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.

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.

It is provided a method for detecting people, the method being performed by a people detector provided by a doorway. The method comprises the steps of: receiving an open signal indicating that a door of the doorway is open; setting a people sensor of the people detector in an active mode based on receiving the open signal; detecting when a person passes through the doorway, using the people sensor; receiving a closed signal indicating that the door of the doorway is closed; transmitting a result of the step of detecting, wherein the step of transmitting is performed based on receiving the closed signal; and setting the people sensor in a power save mode based on receiving the closed signal.

It is provided a method for detecting people, the method being performed by a people detector provided by a doorway. The method comprises the steps of: receiving an open signal indicating that a door of the doorway is open; setting a people sensor of the people detector in an active mode based on receiving the open signal; detecting when a person passes through the doorway, using the people sensor; receiving a closed signal indicating that the door of the doorway is closed; transmitting a result of the step of detecting, wherein the step of transmitting is performed based on receiving the closed signal; and setting the people sensor in a power save mode based on receiving the closed signal.

A device and method for computing a relative direction to a Target, the device including a single antenna exchanging wireless signals with the Target, where the device moves from an initial position to additional positions, where in both positions the single antenna exchanges signals with the Target and the device measures distance-calculation-enabling properties of the wireless signal, where the device then estimates a distance to the Target based on the measured properties, where the device then computes a change in a distance between the DF electronic device and the Target according to the measured distance-calculation-enabling properties of the wireless signals in the initial position and the additional position, where the device then computes a relative direction of the Target from the DF electronic device's heading based on the change between the calculated distances and an associated changes in position of the DF electronic device.

A device and method for computing a relative direction to a Target, the device including a single antenna exchanging wireless signals with the Target, where the device moves from an initial position to additional positions, where in both positions the single antenna exchanges signals with the Target and the device measures distance-calculation-enabling properties of the wireless signal, where the device then estimates a distance to the Target based on the measured properties, where the device then computes a change in a distance between the DF electronic device and the Target according to the measured distance-calculation-enabling properties of the wireless signals in the initial position and the additional position, where the device then computes a relative direction of the Target from the DF electronic device's heading based on the change between the calculated distances and an associated changes in position of the DF electronic device.

Techniques and apparatuses are described that implement motion classification using low-level detections. In particular, a radar system identifies fused detections associated with an object and determines whether the fused detections indicate that the object is moving. If it is determined to be moving or moving perpendicular to the host vehicle, a current motion counter or perpendicular motion counter is incremented, respectively. A current motion flag and/or a perpendicular motion flag are set as true if the current motion counter or the perpendicular motion counter has a value greater than a threshold value, respectively. In response to setting either flag as true, the radar system increments a historical motion counter as true. The host vehicle is then operated based on the current motion flag, the perpendicular motion flag, and the historical motion counter. In this way, the radar system introduces hysteresis to improve the reliability and stability of motion classification.

Vehicles and methods for tracking an object and controlling a vehicle based on the tracked object. A Radar-Doppler (RD) map is received from the radar sensing system of the vehicle and relative acceleration of an object with respect to the vehicle is detected based on the RD map so as to provide acceleration data. A current frame of detected object data is received from a sensing system of the vehicle. When the relative acceleration has been detected, a tracking algorithm is adapted to reduce the influence of the predictive motion model or the historical state of the object and the object is tracked using the adapted tracking algorithm so as to provide adapted estimated object data based on the object tracking. One or more vehicle actuators are controlled based on the adapted estimated object data.

Vehicles and methods for tracking an object and controlling a vehicle based on the tracked object. A Radar-Doppler (RD) map is received from the radar sensing system of the vehicle and relative acceleration of an object with respect to the vehicle is detected based on the RD map so as to provide acceleration data. A current frame of detected object data is received from a sensing system of the vehicle. When the relative acceleration has been detected, a tracking algorithm is adapted to reduce the influence of the predictive motion model or the historical state of the object and the object is tracked using the adapted tracking algorithm so as to provide adapted estimated object data based on the object tracking. One or more vehicle actuators are controlled based on the adapted estimated object data.

Techniques and apparatuses are described that implement a smart device with an integrated radar system. The radar integrated circuit is positioned towards an upper-middle portion of a smart device to facilitate gesture recognition and reduce a false-alarm rate associated with other non-gesture related motions of a user. The radar integrated circuit is also positioned away from Global Navigation Satellite System (GNSS) antennas and a wireless charging receiver coil to reduce interference. The radar system operates in a low-power mode to reduce power consumption and facilitate mobile operation of the smart device. By limiting a footprint and power consumption of the radar system, the smart device can include other desirable features in a space-limited package (e.g., a camera, a fingerprint sensor, a display, and so forth).