Sleepwalking is a type of sleep disorder which originates during deep sleep and results in walking state and performing series of complex behaviors or actions while sleeping. In some cases, sleepwalking patients can injure themselves from their actions such as driving a car or climbing out of a window. In addition, to wake up the sleepwalkers can be difficult. The suddenly waking up and can cause them to be confused or even attack the person who wakes them. Therefore, detecting the sleepwalking incident in an early state can help the caretaker or family members to stop the patients before they harm themselves from any strange, inappropriate, or violent behaviors. In this research, we present a prototype system of sleepwalking detection algorithm and notification system using smart device which work coordinating with wearable device. There are two main groups of users; patients and caretakers. User Activity Sensor (UAS) in the wearable device is utilized for detecting User Activity Data (UAD) which is unusual activities of inducing a sleepwalking patient provided by the Remote Sensor SDK. The system returns the patient UAD states consisting of standing, walking, and running. The smart device accepts the UAD states from the wearable device, performs sleepwalking detection algorithms then, alarms caretakers when the sleepwalking state has already invoked. The system is implemented, built, tested and deployed. The threefold experimental measurement of physical user activites have been performed to validate our proposed sleepwalking detection algorithms. The system correctly detects the sleepwalking states and notifies the caretaker.

<p>Tuberculosis (TB) is still a serious public health concern across the world, causing 1.4 million deaths each year. However, there has been a scarcity of radiological interpretation skills in many TB-infected locations, which may cause poor diagnosis rates and poor patient outcomes. A cost-effective and efficient automated technique might help screening evaluations in underprivileged countries and provide early illness diagnosis. In this work, we proposed a deep ensemble learning framework that integrates multisource data of two deep learning-based techniques for the automated diagnosis of TB. The integrated model framework has been tested on two publicly available datasets and one private dataset. While both proposed deep learning-based automated detection systems have shown high accuracy and specificity compared to state-of-the-art, the en- semble method significantly improved prediction accuracy in detecting chest radiographs with active pulmonary TB from a multi-ethnic patient cohort. Extensive experiments were used to validate the methodology, and the results were superior to previous approaches, showing the method’s practicality for application in the real world. By integrating supervised prediction and unsupervised representation, the ensemble method accu- rately classified TB with the area under the receiver operating characteristic (AUROC) up to 0.98 using chest radiography outperforming the other tested classifiers and achieving state- of-the-art. The methodology and findings provide a viable route for more accurate and quicker TB detection, especially in low and middle-income nations. </p>

<p>Tuberculosis (TB) is still a serious public health concern across the world, causing 1.4 million deaths each year. However, there has been a scarcity of radiological interpretation skills in many TB-infected locations, which may cause poor diagnosis rates and poor patient outcomes. A cost-effective and efficient automated technique might help screening evaluations in underprivileged countries and provide early illness diagnosis. In this work, we proposed a deep ensemble learning framework that integrates multisource data of two deep learning-based techniques for the automated diagnosis of TB. The integrated model framework has been tested on two publicly available datasets and one private dataset. While both proposed deep learning-based automated detection systems have shown high accuracy and specificity compared to state-of-the-art, the en- semble method significantly improved prediction accuracy in detecting chest radiographs with active pulmonary TB from a multi-ethnic patient cohort. Extensive experiments were used to validate the methodology, and the results were superior to previous approaches, showing the method’s practicality for application in the real world. By integrating supervised prediction and unsupervised representation, the ensemble method accu- rately classified TB with the area under the receiver operating characteristic (AUROC) up to 0.98 using chest radiography outperforming the other tested classifiers and achieving state- of-the-art. The methodology and findings provide a viable route for more accurate and quicker TB detection, especially in low and middle-income nations. </p>