An overdose of opioids can cause the user to stop breathing, resulting in death. A physiological monitoring system monitors respiration based on oxygen saturation readings from a fingertip pulse oximeter in communication with a smart mobile device and sends opioid monitoring information from the smart mobile device to an opioid overdose monitoring service. The opioid overdose monitoring service notifies a first set of contacts when the opioid monitoring information.

Emotional loneliness is referred as the absence of an attachment figure in one’s life and someone to turn to. The existing methods use installation of sensors for tracking the movement, behaviour and activity of the person, but most of the efforts are obtrusive in nature. A non-obtrusive method and system for detection of emotional loneliness of a person have been provided. The disclosure is utilizing multiple varied techniques to understand the emotional loneliness. The multiple techniques comprise room change movement anomalies, living room stay anomalies, correlating the living room stay with the bedroom stay and outdoor movement anomalies. The methodology also ensures reduced variance and false positives, as emotional loneliness is finally determined based on more than two positives of above methods. The detection of person’s movement is done using a featured engineered dataset based on collection of raw time series data collected from a plurality of motion sensors.

A method and apparatus for monitoring a human or animal subject in a room using video imaging of the subject and analysis of the video image to detect and quantify movement of the subject and to derive an estimate of vital signs such as heart rate or breathing rate. The method includes techniques for de-correlating global intensity variations such as sunlight changes, compensating for noise, eliminating areas not of interest in the image, and quickly and automatically finding regions of interest for detecting subject movement and estimating vital signs. A logic machine is used for interpreting detected movement of the subject, and an artificial neural network is used to calculate a confidence measure for the vital signs estimates from signal quality indices. The confidence measure may be used with a normal density filter to output estimates of the vital signs.

One aspect of the apparatus comprising, a sensor configured to acquire a biological signal of a user, and a controller configured to, determine whether the biological signal is continuously outside a predetermined range for a first time period, after determining that the biological signal has been continuously outside the predetermined range for the first time period, then determine whether the biological signal is inside the predetermined range, and activate an alarm if the controller has determined that (i) the biological signal has been outside the predetermined range for the first time period, and (ii) the biological signal has been continuously inside the predetermined range for a second time period, the second time period being longer than the first time period.

A wearable device and a system that includes the wearable device is provided. The wearable device includes at least one sensor configured to sense a characteristic of a user, a communication interface configured to obtain a first identifier of a vehicle or equipment, and a controller. The controller is configured to monitor the user based on the first identifier or send the first identifier to a monitoring system; determine whether there is a harm possibly occurring to the user of the wearable device based on at least one output from the at least one sensor, or send the at least one output to the monitoring system via the communication interface; and output, after the harm is determined to be possibly occurring, an alarm based on receiving confirmation from the user, or based on not receiving any confirmation from the user within a predetermined amount of time.

A method and apparatus for monitoring a human or animal subject in a room using video imaging of the subject and analysis of the video image to detect and quantify movement of the subject and to derive an estimate of vital signs such as heart rate or breathing rate. The method includes techniques for de-correlating global intensity variations such as sunlight changes, compensating for noise, eliminating areas not of interest in the image, and quickly and automatically finding regions of interest for detecting subject movement and estimating vital signs. A logic machine is used for interpreting detected movement of the subject, and an artificial neural network is used to calculate a confidence measure for the vital signs estimates from signal quality indices. The confidence measure may be used with a normal density filter to output estimates of the vital signs.

The present invention relates to: a device and method for assessing the risk of lonely death using a door open sensor; and a LED system for assessing the risk of lonely death. More specifically, the device for assessing the risk of lonely death using a door open sensor, according to one embodiment of the present invention, comprises: a door open recognition unit which recognizes that a door has been opened; an outside activity time calculation unit which, when the door is recognized to have been opened, and activity of a human body is not detected from the time point when the door is recognized to have been opened to a predetermined time, determines an outside activity state and calculates an outside activity duration which is the time during which the outside activity state is maintained; an inside activity time calculation unit which, when activity of the human body is detected at a first time point, and activity of the human body is detected again at a second time point which includes an idle detection period after the first time point, determines an inside activity state and calculates an inside activity duration which is the time during which the inside activity state is maintained; and a risk assessment unit which assesses the risk of lonely death by generating a probability distribution model on the basis of the outside activity durations and inside activity durations calculated during a first period, and applying, to the probability distribution model, the outside activity duration and inside activity duration calculated after the first period.

An embodiment in accordance with the present invention includes a technology to continuously measure patient mobility automatically, using sensors that capture color and depth images along with algorithms that process the data and analyze the activities of the patients and providers to assess the highest level of mobility of the patient. An algorithm according to the present invention employs the following five steps: 1) analyze individual images to locate the regions containing every person in the scene (Person Localization), 2) for each person region, assign an identity to distinguish ‘patient’ vs. ‘not patient’ (Patient Identification), 3) determine the pose of the patient, with the help of contextual information (Patient Pose Classification and Context Detection), 4) measure the degree of motion of the patient (Motion Analysis), and 5) infer the highest mobility level of the patient using the combination of pose and motion characteristics (Mobility Classification).

A system, method and apparatus for assessment and optional treatment of spatial attention deficit, such as neglect for example, with computational feedback, based upon tracking the movement of the user.

A user, such as an elderly person, may be assisted by an assistance device in his or her caregiving environment that operates in conjunction with one or more server computers. The assistance device may execute a schedule of assistance actions where each assistance action is associated with a time and is executed at that time to assist the user. An assistance action may present an input request to a user, process a voice input of the user, and analyze the voice input to determine that the voice input corresponds to a negative response event, a positive response event, or a non-response event. Based on the categorization of one or more voice inputs as negative response events, positive response events, or non-response events, it may be determined to notify a caregiver of the user, for example where the user has not responded to a number of assistance actions.