In one embodiment, a method (110) that determines conditions including that a user is located in a monitored area and not wearing a wearable device (112), and provides an alert based on the determinations and an input pattern from one or plural sensors (114).

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.

The disclosed wearable device systems include several features for alerting and guiding persons who are approaching or near to potentially hazardous or high-risk conditions in their nearby environment. Sensor data from wearable devices (also referred to herein as “wearables”) are used to determine the presence of various unsafe environmental conditions and phenomenon, including dangerous terrain or other unusual conditions. The wearable would be used to warn a person, for example via auditory or haptic-based feedback, if the person is about to encounter an unsafe condition. In particular, the proposed systems can be of great benefit to the visually impaired, those persons with physical disabilities, or persons otherwise vulnerable to particular environmental conditions.

An electronic monitoring system implements an evaluation strategy to distinguish between low-interest detected events and high-interest detected events and to send lower or standard alerts to a user device if a detected event is a low-interest event and a heightened alert or warning if the detected event is a high-interest event. The system may utilize patterns of information to establish baseline event characteristics for the particular monitored environment. The baseline event characteristics are used to evaluate triggering events for determining whether those events are best categorized as low-interest or high-interest.

In an analysis processing device for performing analysis on body displacements of passengers in a vehicle, a calculation unit is configured to calculate seated and standing positions of the passengers based on recognition data. A period determination unit is configured to determine an analysis period of the vehicle according to a function that changes the analysis period depending on the seated and standing positions of the passengers.

A door access control system with a temperature detection function includes an infrared body temperature monitor device configured to detect a body temperature of a monitored object; a body temperature comparison device configured to compare a detected body temperature of the monitored object with a body temperature threshold; and a warning device configured to issue a warning message in response to the detected body temperature of the monitored object being higher than the body temperature threshold.

A smart home-care lighting system includes a lighting device having a portion that is light transmittable. The lighting device receives therein an illumination module for purpose of illumination, a detection module for detecting an outside environment, an alarm module and a sound effect module operable in combination with the detection module for indication and alarming, and an image pickup module for capturing and recording images. The lighting device also includes, arranged therein, a microprocessor and a main control module for transmitting and storing personal habits and health information. The device is arranged such that image can be captured in ranges that does not overlap to provide a surveillance range of 360-degree full view and operation can be made with the main control module for determination for proper action to be taken and may be used in combination with household appliances to construct a smart home-care system that benefit home members.

This document describes techniques and devices to detect and classify human and animal presence and/or motion based on changes in the interaction of the human or animal with Wireless Local Area Network (WLAN) radio frequency (RF) signals within or about a building structure, such as a home or office. A first WLAN device transmits a sounding packet to a second WLAN device and receives an acknowledgement (ACK) of receiving the sounding packet by the second WLAN device. The first WLAN device uses the received ACK to determine Channel State Information (CSI) for an RF signal path between the first WLAN device and the second WLAN device, aggregates the determined CSI with additional CSI, and uses the aggregated CSI to determine a presence or a motion within the structure.

A multilayer floor covering comprises a resilient top covering (12) for providing a walking surface, a sheet-type sensor layer (16) arranged underneath the resilient top covering, a first adhesive layer (14) attaching the resilient top covering to the sheet-type sensor layer and a second adhesive layer (18) for attaching the sheet-type sensor layer to an underlying subfloor (20). To facilitate access to the sheet-type sensor layer, the first adhesive layer (14) is configured to provide lower resistance to peeling than the second adhesive layer (18). The invention further proposes a kit of parts for achieving such a floor covering as well as a method for installing the floor covering.

A system and method for patient movement detection and fall monitoring to address the need to proactively monitor patients to detect abnormal movements, in-room activity, and other movements associated with providing in-room care. The system comprises an environmental model which can be used to track the position and movement of a patient and a classifier network configured to receive movement data and classify a patient's movement as normal or abnormal movement. In addition to monitoring in-room activity, the system and method create safe zones within the room to ensure patients are proactively monitor in the event of a seizure, fall, or other unintended activity. The system will record and store in-room video in a secure environment. Videos and notifications are automatically sent to designated staff as events occur.