A method and system for detecting a person falling, confirming a potential fall, and taking action to mitigate the effects of a fall.

System and method of deploying a trained machine learning neural network (MLNN) in generating a fall injury condition of a subject. The method comprises receiving, at input layers of the trained MLNN, millimeter wave (mmWave) radar point cloud data representing fall attributes from monitoring the subject via mmWave radar sensing device, the input layers associated with the fall attributes, receiving, at a second set of input layers, personal attributes of the subject associated with ones of the second set of input layers, the first and second sets of input layers interconnected with an output layer of the trained MLNN via intermediate layers, the trained MLNN produced by establishing a correlation between an injury condition of prior subjects and mmWave point cloud data and personal attributes associated with the prior subjects, and generating, at the output layer, the fall injury condition attributable to the subject.

A monitoring system includes a sensor data acquisition device configured to acquire information on an activity of a user; a data analyzer configured to analyze the acquired information on the activity of the user and detect occurrence of an abnormality in the activity of the user; an imaging controller configured to instruct an imaging terminal device that captures an image of the activity of the user to start capturing an image of the user and record imaging data in a case where the occurrence of the abnormality is detected by the data analyzer; an imaging data acquisition device configured to acquire the imaging data recorded by the imaging terminal device; a data storage device configured to store the imaging data acquired by the imaging data acquisition device; and an output device configured to output the imaging data.

A wireless, patient-worn, physiological sensor configured to, among other things, help manage a patient that is at risk of forming one or more pressure ulcers is disclosed. According to an embodiment, the sensor includes a base having a top surface and a bottom surface. The sensor also includes a substrate layer including conductive tracks and connection pads, a top side, and a bottom side, where the bottom side of the substrate layer is disposed above the top side of the base. Mounted on the substrate layer are a processor, a data storage device, a wireless transceiver, an accelerometer, and a battery. In use, the sensor senses a patient's motion and wirelessly transmits information indicative of the sensed motion to, for example, a patient monitor. The patient monitor receives, stores, and processes the transmitted information.

In an example method, a mobile device obtains sample data generated by one or more sensors over a period of time, where the one or more sensors are worn by a user. The mobile device determines that the user has fallen based on the sample data, and determines, based on the sample data, a severity of an injury suffered by the user. The mobile device generates one or more notifications based on the determination that the user has fallen and the determined severity of the injury.

A data processing system comprising a processing device configured to collect sets of sensed data representing motion characteristics of a user's gait at a first time and at a second time subsequent to the first time, to process each set of sensed data to form a respective information set dependent on the user's gait at the first and second times, and to compare the first information set with one of (i) the second information set and (ii) a predetermined reference data set in accordance with a predetermined algorithm to identify a condition or change in condition of the user.

A fall detection and identification system includes a tag worn by a user and a server. The tag collects acceleration data from one or more sensors embedded within the tag and transmits the data to the server. The server receives the data from the tag and performs an analysis on the data to detect and identify a fall. The analysis includes determining a change in acceleration of the user over the time window based on the acceleration data; comparing the change in acceleration of the user against one or more thresholds; and determining a status of the user based on the comparison of the change in acceleration of the user against the one or more thresholds.

Provided are systems and methods for improving movement irregularities.

A computer-implemented system and associated methods are disclosed including a device for personalized activity monitoring using the hands The device is worn about a wrist and captures images along the wrist including movement of the hands to monitor predetermined hand movements relative to an object of interest.