Abstract: A computer-implemented method for performing image-based food quantity estimation is provided. The method includes acquiring a first image, when the first image depicts a food item and a body part of a first user. The method further includes detecting, based on the acquired first image, a first physical attribute of the body part of the first user. The method furthermore includes identifying, based on the acquired first image, the depicted food item, and estimating a quantity of the food item depicted in the acquired first image based on the identified food item and the detected first physical attribute of the body part of the user.

Abstract: A method for providing dietary recommendation for a subject includes acquiring a dietary target for the subject, acquiring data associated with dietary consumption of the subject, and determining a balance score based on the acquired dietary target and the acquired data associated with dietary consumption of the subject. The method further includes acquiring a plurality of meal options for the subject, where each of the plurality of meal options is representative of a suggested meal, determining a salience score for each of the plurality of meal options, where the determination is based on the dietary target, dietary information of the respective meal option, and the determined balance score, and generating a personalized dietary recommendation for the subject based on at least one of the determined salience scores.

Abstract: According to an aspect of this disclosure, there is provided an apparatus (42) configured for use with a personal care device (2). The apparatus (42) is configured to provide visual guidance to a user on movement of the personal care device (2) across a body of a subject, wherein the personal care device (2) is configured to perform a personal care operation on a plurality of successive treatment areas of the body by successively moving the personal care device into a plurality of successive treatment positions on the body each corresponding with a respective one of said successive treatment areas. The apparatus (42) comprises a projection unit (22) configured to project a light pattern on to a surface of the body, and a processing unit (46).

Abstract: Typically, high NREM stage N3 sleep detection accuracy is achieved using a frontal electrode referenced to an electrode at a distant location on the head (e.g., the mastoid, or the earlobe). For comfort and design considerations it is more convenient to have active and reference electrodes closely positioned on the frontal region of the head. This configuration, however, significantly attenuates the signal, which degrades sleep stage detection (e.g., N3) performance. The present disclosure describes a deep neural network (DNN) based solution developed to detect sleep using frontal electrodes only. N3 detection is enhanced through post-processing of the soft DNN outputs. Detection of slow-waves and sleep micro-arousals is accomplished using frequency domain thresholds. Volume modulation uses a high-frequency/low-frequency spectral ratio extracted from the frontal signal.

Abstract: The invention relates to a respiratory motion detection apparatus (1) for detecting respiratory motion of a person. An illuminator (3) illuminates the person (2) with an illumination pattern (11), and a detector (4) detects the illumination pattern (11) on the person (2) over time. A temporal respiratory motion signal being indicative of the respiratory motion of the person (2) is determined from the detected illumination pattern by a respiratory motion signal determination unit (5). The illumination pattern deforms significantly with slight movements of the person. Thus, since the respiratory motion signal determination unit (5) is adapted to determine the temporal respiratory motion signal from the detected illumination pattern, even slight respiratory movements of the person can be determined. The sensitivity of detecting respiratory movements of a person can therefore be improved.

Abstract: A method for providing dietary recommendation for a subject includes acquiring a dietary target for the subject, acquiring data associated with dietary consumption of the subject, and determining a balance score based on the acquired dietary target and the acquired data associated with dietary consumption of the subject. The method further includes acquiring a plurality of meal options for the subject, where each of the plurality of meal options is representative of a suggested meal, determining a salience score for each of the plurality of meal options, where the determination is based on the dietary target, dietary information of the respective meal option, and the determined balance score, and generating a personalized dietary recommendation for the subject based on at least one of the determined salience scores.

Abstract: A computer-implemented method for estimating surface area and/or volume of a subject's body or body part. First, at least one image, including the subject's face, is obtained. The image is processed to determine facial image parameter(s) of the subject's face. The subject's characteristic(s) including age, weight, height, and/or gender—is/are also determined. A facial parametric model and the facial image parameters are used to determine facial shape parameters of the subject's face, where the facial parametric model relates specific values for facial image parameters to a respective 3D representation of a face having respective values for the facial shape parameters. A prediction model with the characteristic(s) and the facial shape parameters are used to predict a 3D representation of the subject's full body. The predicted 3D representation of the subject's full body is analyzed to estimate the surface area and/or volume of the subject's body or body part.

Abstract: A computer-implemented method of identifying a subject's body part on which a hand-held device is used to perform a personal care operation. The hand-held device comprises a head portion for contacting the subject's skin one or more sensors for measuring a parameter and generating measurement signals as the hand-held device is used on the subject's skin. The sensor(s) comprise(s) at least one of a skin contact sensor, skin tone sensor, and/or proximity sensor. The method includes obtaining a measurement signal from each sensor; analyzing the obtained measurement signal using a trained machine learning model (MLM) to identify the subject's body part that the hand-held device is used on. The trained MLM analyzes a plurality of measurements in a time window on each of the obtained measurement signals to identify the body part. An indication of the identified body part is then outputted.

Abstract: According to an aspect, there is provided an apparatus for use with a treatment device. The treatment device is configured to apply a light pulse to skin of a subject to perform a treatment operation to hairs on the skin. The apparatus comprises a processing unit configured to receive one or more images of a first area of the skin from an imaging unit, wherein the imaging unit is arranged to obtain images of the skin of the subject; process the one or more images to determine whether the hairs on the first area of the skin have been treated with a light pulse based on a degree of at least one of carbonization and curling of the hairs on the first area of the skin as shown in the one or more images; and output a first signal indicating whether the hairs on the first area of skin have been treated with a light pulse.

Abstract: A computer-implemented method for performing image-based food quantity estimation is provided. The method includes acquiring a first image, when the first image depicts a food item and a body part of a first user. The method further includes detecting, based on the acquired first image, a first physical attribute of the body part of the first user. The method furthermore includes identifying, based on the acquired first image, the depicted food item, and estimating a quantity of the food item depicted in the acquired first image based on the identified food item and the detected first physical attribute of the body part of the user.

Abstract: The present invention relates to a device for detecting motion of a person. The present invention further relates to a system incorporating the aforementioned device and a corresponding method. The proposed device comprises a projection sweep unit configured to obtain image data comprising depth information corresponding to an image showing the patient and to generate one or more projection images of the patient for different viewpoints, a motion detection unit configured to detect motion of the patient in the one or more projection images and to generate one or more motion detection signals indicating if motion is detected or not in a respective projection image, and an analysis unit configured to detect and localize body movements of one or more body parts of the patient based on one or more combinations of motion detection signals.

Abstract: The present disclosure pertains to a system configured to facilitate prediction of a sleep stage and intervention preparation in advance of the sleep stage's occurrence. The system comprises sensors configured to be placed on a subject and to generate output signals conveying information related to brain activity of the subject; and processors configured to: determine a sample representing the output signals with respect to a first time period of a sleep session; provide the sample to a prediction model at a first time of the sleep session to predict a sleep stage of the subject occurring around a second time; determine intervention information based on the prediction of the sleep stage, the intervention information indicating one or more stimulator parameters related to periheral stimulation; and cause one or more stimulators to provide the intervention to the subject around the second time of the sleep session.

Abstract: The invention provides a patient monitoring system for monitoring a patient in a bed. A video camera is used for capturing video images of the patient. Video analysis is used to determine and track the position of body parts of the patient including the hands. This analysis is enhanced by using sensors which detect interaction by the patient with pieces of equipment in the vicinity of the bed.

Abstract: The present disclosure pertains to a system and method for delivering sensory stimulation to a user during a sleep session. The system comprises one or more sensors, one or more sensory stimulators, and one or more hardware processors. The processor(s) are configured to: determine one or more brain activity parameters indicative of sleep depth in the user based on output signals from the sensors; cause a neural network to indicate sleep stages predicted to occur at future times for the user during the sleep session; cause the sensory stimulator(s) to provide the sensory stimulation to the user based on the predicted sleep stages over time during the sleep session, and cause the sensory stimulator(s) to modulate a timing and/or intensity of the sensory stimulation based on the one or more brain activity parameters and values output from one or more intermediate layers of the neural network.

Abstract: There is provided a restraint management apparatus. The restraint management apparatus comprises a processing unit arranged to: receive one or more types of sensor data; determine a status of a subject based on the received sensor data; determine, based on the determined subject status, a restraint parameter for a restraint device configured to restrain a body part of the subject; and output a signal based on the determined restraint parameter.

Abstract: The present invention relates to a device for detecting motion of a person. The present invention further relates to a system incorporating the aforementioned device and a corresponding method. The proposed device comprises a projection sweep unit configured to obtain image data comprising depth information corresponding to an image showing the patient and to generate one or more projection images of the patient for different viewpoints, a motion detection unit configured to detect motion of the patient in the one or more projection images and to generate one or more motion detection signals indicating if motion is detected or not in a respective projection image, and an analysis unit configured to detect and localize body movements of one or more body parts of the patient based on one or more combinations of motion detection signals.

Abstract: Typically, high NREM stage N3 sleep detection accuracy is achieved using a frontal electrode referenced to an electrode at a distant location on the head (e.g., the mastoid, or the earlobe). For comfort and design considerations it is more convenient to have active and reference electrodes closely positioned on the frontal region of the head. This configuration, however, significantly attenuates the signal, which degrades sleep stage detection (e.g., N3) performance. The present disclosure describes a deep neural network (DNN) based solution developed to detect sleep using frontal electrodes only. N3 detection is enhanced through post-processing of the soft DNN outputs. Detection of slow-waves and sleep micro-arousals is accomplished using frequency domain thresholds. Volume modulation uses a high-frequency/low-frequency spectral ratio extracted from the frontal signal.

Abstract: The invention relates to a method and an apparatus for the detection of the body position, especially while sleeping. More particularly, the invention relates to how the main body positions during sleep can be derived from the distribution of the reflection of a projected IR light from a subject's body under a blanket. Additionally, the breathing signals can be analyzed to determine the body posture.

Abstract: There is provided a context detection apparatus for use with a medical monitoring device arranged to measure a physiological characteristic of a subject. The context detection apparatus comprises at least one context sensor comprising a camera; and a processing unit. The processing unit is arranged to receive image data acquired by the camera from the at least one context sensor; analyze the received image data to generate a plurality of different types of context information; wherein each type of context information comprises information which relates to a factor capable of influencing measured values of the physiological characteristic and which is not measurable by the medical monitoring device; and output a signal to the medical monitoring device based on the generated context information.

Abstract: In one embodiment, a wearable device comprising one or more sensors is presented that receives one or more physiological parameters of a user sensed by the one or more sensors and causes adjustment of one or more vehicle parameters based on the one or more physiological parameters.