Abstract: A health management system using contactless physiological measurement technology is disclosed. The health management system principally comprises a camera and a first processor, of which the camera is faced to a user for capturing a user image. The first processor is particularly configured to have a face detection unit and an activity index calculating unit therein. By such arrangement, after receiving the user image from the camera, the first processor detects a face portion from the user image, thereby subsequently extracting a PPG signal from the face portion. Consequently, after completing at least one process of the PPG signal, multiple indexes for describing a user's health activity are generated. The health activity indexes include health index, activity index, stability index, relaxation index, metabolism index, and balance index. Therefore, the first processor achieves an evaluation of the user's health activity state according to the forgoing health activity indexes.

Abstract: A device of contactless physiological measurement with error compensation function is disclosed, and comprises a camera and a modular electronic device that is embedded with a face detection unit, a physiological parameter estimating unit, a feature extraction unit, an error compensation unit, and a physiological parameter generating unit therein. The face detection unit detects a face region from a user image, such that the physiological parameter estimating unit extracts an rPPG signal from the face region, so as to calculate a preliminary physiological parameter based on the rPPG signal. Simultaneously, the feature extraction unit extracts an error feature from the face region, such that the error compensation unit generates an error compensation parameter based on the error feature and the preliminary physiological parameter.

Abstract: A contactless physiological measurement device is disclosed. The contactless physiological measurement device is an electronic device comprising a processor and a memory storing an application program. When the application program is executed, the processor controls a front camera and a rear camera of the electronic device to photograph a user, so as to obtain a face image and a hand image. Subsequently, after extracting a first rPPG signal and a second rPPG signal from the face image and the hand image respectively, physiological parameters are calculated by apply a signal process to the first/second rPPG signal. Moreover, a signal parameter difference is also acquired after applying a signal difference calculation to the two rPPG signals. Consequently, an estimation value of blood pressure is outputted by inputting user anthropometric parameter, said signal parameter difference and at least one physiological parameter into a pre-trained blood pressure estimating model.

Abstract: The present invention discloses a home infotainment system having function of non-contact imaging-based physiological signal measurement. In the present invention, an image-based physiological signal (IBPS) measuring device is integrated with a television, and is configured for capture at least one image frame of a user who is watching the television. As such, after detecting detects a face portion from the image frame, the IBPS measuring device is able to subsequently extract at least one physiological signal from the face portion. Consequently, after the physiological signal is applied with at least one signal process by a display processor of the television or the IBPS measuring device, physiological indices of the user are hence calculated, and are subsequently applied with data processes of data storing, data transmitting, and/or displaying the physiological indices by a form of graphs, or diagrams, and/or numeric values.

Abstract: A device for liveness detection is disclosed. The liveness detecting device has a simplest structure that principally comprises a light sensing unit and a signal processing module. Particularly, the signal processing module is configured for having a physiological feature extracting unit and a liveness detecting unit therein. The physiological feature extracting unit is adopted for extracting a first physiological feature from a PPG signal, or extracting a second physiological feature from the PPG signal that has been applied with a signal process. As such, through the first and second physiological features, the liveness detecting unit is able to determine whether a subject is a living body or not. The liveness detecting device does not use any camera unit and iPPG technology, such that the liveness detecting device has advantages of simple structure, low cost and immediately completing liveness detection.

Abstract: A biological image processing method is provided. The method acquires first time-frequency data from an image data; processing the first time-frequency data into the second time-frequency data using a filter module; and converting the second time-frequency data into a time domain signal. The filter module is obtained by machine learning, and is trained using a first sample time-frequency signal as input and a second sample time-frequency signal as output, wherein the noise of the time domain signal corresponding to the second sample time-frequency signal is less than the noise of the time domain signal corresponding to the first sample time-frequency signal. A biological information detection device is also provided.

Abstract: A method of liveness detection for a computing device comprises acquiring at least one full cycle of a remote photoplethysmography, rPPG, signal from a skin image, extracting at least one rPPG waveform characteristic from the full cycle of the rPPG signal, and determining whether the skin image includes a life according to the extracted rPPG waveform characteristic.

Abstract: A biological image processing method is provided. The method acquires first time-frequency data from an image data; processing the first time-frequency data into the second time-frequency data using a filter module; and converting the second time-frequency data into a time domain signal. The filter module is obtained by machine learning, and is trained using a first sample time-frequency signal as input and a second sample time-frequency signal as output, wherein the noise of the time domain signal corresponding to the second sample time-frequency signal is less than the noise of the time domain signal corresponding to the first sample time-frequency signal. A biological information detection device is also provided.