The embodiments of the present disclosure disclose a system and method. The system may include at least one storage device configured to storage computer instruction; and at least one processor, in communication with the storage device. When executing the computer instructions, the at least one processor is configured to direct the system to perform operations including: obtaining a sensing signal of at least one sensing device; identifying a signal feature of the sensing signal; and determining, based on the signal feature, an operation of a target object associated with the at least one sensing device.

Embodiments relate to a device and method for determining a status of an environment and/or a status or condition of a person therein. The method may comprise: receiving an output of a sensor to monitor said environment; commencing a time window after the sensor detects activity in the environment; upon expiry of the time window, activating an active reflected wave detector to measure wave reflections from the environment, the detector consuming more power in an activated state than the sensor in an activated state; and determining a status of the environment and/or of a person therein based on an output of the active detector indicative of one or more of the measured reflections. The method comprises delaying expiry of the time window in response to the sensor detecting activity in the environment during the time window.

A fall detector device (102) for mounting on a wall for monitoring an environment (100), comprising: a motion sensor (204) for detecting motion of a person (106) within a first field of view of the motion sensor; an active reflective wave sensor (206) for detecting the presence of a person within a second field of view of the active reflective wave sensor using wave reflections from the environment, the first and second fields of views at least partially overlapping one another, and a processor coupled to the motion sensor and active reflective wave sensor for receiving output from each of the motion sensor and active reflective wave sensor, wherein operation of the active reflective wave sensor is dependent on a detection of motion of a person within at least a portion of the first field of view by the motion sensor.

There is provided a controller that performs: determining, based on information obtained from a sensor configured to detect a physical condition of a target person, which of a plurality of predetermined categories the physical condition of the target person falls into; and transmitting a notification corresponding to the physical condition of the target person to a terminal of a notification destination that corresponds to a category into which the physical condition of the target person is determined to fall, wherein terminals of notification destinations are set for the plurality of categories, respectively.

An artificial intelligence (AI) robot includes a body for defining an exterior appearance and containing a medicine to be discharged according to a medication schedule, a support, an image capture unit for capturing an image within a traveling zone to create image information, and a controller for discharging the medicine to a user according to the medication schedule, reading image data of the user to determine whether the user has taken the medicine, and reading image data and biometric data of the user after the medicine-taking to determine whether there is abnormality in the user. The AI robot identifies a user and discharges a medicine matched with the user, so as to prevent errors. The AI robot detects a user's reaction after medicine-taking through a sensor, and performs deep learning, etc. to learn the user's reaction, to determine an emergency situation, etc. and cope with a result of the determination.

Controlled-environment facility resident behavioral and/or health monitoring may employ controlled-environment facility resident wearables each having a band configured to be affixed around a portion of a controlled-environment facility resident, irremovable by the resident and may include sensor(s) configured to measure biometric(s) of the controlled-environment facility resident and one or more physical parameter(s) experienced by the wearable, with a transmitter transmitting the biometric(s) and/or the physical parameter(s) to a controlled-environment facility management system. The controlled-environment facility management system may predetermine one or more normal input levels of the biometric(s) and/or physical parameter(s), receive the transmitted biometric(s) and/or physical parameter(s), determine whether received biometric(s) and/or physical parameter(s) rises above or falls below the predetermined normal input level(s), and alert controlled-environment facility personnel and/or law enforcement when received physical parameter(s) and/or received biometric(s) rise above or fall below the predetermined normal input level(s).

A method for preventing a collision between a first object and a second object using a camera. The method includes capturing images of the first object and the second object using the camera, accessing a database of point clouds, and identify a first point cloud corresponding to the first object and a second point cloud corresponding to the second object within the database of point clouds, where the first point cloud corresponds to the first object being a person. The method further includes calculating a distance between the first object and the second object and comparing the distance to a threshold. The method further includes generating a notification when the distance is below the threshold.

A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing hybrid machine learning. More specifically, the system and method produce predictions of MCI conversions to dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is trained using transfer learning. A platform may then receive a request from a clinician for a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.

Remote monitoring of a subject wearing a sports helmet is enabled. In one aspect, an example system includes a safety helmet and a sensor integrated with the helmet for continuously gathering head acceleration force data, the head acceleration force data associated with the head movements of a subject. The system can also include a wireless transceiver coupled to the sensor for transmitting the head acceleration force data and a mobile device for receiving the head acceleration force data from the wireless transceiver. The system can further include a database engine for displaying the head acceleration force data to a user.

Disclosed is a capacitive sensor (100) for locating the presence of an individual and/or of an object, the sensor (100) including:—a first layer (C1) including at least one first electrode (E1i, i∈[1,N]) extending in a first direction (d1);—a second layer (C2) having at least one second electrode (E2j, j∈[1,M]) extending in a second direction (d2); in which the first direction (d1) is different from the second direction (d2), and in which the first layer (C1) is electrically insulated from the second layer (C2).