A fall prevention system includes an image capturing unit that captures an image of a space to be monitored; a skeleton model generating unit that generates a skeleton model representing a person in the image captured by the image capturing unit; a determination unit that determines a state of a person corresponding to the skeleton model generated by the skeleton model generating unit, by distinguishing between the person standing up and the person sitting down, based on the skeleton model; and an operation processing unit capable of executing a fall prevention process that is a process according to a determination result by the determination unit, and that prevents the person from falling over, based on the determination result.

The wake-up detection device may include a sensor configured to detect a movement of a person and a biosignal of the person; and a controller configured to analyze the movement and the biosignal recognized by the sensor, and determine whether the person wakes up from sleep, on the basis of a result of analyzing the movement and the biosignal. The controller may determine a change in a heart rate of the person when the person converts from a sleep state to a non-sleep state, and when it is determined that the change in the heart rate and the movement of the person increase, the controller may output a first alarm.

The purpose of the present invention is to provide a fall risk evaluation system whereby risk of falling of an elderly person or other person to be managed can be easily evaluated on the basis of a captured image of daily life, instead of by a physical therapist, etc. To achieve this purpose, the present invention is a fall risk evaluation system comprising a stereo camera and a fall risk evaluation device, the fall risk evaluation device being provided with: a person authentication unit for authenticating a person to be managed who has been imaged by the stereo camera; a person tracking unit for tracking the person to be managed who is authenticated by the person authentication unit; an action extraction unit for extracting walking by the person to be managed; a feature value calculation unit for calculating a feature value of the walking extracted by the action extraction unit; an integration unit for generating integrated data obtained by integrating the outputs of the person authentication unit, the person tracking unit, the action extraction unit, and the feature value calculation unit; a fall index calculation unit for calculating a fall index value of the person to be managed, on the basis of a plurality of integrated data generated by the integration unit; and a fall risk evaluation unit for comparing the fall index value calculated by the fall index calculation unit and a threshold value to evaluate the risk of falling of the person to be managed.

Various embodiments of a jobsite keep out zone system are provided. The system includes one or more detectors configured to determine when an intruder is within and/or is approaching a protected area. In response to the determination, one or more alarms and/or notifications are generated.

Methods, apparatus and systems for periodic or transient motion detection, e.g. fall event detection, based on wireless signals are described. In one example, a described system comprises: a transmitter configured for transmitting a first wireless signal through a wireless multipath channel of a venue; a receiver configured for receiving a second wireless signal through the wireless multipath channel; and a processor. The second wireless signal differs from the first wireless signal due to the wireless multipath channel that is impacted by a target motion of an object in the venue. The processor is configured for: obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the second wireless signal, computing a time series of spatial-temporal information (STI) of the object based on the TSCI, and detecting the target motion of the object based on the time series of STI (TSSTI).

The present invention relates to a multi-sensing-based vulnerable social group danger recognition detection method of sensing a person being monitored and residence states in real time in a vulnerable social group residence, and, if an analysis result based on the sensed information corresponds to a dangerous situation, transmitting information thereabout to a guardian or related organizations so as to quickly respond thereto. In addition, the present invention is configured to include operations S100 to S800 of receiving sensing information from a sensing means (100) comprised of one or more sensors, analyzing the sensing information, and transmitting, to a central server (300), a dangerous-situation analysis information result of a person being monitored and an alarm signal according thereto.

A method and system for activity classification. A pressure sensor receives input data resulting from physical activity of a subject performing an activity. The input data includes pressure data from at least one pressure sensor, and may include other data acquired through other types of sensors. A deep learning neural network is applied to the input data for identifying the activity. The neural network is trained with reference to training data from a training database. The training data may include empirical data from a database of previous data of corresponding activities, synthesized data prepared from the empirical data or simulated data. The training data may include data from physical activity of the subject being monitored by the system. Different aspects of the neural network may be trained with reference to the training data, and some aspects may be locked or opened depending on the application and the circumstances.

A system for monitoring health and safety of workers in a manufacturing facility is presented. The system includes a user interface, a set of sensors, a database, management software and/or a health monitoring system, among other components. The system is configured to monitor health of workers using biometric data gathered by the set of sensors. In one or more arrangements, the system also tracks the position and motion of the worker. In one or more arrangements, the data gathered is aggregated in a database for datamining purposes so as to facilitate health monitoring and mitigation of identified illness. The system may be configured to screen and identify workers who are not in normal health. The system may be configured to screen workers health when clocking into or out of the time keeping system. The system may be configured to perform contact tracing.

A fall detection system includes a radar that generates emitting radio waves and receives reflected radio waves from a person under detection, a data generator that generates a point cloud according to the reflected radio waves, an area determining device that determines a sub-area of a detecting area in which the person under detection lies, and a classifier that determines whether the person under detection falls according to the point cloud. The classifier adaptively processes the point cloud with different methods according to sub-areas as determined by the area determining device respectively to determine whether the person under detection falls.

According to an aspect, there is provided a computer-implemented method for evaluating movement of a subject. The method comprises obtaining a first signal representing measurements of the subject from a first sensor; processing the first signal to determine a quality measure for the first signal; determining if the determined quality measure meets a first criterion; if the determined quality measure meets the first criterion, determining values for a plurality of features in a first feature set, the first feature set comprising one or more first features to be determined from the first signal, and evaluating the movement of the subject based on the values for the plurality of features in the first feature set; and, if the determined quality measure does not meet the first criterion, determining values for one or more features in a second feature set, wherein the one or more features in the second feature set are a subset of the plurality of features in the first feature set and the second feature set does not include at least one of the one or more first features in the first feature set, and evaluating the movement of the subject based on the values for the one or more features in the second feature set. A corresponding apparatus and computer program product are also provided.