Methods and apparatus for determining biological effects of environmental sounds are disclosed. An example apparatus includes a sound characteristic analyzer to identify a sound event based on audio data in an environment. The example apparatus includes a physiological data analyzer to identify a physiological event based on physiological response data collected from a user exposed to the sound event in the environment. The example apparatus includes a correlation identifier to identify a correlation between the sound event and the physiological event and a report generator to generate a report based on the correlation.

Methods and apparatus for determining biological effects of environmental sounds are disclosed. An example apparatus includes a sound characteristic analyzer to identify a sound event based on audio data in an environment. The example apparatus includes a physiological data analyzer to identify a physiological event based on physiological response data collected from a user exposed to the sound event in the environment. The example apparatus includes a correlation identifier to identify a correlation between the sound event and the physiological event and a report generator to generate a report based on the correlation.

A measurement station includes a base including a measurement plate having an upper surface adapted to receive a user's feet, a handle support, mounted on the upper surface of the measurement plate, a handle adapted to receive a user's hands, the handle being configured to be received on the handle support.

A measurement station includes a base including a measurement plate having an upper surface adapted to receive a user's feet, a handle support, mounted on the upper surface of the measurement plate, a handle adapted to receive a user's hands, the handle being configured to be received on the handle support.

Systems and methods for correlating inputs found external to a user to inputs measured from the user are disclosed, using wearables, various types of non-wearable sensors, and other external data sources and mobile device technology. Pattern matching and rules can be used to provide useful suggestions or control external machines based on the correlated inputs.

An apparatus for estimating bio-information includes: an impedance sensor including a pair of input electrodes and a pair of receiving electrodes, and configured to measure bio-impedance of a user by applying a current to the pair of input electrodes and by measuring a voltage between the pair of receiving electrodes; and a processor configured to estimate bio-information by applying, to the measured bio-impedance, an estimation model that uses a correlation between the measured bio-impedance and the bio-information to be estimated.

Systems and methods for predicting problem behavior in individuals with developmental and behavior disabilities. A plurality of sensors are configured to collect multimodal data signals of a subject individual including a wearable upper body motion sensing device with a plurality of inertial measurement units (IMUs). An electronic controller is configured to receive output signals from each of IMUs and to model an upper body position of the subject individual based on the output signals from the IMUs. A trained machine-learning model is then applied by providing an input data set that includes multimodal signal data (e.g., including signal data from at least one IMU) and/or features extracted from the multimodal signal data. The machine-learning model is trained to produce as output an indication of whether a precursor to the problem behavior is detected and, in response to detecting the precursor, a notification (or alarm) is generated.

Systems and methods for predicting problem behavior in individuals with developmental and behavior disabilities. A plurality of sensors are configured to collect multimodal data signals of a subject individual including a wearable upper body motion sensing device with a plurality of inertial measurement units (IMUs). An electronic controller is configured to receive output signals from each of IMUs and to model an upper body position of the subject individual based on the output signals from the IMUs. A trained machine-learning model is then applied by providing an input data set that includes multimodal signal data (e.g., including signal data from at least one IMU) and/or features extracted from the multimodal signal data. The machine-learning model is trained to produce as output an indication of whether a precursor to the problem behavior is detected and, in response to detecting the precursor, a notification (or alarm) is generated.

A driving assistance system and a driving assistance method are provided. The driving assistance system includes a physiological information sensing system, an external physical symptom detection system, and a processing device. The physiological information sensing system is configured to sense physiological information of a driver. The external physical symptom detection system is configured to detect an external physical symptom of the driver. The processing device is coupled to the physiological information sensing system and the external physical symptom detection system. When the physiological information of the driver and the external physical symptom of the driver are abnormal, the processing device initiates an emergency procedure.

The present disclosure provides systems and methods for detecting and monitoring a patient in need of pressure ulcer treatment based on measurements of Sub-Epidermal Moisture (SEM) values.