Millimeter (mmWave) mapping systems and methods are disclosed for generating one or more point clouds and determining one or more vital signs for defining a human psychological state. A point cloud comprising point cloud data defining a person or an object detected within a physical space is generated based on one or more mmWave waveforms of an mmWave sensor. A posture of the person within the portion of the physical space is determined from the point cloud data. One or more vital signs of the person is determined based on the mmWave waveform(s). An electronic feedback is provided representing a human psychological state of the person as defined by the point cloud data and the one or more vital signs of the person.

The method of the present invention quantifies the severity of a subject's neurodegenerative disorder. The subject answers a questionnaire which results in a patient-reported outcome dataset. Benchmark tests are carried out by the subject performing one or more tasks resulting in a task result dataset. Continuous sensors collect data resulting in a sensor dataset. Short assessment tests of the subject are conducted resulting in a short assessment dataset. The patient-reported outcome dataset, task result dataset, sensor dataset, and short assessment dataset are aggregated into an output dataset that includes non-motor outcome measures and motor outcome measures. A single score is generated that quantifies the severity of a neurodegenerative disorder of the subject based on the output dataset.

The present invention is directed to a method for determining a paroxysmal slow waves event (PSWE) so as to determine blood-brain barrier dysfunction (BBBD) or increased risk of developing a neurological disease or disorder in a subject.

There is provided a method of training a machine learning model for generating a sleep wellness score used for treatment of a sleep condition in a target individual, comprising: providing a baseline machine learning model with weights set to initial baseline values, accessing sleep-parameters computed for historical sleep sessions of the target individual, training the baseline machine learning model using the sleep-parameters for the historical sleep sessions of the target individual by adjusting the initial baseline values of the weights, to obtain a customized machine learning model, accessing sleep-parameters computed for previous sleep session(s) of the target individual, inputting the sleep-parameters computed for previous sleep session(s) into the customized machine learning model, and obtaining a sleep wellness score as an outcome of the customized machine learning model.

In some implementations, a mobile device can adjust an alarm setting based on the sleep onset latency duration detected for a user of the mobile device. For example, sleep onset latency can be the amount of time it takes for the user to fall asleep after the user attempts to go to sleep (e.g., goes to bed). The mobile device can determine when the user intends or attempts to go to sleep based on detected sleep ritual activities. Sleep ritual activities can include those activities a user performs in preparation for sleep. The mobile device can determine when the user is asleep based on detected sleep signals (e.g., biometric data, sounds, etc.). In some implementations, the mobile device can determine recurring patterns of long or short sleep onset latency and present suggestions that might help the user sleep better or feel more rested.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for geofencing and location tracking for predicting and limiting disease exposure. In some implementations, location tracking data is received indicating locations of user devices over time. Location tags specifying visits of the user devices to different locations indicated by the location tracking data are defined. A geofence is assigned to each of the location tags to specify a geofenced area corresponding to the location tag. Disease transmission scores are assigned to first location tags representing visits of a first user. A disease exposure score is determined for a second user whose user device is determined, based on the location tracking data, to have entered at least one of the geofenced areas corresponding to the first location tags.

A sleep monitoring circuit and a sleep monitoring apparatus are provided, in the circuit: a bidirectional receiving unit includes an electrode pad, and when the electrode pad receives a power supply signal, a handover control unit generates a charging control signal according to the power supply signal, so as to control a charging unit to perform charging management; when the electrode pad receives a bioelectric signal, a command acquisition unit acquires from a user a sleep monitoring command, so as to trigger an enabling unit to generate a monitoring handover signal, the handover control unit outputs a bioelectric signal to a bioelectric signal pick-up unit according to the monitoring handover signal, causing the bioelectric signal pick-up unit to extract feature information from the bioelectric signal and output same to a sleep monitoring unit, and the sleep monitoring unit generates a person sleep monitoring result according to the feature information.

A learning apparatus learns a quality of sleep of a sleeping person. The learning apparatus includes an acquisition unit, a learning unit, and a generation unit. The acquisition unit acquires, as a state variable, a feature quantity related to a core body temperature of the sleeping person or a feature quantity related to a skin temperature of the sleeping person. The learning unit learns the state variable and the quality of sleep in association with each other. The generation unit generates, based on a learning result of the learning unit, a learning model that receives, as an input, the feature quantity related to the core body temperature at sleep onset of the sleeping person. or the feature quantity related to the skin temperature and infers the quality of sleep of the sleeping person at the sleep onset of the sleeping person.

A method and a system for providing feedback to a user for adjusting sleep pattern of the user. The method includes collecting a set of information related to the user, receiving a set of measurement data related to the user from a wearable electronic device, defining circadian rhythm and duration of sleep of the user, determining sleep scores for a predefined number of days and associating each sleep score with a corresponding go-to-bed time or time of falling asleep of the user. A sleep score is determined for each of the predefined number of days from the collected set of information, the set of measurement data, the circadian rhythm and the duration of sleep of the user. The method further includes analysing the sleep scores and associated go-to-bed time or time of falling asleep of the user to determine an optimum bedtime window for the user and providing feedback to the user based on the analysed sleep scores and the optimum bedtime window.

An apparatus for monitoring a sleep parameter of a user includes an adhesive pad configured to conform to a surface of the user and a flexible element coupled to the adhesive pad. The flexible element includes a conductive fabric, and exhibits a modified electrical property in response to an applied force. The apparatus also includes a power source electrically coupled to the flexible element, and an electrical circuit electrically coupled to the power source and the flexible conductive element. The electrical circuit is configured to detect, during use, a change in an electrical property of the flexible element.