Technologies and implementations for a wearable healthcare system, which may be worn by a person. The wearable healthcare systems may include one or more motion sensors. A motion analysis modules may be included in the wearable healthcare system, which may be configured to determine physical activities and intensity of the physical activities of the person.

Embodiments herein determine when to place a passive assistive call using personal artificial intelligence (AI) assistants. The present embodiments improve upon the base functionalities of the assistant devices by monitoring the usually discarded or filtered-out environmental sounds to identify when a person is in distress to automatically issue an assistive call in addition to or alternatively to monitoring user speech for active commands to place assistive calls. The assistant device may be in communication with various other sensors to enhance or supplement the audio assessment of the persons in the environment, and may be used in a variety of scenarios where prior call systems struggled to quickly and accurately identify distress in various monitored persons (e.g., patients) including falls, stroke onset, and choking.

A gait evaluation apparatus that evaluates a training gait of a paralyzed patient suffering from paralysis in a leg includes an acquisition unit configured to acquire a plurality of motion amounts of a paralyzed body portion according to a gait motion and an evaluation unit configured to evaluate that the gait motion is an abnormal gait in a case where at least one of the motion amounts acquired by the acquisition unit meets any one of a plurality of abnormal gait criteria set in advance. The abnormal gait criteria include at least two or more first criteria, which are criteria relevant to motion amounts of different parts of the paralyzed body portion, or at least two or more second criteria, which are criteria relevant to motion amounts of the same part of the paralyzed body portion in different directions.

A mount for a device configured to monitor the movements or other activities of patient. Aspects include a monitoring unit and base, where the base may further include a pad with one or more pins extending into the base. The pad may be positioned inside a garment worn by a patient, the pins passing through the garment and electrically connecting to circuits in the fabric of the garment (e.g. a sock worn by the patient). The circuits may include sensors which are response to changes in pressure caused by patient movement. Output from the sensors may be carried by the circuits in the garment to the pins in the pad, and from there through the garment and into the base and the monitoring unit for processing and reporting to caregivers as needed.

Systems, methods and media are disclosed for identifying the crossing of a virtual barrier. A person in a 3D image of a room may be circumscribed by a bounding box. The position of the bounding box may be monitored over time, relative to the virtual barrier. If the bounding box touches or crosses the virtual barrier, an alert may be sent to the person being monitored, a caregiver or a clinician. Bounding box tracking may be used in addition to or instead of an initial tracking process, such as skeletal tracking.

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.

A system for contactless monitoring of a person in a chair includes a detector mount positioned adjacent the chair and detached from the chair. A detector is coupled to the detector mount and is configured to detect thermal radiation from a field of view that includes the chair. A controller controls the detector. The controller includes a processor and a nontransitory memory device that includes instructions that are performed by the processor to control the detector.

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 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.

The present disclosure generally relates to user interfaces for managing walking steadiness data. In some embodiments, a walking steadiness state is determined based on detected movement. When a first set of conditions are met, a notification is displayed. When a second set of conditions is met, the notification is not displayed.