Abstract: An image sleep analysis method and system thereof are disclosed. During sleep duration, a plurality of visible-light images of a body are obtained. Positions of image differences are determined by comparing the visible-light images. A plurality of features of the visible-light images are identified and positions of the features are determined. According to the positions of the image differences and features, the motion intensities of the features are determined. Therefore, a variation of the motion intensities is analyzed and recorded to provide accurate sleep quality.

Abstract: A breath detecting system and breath detecting mat thereof are disclosed. The breath detecting mat is placed under bed mattress and has a hollow board, a vibration sensor and a signal processing circuit. The vibration sensor and the signal processing circuit are mounted in the hollow board. The vibration sensor senses the micro-vibrations caused by the breathing of the person is lying on the bed mattress and outputs the breath sensing signal to the signal processing circuit. The signal processing circuit samples the sensing signal according to different moving average points to generate the fast-moving and slow-moving average signals. Since the first fast-moving and slow-moving average signals have many cross points, the signal processing circuit calculates each time difference between every two adjacent cross points. A present breath frequency is calculated according to the time differences. Therefore, the noises of the sensing signal are effectively removed.

Abstract: An image detection method for selecting a representative image of a user is provided. In the image detection method, a plurality of images of the user are obtained, and a plurality of feature parameters of the plurality of images are obtained. A face occlusion analysis is performed on the plurality of images according to the plurality of feature parameters to determine whether the plurality of images clearly show the user's face. A plurality of feature vectors are determined, and a body distribution analysis is performed on the plurality of images according to the plurality of feature vectors to determine a body position and a position type of the user. An image related to the position type is selected according to results of the face occlusion analysis and the body distribution analysis.

Abstract: An image sleep analysis method and system thereof are disclosed. During sleep duration, a plurality of visible-light images of a body are obtained. Positions of image differences are determined by comparing the visible-light images. A plurality of features of the visible-light images are identified and positions of the features are determined. According to the positions of the image differences and features, the motion intensities of the features are determined. Therefore, a variation of the motion intensities is analyzed and recorded to provide accurate sleep quality.

Abstract: An image detection method is provided. In the image detection method, images of a user are obtained, feature parameters are marked in the images, and detection results of the feature parameters in each of the images are evaluated. A body distribution analysis is performed on the images according to the detection result of at least one first feature parameter among the feature parameters to determine first position information of the user. A face occlusion analysis is performed on the images according to the detection result of at least one second feature parameter among the feature parameters and the first position information to determine second position information of the user. The at least one second feature parameter is different from the at least one first feature parameter. The second position information represents a position of the user.

Abstract: An image detection method for determining positions of a user. According to the image detection method, a plurality of images of the user are obtained, whether the user moves is determined according to the images, a plurality of feature parameters of the plurality of images are obtained, and a body distribution analysis and a face occlusion analysis are performed to determine the position of the user.

Abstract: An image detection method for determining positions of a user. According to the image detection method, a plurality of images of the user are obtained, whether the user moves is determined according to the images, a plurality of feature parameters of the plurality of images are obtained, and a body distribution analysis and a face occlusion analysis are performed to determine the position of the user.

Abstract: An image detection method is provided. In the image detection method, images of a user are obtained, feature parameters are marked in the images, and detection results of the feature parameters in each of the images are evaluated. A body distribution analysis is performed on the images according to the detection result of at least one first feature parameter among the feature parameters to determine first position information of the user. A face occlusion analysis is performed on the images according to the detection result of at least one second feature parameter among the feature parameters and the first position information to determine second position information of the user. The at least one second feature parameter is different from the at least one first feature parameter. The second position information represents a position of the user.

Abstract: An image detection method for selecting a representative image of a user is provided. In the image detection method, a plurality of images of the user are obtained, and a plurality of feature parameters of the plurality of images are obtained. A face occlusion analysis is performed on the plurality of images according to the plurality of feature parameters to determine whether the plurality of images clearly show the user's face. A plurality of feature vectors are determined, and a body distribution analysis is performed on the plurality of images according to the plurality of feature vectors to determine a body position and a position type of the user. An image related to the position type is selected according to results of the face occlusion analysis and the body distribution analysis.

Abstract: First and second positioning devices disposed at first and second stationary locations transmit first and second pilot signals, respectively. Transmission coverages of the first and second pilot signals have an area of overlap. When a mobile robot moves to the area of overlap, the mobile robot determines first angular orientation information between the mobile robot and the first positioning device, and second angular orientation information between the mobile robot and the second positioning device. The mobile robot then determines an initial position of the mobile robot based on the first stationary location, the second stationary location, the first angular orientation information, and the second angular orientation information.

Abstract: First and second positioning devices disposed at first and second stationary locations transmit first and second pilot signals, respectively. Transmission coverages of the first and second pilot signals have an area of overlap. When a mobile robot moves to the area of overlap, the mobile robot determines first angular orientation information between the mobile robot and the first positioning device, and second angular orientation information between the mobile robot and the second positioning device. The mobile robot then determines an initial position of the mobile robot based on the first stationary location, the second stationary location, the first angular orientation information, and the second angular orientation information.