Open Access

The class of square matrices of order nhaving a positive determinant and all their minors up to order n−1nonpositive is considered. A characterization of these matrices based on the Cauchon algorithm is presented, which provides an easy test for their recognition. Furthermore, it is shown that all matrices lying between two matrices of this class with respect to the checkerboard ordering are contained in this class, too. For both results, we require that the entry in the bottom-right position is negative.

Sufficient and regular sleep is essential for people’s physical and psychological health and a good life quality. Insomnia is one of the most frequent sleep disorders worldwide. Patients with a significant leak of sleep can also have a higher risk for depression. The main goal of this research was to create the concept of a software solution based on “Cognitive behavior therapy for insomnia” or short CBT-I, to support patients to enhance their sleep quality. But also, to make the monitoring of multiple patients more efficient and accessible, for the therapist’s side. Methods that can be quantified and are subsequently easy to automate were transferred, form CBT-I. One of the most essential components of this concept is the sleep diary, being used for the collection of the data for further processing. The PSQI questionnaire with an evaluation function was also included because it is an important tool for the therapist side. To guarantee access to the data and results for both sides, the data are stored on an online database. An internal test with 3 users confirmed a good user experience and has shown that the implementation of a user-friendly CBT-I based software solution for the simultaneous use is realizable and can provide benefits for patients and therapists. Like better monitoring and the automation of numerous proven CBT-I methods, e.g. sleep restriction or the transfer of CBT-I knowledge, making a considerable part of the process more efficient. This increase in efficiency can enable therapists to treat more patients simultaneously, while maintaining the same level of quality.

Sleep is a crucial aspect of human well-being, with significant implications for overall health and quality of life. In response to the growing concern over sleep-related issues and the need for innovative solutions, this paper presents ‘Sleep Sheep’, an innovative system designed to monitor sleep and promote healthy sleep habits. The motivation behind Sleep Sheep stems from recognizing the vital role sleep plays in our daily lives. Inadequate sleep has been associated with various health problems, including cognitive impairments, mood disorders, and compromised immune function. Thus, addressing sleep-related concerns has become a pressing priority. To achieve its objectives, Sleep Sheep utilizes a smartphone application monitored by a doctor, to collect and analyze comprehensive sleep data, including sleep duration, sleep stages, and sleep disturbances. The collected data is then processed using several algorithms to provide results, which demonstrate its potential to impact sleep quality and overall well-being. By providing users with personalized sleep reports and actionable recommendations, Sleep Sheep makes individuals aware that they should adopt healthier sleep practices. These reports and recommendations, in turn, can lead to improved sleep duration, enhanced sleep efficiency, and a better overall sleep experience. By fostering awareness about the importance of sleep and providing individuals with the tools for being monitored by their doctors and improving their sleep, Sleep Sheep has the potential to make a substantial impact on public health. Ultimately, this innovative system aims to contribute to the well-being and quality of life of individuals by encouraging healthy sleep habits and optimizing sleep outcomes.

Paying attention helps us learn, advance in our careers, and build successful relationships, but when it’s compromised, achievement of any kind becomes far more challenging. Causes of not paying attention can range from common factors like sleep deprivation, stress, or a mood disorder to health difficulties such as ADHD, OCD, or a thyroid problem that affects concentrating. This work extracts paying attention and not paying attention behavior patterns in the context of learning. In early work, our study identified attention and distraction behaviors using gathered video recordings of online classes. The work found ten paying attention behaviors and six distracted behavior patterns. In this paper, we use computer vision techniques to extract features related to these behaviors. These features are distance between hand and face, pitch yaw roll, eye-to-camera distance, hand-to-camera distance, iris direction, gaze tracking, mouth aspect ratio, eye aspect ratio, distance between face and frame side, and facial landmark configuration. This research also applied three types of machine learning—logistic regression, decision trees, and random forest—and the accuracy rates were 79%, 86%, and 89%, respectively. This result is better than relying only on two extracted features in our previous work.

With the emergence of new sensor technologies, such as fiber optic sensors (FOSs), compared to traditional mechanical sensors, unobtrusive sleep monitoring has been a research focus for decades. This work aims to provide a guide to current bed-based sensor technologies with diverse applications in various settings. We conducted a retrospective literature review, summarizing the state-of-the-art research over the past decade on non-contact bed-based sensor technology in sleep monitoring. We developed a three-category terminology: unobtrusive sensor technology, application, and subject. A total of 263 unique articles were acquired from three databases and screened for relevance, resulting in 21 papers selected for in-depth analysis. The findings revealed eight types of sensors: six mechanical sensors (pressure, accelerometer, piezoelectric, load cell, electromechanical film (EMFI), and hydraulic) and two FOSs (fiber Bragg grating and microbend FOS) that are integrated with or positioned under the bed at three levels of unobtrusiveness. We identified 15 parameters, with heart rate (HR) (14) and respiratory rate (RR) (13) being the most frequently measured. These parameters are generally categorized into three applications: disease-related diagnosis (18), general sleep analysis (9), and general well-being (11). The results indicated that sleep apnea (5) and insomnia (2) were the most frequently detected sleep disorders. Additionally, 59.1% (13) of the systems were tested in a lab environment, with only one undergoing clinical trials. In summary, there is a clear lack of convincing proof of the systems’ effectiveness in continuous in-home sleep monitoring.