A system may obtain a set of features characterizing a segment of inertial measurement unit (IMU) data generated by an IMU of an ear-wearable device. The system may apply a machine learning model (MLM) that takes the features characterizing the segment of the IMU data as input. The system may determine, based on output values produced by the MLM, whether a user of the ear-wearable device has potentially been subject to physical abuse. The system may then perform an action in response to determining that the user of the ear-wearable device has potentially been subject to physical abuse.

According to the present invention, a filtering apparatus includes an FIR filter that has multipliers arranged to multiply input digital data having their respective different input time points by respective variable tap coefficients. The variable tap coefficients are each switched from a first tap coefficient to a second tap coefficient sequentially for the input digital data from later to earlier input time points. The first tap coefficient is arranged to cause the FIR filter to serve as a low-pass filter with the cut-off frequency set at a first frequency. The second tap coefficient is arranged to cause the FIR filter to serve as a low-pass filter with the cut-off frequency set at a second frequency different from the first frequency.

A magnetometer-based metal detection device and methods of use are described. The device includes a proximal portion, a central body and a distal portion, and at least one magnetometer positioned within or on the distal portion. The at least one magnetometer includes at least one sensor capable of sensing a magnetic field in three orthogonal axes. Also described is a method of calibrating the device to achieve rotational invariance, and a method of determining a directionality or directional line along which a target metal object lies.

Disclosed is a magnetoencephalography apparatus (100) and a method. The apparatus comprises a plurality of magnetic sensors, one or more processors and one or more memories. The method comprises obtaining a reference data, calculating from the reference data a reference basis, obtaining a source basis, obtain a source data, adding together the source basis and the reference basis to form a joint basis and determine an estimate for the magnetic brain activity of the source by parametrizing the source data in the joint basis.

A readout integrated circuit (IC) architecture for a tunnelling magnetoresistive (TMR) sensor which uses common mode feedback to achieve a performance level suitable for accurate detection of biomagnetic signals. The architecture uses a three-operational amplifier configuration with chopper stabilization. The architecture may form part of a fully integrated biomagnetic sensor electronics package that includes an array of TMR sensors together with modules for signal amplification and conditioning, data conversion and communication.

Various analyte sensor systems for controlling activation of analyte sensor electronics circuitry are provided. Related methods for controlling analyte sensor electronics circuitry are also provided. Various analyte sensor systems for monitoring an analyte in a host are also provided. Various circuits for controlling activation of an analyte sensor system are also provided. Analyte sensor systems utilizing a state machine having a plurality of states for collecting a plurality of digital counts and waking a controller responsive to a wake up signal are also provided. Related methods for such analyte sensor systems are also provided. Systems for controlling activation of analyte sensor electronics circuitry utilizing a magnetic sensor are further provided. One or more display device configured to display one or more analyte concentration values are also provided.

A robot according to the present disclosure comprises: a microphone; a camera disposed to face a predetermined direction; and a processor configured to: inactivate driving of the camera and activate driving of the microphone, if a driving mode of the robot is set to a user monitoring mode; acquire a sound signal through the microphone; activate the driving of the camera based on an event estimated from the acquired sound signal; confirm the event from the image acquired through the camera; and control at least one constituent included in the robot to perform an operation based on the confirmed event.

An exemplary wearable sensor unit includes 1) a magnetometer comprising a vapor cell comprising an input window and containing an alkali metal, and a light source configured to output light that passes through the input window and into the vapor cell along a transit path, and 2) a temperature control circuit external to the vapor cell and configured to create a temperature gradient within the vapor cell, the temperature gradient configured to concentrate the alkali metal within the vapor cell away from the transit path of the light.

Embodiments of the presently described device, system and method support upper extremity (UE) therapy through tracking the human hand. The system can include a hand-wearable sensor mounting system with hand-wearable components and a movement interpretation circuit. The device can include a hand-wearable component comprising a hook, and a sensing transducer comprising a clip, wherein the clip is detachably securable to the hook. In embodiments, one or more sensing transducers are translationally and rotationally restricted when secured to the hand-wearable components.

A system for monitoring medical conditions including pressure ulcers, pressure-induced ischemia and related medical conditions comprises at least one sensor adapted to detect one or more patient characteristic including at least position, orientation, temperature, acceleration, moisture, resistance, stress, heart rate, respiration rate, and blood oxygenation, a host for processing the data received from the sensors together with historical patient data to develop an assessment of patient condition and suggested course of treatment, including either suspending or adjusting turn schedule based on various types of patient movement. Compliance with Head-of-Bed protocols can also be performed based on actual patient position instead of being inferred from bed elevation angle. The sensor can include bi-axial or tri-axial accelerometers, as well as resistive, inductive, capacitive, magnetic and other sensing devices, depending on whether the sensor is located on the patient or the support surface, and for what purpose.