No abstract

Sleep disruption from causes, such as changes in lifestyle, stress from aging, family issues, or life pressures are a growing phenomenon that can lead to serious health problems. As such, sleep disorders need to be identified and addressed early on. In recent years, studies have investigated sleep patterns through body movement information collected by wristwatch-type devices or cameras. However, these methods capture only the individual’s awake and sleep states and lack sufficient information to identify specific sleep stages. The aim of this study was to use a 3-axis accelerometer attached to an individual’s head to capture information that can identify three specific sleep stages: rapid eye movement (REM) sleep, light sleep, and deep sleep. These stages are measured by heart rate features captured by a ballistocardiogram and body movement. The sleep experiment was conducted for two nights among eight healthy adult men. According to the leave-one-out cross-validation results, the F-scores were: awake 76.6%, REM sleep 52.7%, light sleep 78.2%, and deep sleep 67.8%. The accuracy was 74.6% for the four estimates. This proposed measurement system was able to estimate the sleep stages with high accuracy simply by using the acceleration in the individual’s head.

No abstract

People sensing occurs everywhere, in the Internet of Things (IoT). Cameras are being increasingly used because they provide inexpensive and effective sensing devices. However, the camera acquires the information that identifies an individual, there is a problem that the privacy of the person is invaded. Furthermore, since home appliances are increasingly being connected to the Internet via the IoT, it has become possible for user images to leak out unintentionally. With these concerns in mind, we propose a face detection method that protects user privacy by using intentionally blurred images. In this method, the presence of a human being is determined by dividing an image into several regions and then calculating the heart rate detected in each region. In our performance evaluation, the proposed method showed dominant performance results when compared with an existing face detection method, and was confirmed to be an effective method for detecting faces in both normal and blurred images. We confirmed the influence of the performance in the proposed method when changing the sharpness of the images. The proposed method also showed high accuracy position detection performance results. Furthermore, we confirmed the effectiveness of the proposed method in near‐infrared images and distorted images.