A motion-sensing floor mat, an assembly of such floor mats, and a monitoring system with such floor mats are provided; wherein the floor mat can be joined with another such floor mat and electrically connected to a monitoring device to form the monitoring system; the monitoring device stores a queue list and a topology matrix and uses a topological algorithm to store the identification tag of each such floor mat detected into the queue list in order, to gradually establish the topology matrix for the floor mats detected; and to thereby obtain the relative positions of the floor mats detected. When any of the floor mats is subjected to pressure (e.g., when someone falls on the floor mat accidentally) and generates a sensing signal, the monitoring device can pinpoint the position of that floor mat (i.e., the location of the fall) rapidly according to the topology matrix.

A system for detecting and discriminating the severity of a fall includes a mobile device configured to communicate a network, a notification module, and a wearable device configured to communicate with the mobile device, the wearable device including a fall monitor and an activity log resident on the wearable device, where the fall monitor is configured to record detected movement on the activity log. The notification module is configured to effect a selectable setting of a rate of communication between the mobile device and the wearable device based on at least part of a predetermined pattern of a fall discriminator within the activity log, where the selectable setting defines a predetermined period of communication between the mobile device and the wearable device. The fall discriminator is configured to determine when the activity log includes a notable fall event based on the predetermined pattern.

A wearable device and a method of operating the same are provided. The wearable device includes a shoe assembly, a plurality of pressure sensors, a processing circuit and an alarm module. The plurality of pressure sensors are disposed on the shoe assembly and configured to generate a plurality of pressure sensing values. The processing circuit is configured to calculate a center of gravity coordinate according to the plurality of pressure sensing values and coordinates of the plurality of pressure sensors, and generate a determination result according to the center of gravity coordinate. The alarm module is configured to output an alarm signal to perform an alarm function.

Training a machine learning neural network (MLNN) in radiowave based monitoring of fall characteristics in diagnosing injury. The method comprises receiving, in a first set of input layers of the MLNN, from a millimeter wave (mmWave) radar sensing device, a set of mmWave radar point cloud data representing fall attributes associated with a subject, each of the first set associated with a respective fall attribute; receiving, at a second set of input layers of the MLNN, a set of personal attributes of the subject, training a MLNN classifier based on supervised training that establishes a correlation between an injury condition of the subject as generated at the output layer, the mmWave point cloud data, and personal attributes; and adjusting an initial matrix of weights by backpropagation to increase correlation between the injury condition, the mmWave point cloud data, and the personal attributes.

In some examples, a device for detecting seizure events can include a non-transitory machine readable medium storing instructions executable by a processing resource to generate a point cloud of an object, monitor a movement of the generated point cloud based on data received from a sensor, and detect a seizure event based on the monitored movement

A hub device including a housing defining an interior, a plurality of sensors, and a controller module. The plurality of sensors being provided within the interior or along a portion of the housing. The controller module having access to a memory comprising a set of preloaded software and a set of additional software. At least one of least one of the set of preloaded software or the set of additional software is configured to allow the hub device to be communicatively couplable to at least one additional hardware.

Various arrangements for performing fall detection are presented. A smart-home device (110, 201), comprising a monolithic radar integrated circuit (205), may transmit radar waves. Based on reflected radar waves, raw waveform data may be created. The raw waveform data may be processed to determine that a fall by a person (101) has occurred. Speech may then be output announcing that the fall has been detected via the speaker (217) of the smart home device (110, 201).

A water activity board is described, comprising: a. a plurality of sensors, b. a data unit interconnected to the sensors. c. a communication unit interconnected to the data unit. wherein the communication unit is configured to communicate to a cloud-based database. In some embodiments the sensors are selected from a group consisting of Environmental sensors, sea platform sensors and User sensors.

Systems and methods are provided for machine learning based monitoring. Image data from a camera is received. On the hardware accelerator, a person detection model based on the image data is invoked. The person detection model outputs first classification result. Based on the first classification result, a person is detected. Second image data is received from the camera. In response to detecting the person, a fall detection model is invoked on the hardware accelerator based on the second image data. The fall detection model outputs a second classification result. A potential fall based on the second classification result is detected. An alert is provided in response to detecting the potential fall.

Automatically gathering and processing data relating to risk of patient falls. The data is analyzed to determine a level of risk each patient has for falling. Patient risk for falls is stratified and protocols are implemented to mitigate that risk. The system communicates instructions and alerts to caregivers to complete tasks or provide aid to patients. Updates to patient medical information and updates to best practices in fall risk management result in updates to patient risk stratifications. In turn, tasks and alerts are updated to reflect updates to patient risk stratifications.