It is provided a wearable device for determining when a user has fallen down. The wearable device comprises: a first biometric sensor for obtaining first biometric data of the user, wherein the first biometric sensor is a first accelerometer configured to measure acceleration of a part of a first limb of the user; a second biometric sensor for obtaining second biometric data of the user comprising a finger pressure parameter; and a third biometric sensor for obtaining third biometric data, the third biometric sensor being a second accelerometer configured to measure acceleration of a body part of the user being distinct from the first limb. The wearable device is configured to determine an identity of the user is based on the first biometric data, the second biometric data and the third biometric data, the identity being used to control access to a physical space, and to determine when the user has fallen down.

A method, computer system, and a computer program product for managing a plurality of electronic devices controlling access to one or more hazards in a physical environment. The present invention may include detecting a plurality of brain-wave patterns associated with a user. The present invention may then, in response to detecting motion of the user and the plurality of brain-wave patterns associated with the user matching a pattern, include operating at least one electronic device to disable access to a corresponding hazard. The present invention may further include operating the at least one electronic device to enable access to the corresponding hazard based on receiving input from the user to enable the at least one electronic device, wherein the input being received from the user is in response to prompting the user to correctly respond to a question previously answered by the user.

The invention provides devices that improve tests for detecting specific cellular, viral, and molecular targets in clinical, industrial, or environmental samples. The invention permits efficient detection of individual microscopic targets at low magnification for highly sensitive testing. The invention does not require washing steps and thus allows sensitive and specific detection while simplifying manual operation and lowering costs and complexity in automated operation. In short, the invention provides devices that can deliver rapid, accurate, and quantitative, easy-to-use, and cost-effective tests.

A method, system, apparatus, and/or device that may include: a first sensor operable to take a first physiological measurement; a second sensor operable to take a second physiological measurement; and a processing device operatively coupled to the first sensor and the second sensor. The processing device may be operable to: receive a first measurement data for the first physiological measurement; receive a second measurement data for the second physiological measurement; generate an event data set based on the first measurement data with the second measurement data; determine an event occurred based on the event data set; determine a type of the event; and in response to the event being a safety event: determine a type of the safety event; identify a risk level of the safety event; and in response to the risk level exceeding a threshold level, notify a second device of the safety event.

A method, system, apparatus, and/or device that may include: a first sensor operable to take a first physiological measurement; a second sensor operable to take a second physiological measurement; and a processing device operatively coupled to the first sensor and the second sensor. The processing device may be operable to: receive a first measurement data for the first physiological measurement; receive a second measurement data for the second physiological measurement; generate an event data set based on the first measurement data with the second measurement data; determine an event occurred based on the event data set; determine a type of the event; and in response to the event being a safety event: determine a type of the safety event; identify a risk level of the safety event; and in response to the risk level exceeding a threshold level, notify a second device of the safety event.

A method and system may use machine learning analysis of audio data to automatically identify a user's biometric characteristics. A user's client computing device may capture audio of the user. Feature data may be extracted from the audio and applied to statistical models for determining several biometric characteristics. The determined biometric characteristic values may be used to identify individual health scores and the individual health scores may be combined to generate an overall health score and longevity metric. An indication of the user's biometric characteristics which may include the overall health score and longevity metric may be displayed on the user's client computing device.

Introduced are methods and systems for an adjustable bed device configured to: gather biological signals associated with multiple users, such as heart rate, breathing rate, or temperature; analyze the gathered human biological signals; and heat or cool a bed based on the analysis.

Introduced are methods and systems for an adjustable bed device configured to: gather biological signals associated with multiple users, such as heart rate, breathing rate, or temperature; analyze the gathered human biological signals; and heat or cool a bed based on the analysis.

In each trial, brain electrical activity at multiple points of a target person is measured. An acquirer of a determination device acquires response matrices for n trials under a first condition and response matrices form trials under a second condition. An analyzer performs canonical correlation analysis on the acquired response matrices to obtain first canonical variable time series. A distance calculator calculates a distance between the trials from the obtained first canonical variable time series to obtain a distance matrix. A determiner obtains a possibility that the n trials and the m trials are classified into two different clusters from the distance matrix and determines whether the first condition and the second condition are substantially different. It is possible to provide to a single target person a first content in n trials and a second content in m trials so as to determine a difference in interest of the single target person. It is possible to provide the same content to a first subject who is the target person in n trials and to a second subject who is the target person in m trials so as to determine whether the two are different or the same.

In each trial, brain electrical activity at multiple points of a target person is measured. An acquirer of a determination device acquires response matrices for n trials under a first condition and response matrices form trials under a second condition. An analyzer performs canonical correlation analysis on the acquired response matrices to obtain first canonical variable time series. A distance calculator calculates a distance between the trials from the obtained first canonical variable time series to obtain a distance matrix. A determiner obtains a possibility that the n trials and the m trials are classified into two different clusters from the distance matrix and determines whether the first condition and the second condition are substantially different. It is possible to provide to a single target person a first content in n trials and a second content in m trials so as to determine a difference in interest of the single target person. It is possible to provide the same content to a first subject who is the target person in n trials and to a second subject who is the target person in m trials so as to determine whether the two are different or the same.