to a lack of interpersonal skills and this often continues into adult life. Appropriate early therapies and medications can be very beneficial. In this paper, we introduce and demonstrate the benefits of a new type of treatment, namely, an eye-contact game which successfully exploits mixed reality technology. None of the patients in our experiment were older than ten years of age. They were able to pay attention and sustain interest in the treatment sessions over a span of six weeks. After participating in the treatment sessions with our game, the omission/commission errors which were evaluated in an attention test taken by the experimental group decreased significantly and appeared within the normal range. In addition to the improvement in error rates, the mean response time to an interactive metronome test significantly decreased. Importantly, our game allows patients to conduct this treatment by themselves at home without professional assistance. To the best of our knowledge, this study is one of the first studies to use a mixed reality head-mounted display to treat children with attention deficit hyperactivity disorder and to prove its potential as a treatment for clinically diagnosed children.
Pose estimation is a significant strategy that has been actively researched in various fields. For example, the strategy has been adopted for motion capture in moviemaking, and character control in video games. It can also be applied to implement the user interfaces of mobile devices through human poses. Therefore, this paper compares and analyzes four popular pose estimation models, namely, OpenPose, PoseNet, MoveNet Lightning, and MoveNet Thunder, using pre-classified images. The results show that MoveNet Lightning was the fastest, and OpenPose was the slowest among the four models. But OpenPose was the only model capable of estimating the poses of multiple persons. The accuracies of OpenPose, PoseNet, MoveNet Lightning, and MoveNet Thunder were 86.2%, 97.6%, 75.1%, and 80.6%, respectively.
Wireless sensor networks (WSNs) have recently been viewed as the basic architecture that prepared the way for the Internet of Things (IoT) to arise. Nevertheless, when WSNs are linked with the IoT, a difficult issue arises due to excessive energy utilization in their nodes and short network longevity. As a result, energy constraints in sensor nodes, sensor data sharing and routing protocols are the fundamental topics in WSN. This research presents an enhanced smart-energy-efficient routing protocol (ESEERP) technique that extends the lifetime of the network and improves its connection to meet the aforementioned deficiencies. It selects the Cluster Head (CH) depending on an efficient optimization method derived from several purposes. It aids in the reduction of sleepy sensor nodes and decreases energy utilization. A Sail Fish Optimizer (SFO) is used to find an appropriate route to the sink node for data transfer following CH selection. Regarding energy utilization, bandwidth, packet delivery ratio and network longevity, the proposed methodology is mathematically studied, and the results have been compared to identical current approaches such as a Genetic algorithm (GA), Ant Lion optimization (ALO) and Particle Swarm Optimization (PSO). The simulation shows that in the proposed approach for the longevity of the network, there are 3500 rounds; energy utilization achieves a maximum of 0.5 Joules; bandwidth transmits the data at the rate of 0.52 MBPS; the packet delivery ratio (PDR) is at the rate of 96% for 500 nodes, respectively.
The human body is designed to experience stress and react to it, and experiencing challenges causes our body to produce physical and mental responses and also helps our body to adjust to new situations. However, stress becomes a problem when it continues to remain without a period of relaxation or relief. When a person has long-term stress, continued activation of the stress response causes wear and tear on the body. Chronic stress results in cancer, cardiovascular disease, depression, and diabetes, and thus is deeply detrimental to our health. Previous researchers have performed a lot of work regarding mental stress, using mainly machine-learning-based approaches. However, most of the methods have used raw, unprocessed data, which cause more errors and thereby affect the overall model performance. Moreover, corrupt data values are very common, especially for wearable sensor datasets, which may also lead to poor performance in this regard. This paper introduces a deep-learning-based method for mental stress detection by encoding time series raw data into Gramian Angular Field images, which results in promising accuracy while detecting the stress levels of an individual. The experiment has been conducted on two standard benchmark datasets, namely WESAD (wearable stress and affect detection) and SWELL. During the studies, testing accuracies of 94.8% and 99.39% are achieved for the WESAD and SWELL datasets, respectively. For the WESAD dataset, chest data are taken for the experiment, including the data of sensor modalities such as three-axis acceleration (ACC), electrocardiogram (ECG), body temperature (TEMP), respiration (RESP), etc.
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