A method of protecting an in-vivo sensor includes forming a sensing surface on a surface of the in-vivo sensor, the sensing surface including a functionalized monolayer that will bind to an analyte of interest; and coating the sensing surface of the sensor with a bioabsorbable polymeric coating including a bioabsorbable polymer; wherein the bioabsorbable polymeric coating is configured to protect the in-vivo sensor until needed for implantation.

A bed-leaving prediction device (server device) (10) is connected through a digital communication network (60) to: a portable information processing terminal (40) of care staff; environmental sensors (32 to 34) for detecting environment values such as temperature in a room; a human sensor (31); and a bed sensor (35). A bed-leaving prediction processing section (115) calculates a bed-leaving prediction value indicative of a degree of possibility that a care recipient leaves a sleeping furniture after a second time interval has expired since a current time point based on a plurality of environment values detected in a time period between the current time point and a time point before expiration of a first time interval, outputs of the human sensor, and outputs of the bed sensor. A bed-leaving notification processing section (117) compares the bed-leaving prediction value with a threshold value, and transmits, to the portable information processing terminal, a bed-leaving notification indicating that the care recipient leaves the sleeping furniture after the second time interval expires when the bed-leaving prediction value exceeds the threshold value.

A physiological signal monitoring device includes a sensing member and a transmitter connected to the sensing member and including a circuit board that has electrical contacts, and a connecting port, which includes a socket communicated to the circuit board and a plurality of conducting springs disposed at two opposite sides of the socket. The sensing member is removably inserted into the socket. The conducting springs are electrically connected to the electrical contacts and the sensing member for enabling electric connection therebetween. Each of the conducting springs is frictionally rotated by the sensing member during insertion of the sensing member into the socket and removal of the sensing member from the socket.

An orthopedic system configured for use in a pre-operative, intra-operative, and post-operative assessment. The orthopedic system comprises a first screw, a second screw, a first device, a second device, and a computer. The first device and the second device are respectively coupled to a first bone and a second bone of a musculoskeletal system. The first and second devices each include electronic circuitry, one or more sensors, and an IMU. A bracket, wrap, or sleeve can be used to hold the first and second devices to the musculoskeletal system. The first and second devices are configured to send measurement data to a computer. The first and second devices each have an antenna system. Electronic circuitry in the first or second devices are configured to harvest energy from a received radio frequency signal to recharge a battery to maintain operation.

There is provided a method for determining dementia risk factors by a server using deep learning. In this instance, the method for determining dementia risk factors includes acquiring biometric information from each subject corresponding to a first control group through a wearable device, acquiring measurement information for each subject corresponding to the first control group, deriving a first dementia risk factor based on the biometric information and the measurement information for each subject, and deriving a second dementia risk factor related to the first dementia risk factor via deep learning performed based on the biometric information related to the first dementia risk factor and control group information.

An integrated triad system to measure a patient's blood pressure at different body positions, to instruct the patient when to move from one position to the next to alleviate vascular hypertension based on an algorithm stored in the system calculating blood pressure changes and predictive risk, and to transmit the result to a remote medical location. Using the integrated triad system to perform the test over a period can increase test sensitivity by accommodating the variable onset of the disease.

Embodiments herein relate to embodiments herein relate to hearing assistance systems and methods for monitoring a device wearer's emotional state and status. In an embodiment, a hearing assistance system is included having an ear-worn device that can include a control circuit and a microphone in electronic communication with the control circuit. The ear-worn device can be configured to monitor signals from the microphone, analyze the signals in order to identify speech, and transmit data based on the signals representing the identified speech to a separate device. Other embodiments are also included herein.

A healthcare apparatus includes a ballistocardiogram (BCG) sensor configured to sense a ballistocardiogram signal of a subject, a camera configured to acquire a color facial image, and a processor configured to detect a region of interest (ROI) from the color facial image, to detect a first color image of a forehead area to acquire a first black and white image, to detect a second color image of a cheek area to acquire a second black and white image, to apply the first and second black and white images to a predetermined trained algorithm model to output a remote photoplethysmography (rPPG) signal waveform of the subject, to calculate a first heart rate from the BCG signal waveform, to calculate a second heart rate from the remote PPG signal waveform, and to output a heart rate of the subject based on the first heart rate and the second heart rate.

A sensor including electrodes, a control module and a physical layer module. The electrodes are configured to (i) attach to a patient, and (ii) receive a first electromyographic signal from the patient. The control module is connected to the electrodes. The control module is configured to (i) detect the first electromyographic signal, and (ii) generate a first voltage signal. The physical layer module is configured to: receive a payload request from a console interface module or a nerve integrity monitoring device; and based on the payload request, (i) upconvert the first voltage signal to a first radio frequency signal, and (ii) wirelessly transmit the first radio frequency signal from the sensor to the console interface module or the nerve integrity monitoring device.

Multi-state engagement with continuous glucose monitoring (CGM) systems is described. Given the number of people that wear CGM systems and because CGM systems produce measurements continuously, a platform that provides a CGM system may have an enormous amount of data. This amount of data is practically, if not actually, impossible for humans to process. In implementations, a CGM platform includes a data analytics platform that obtains packages of glucose measurements provided by a CGM system and also obtains additional data associated with a user. The data analytics platform generates state information for the user by processing these CGM packages and the additional data, at least in part, by using one or more models. Based on this state information, the data analytics platform controls communication with the user, which may include generating intervention strategies to prevent users from transitioning to a negative state such as discontinuing use of the CGM system.