Methods and systems are provided for assisting a user of a wearable biosignal monitoring device (2) in adjusting the device to achieve optimum fit and positioning. The biosignal monitoring devices considered use integrated bio sensors (5) to monitor the user’s physiological activity for various purposes such as tracking daily activity patterns, determining mood, and monitoring sleep stages, among others. It is determined either during device setup or during primary use of the device whether the current fit and positioning of the device (2) enable the bio sensors (5) to properly sense the physiological signals needed for the device to perform its primary function. The user is then informed either after initial device setup whether adjustments need to be made in order to optimize device function during primary use, or is informed after primary use whether adjustments need to be made in order to improve device function during future primary use.

A physiological data detection method is provided. The physiological data detection method includes the following steps. Firstly, an ECG signal and a PPG signal are detected. Then, a plurality of RRI values is calculated according to the ECG signal, and a plurality of PPI values is calculated according to the PPG signal. Thereafter, wrong RRI values are excluded according to the RRI values and/or the PPI values. Then, whether an abnormal state occurs or not is determined by using the remaining RRI values. A wearable device therefor is also provided.

A dynamic neuropsychological assessment tool according to an embodiment utilizes speech recognition, speech synthesis and machine learning to assess whether a patient is at risk for a neurological disorder, such as Alzheimer's disease. The dynamic neuropsychological assessment tool enables self-administration by a patient. The tool performs pre-test validation operations on the test environment, test equipment, and the patient's capability for performing the test at that time. The tool also enables dynamic modification of a questionnaire presented to the patient while the patient completes the questionnaire. Also provides the dynamic modification of which tests to present the patient with. The modification can be rule based or modified by a provider. The dynamic neuropsychological assessment tool enables providers and administrators to modify and improve tests and validate them using machine learning based on previously completed assessments and results.

A medical apparatus, method, or storage medium to perform monitoring a sensor of the medical apparatus to obtain a sensor reading, performing impact detection based on the sensor reading, generating an impact profile based on the impact detection, characterizing the impact detection, and responding to the impact detection.

Access is provided to certain pulse oximetry systems utilizing a keyed sensor and a corresponding locked sensor port of a restricted access monitor. In such systems, the keyed sensor has a key comprising a memory element, and the monitor has a memory reader associated with the sensor port. The monitor is configured to function only when the key is in communications with the locked sensor port, and the memory reader is able to retrieve predetermined data from the memory element. The monitor is accessed by providing the key separate from the keyed sensor, integrating the key into an adapter cable, and connecting the adapter cable between the sensor port and an unkeyed sensor so that the monitor functions with the unkeyed sensor.

Systems and methods for providing sensitive and specific alarms indicative of glycemic condition are provided herein. In an embodiment, a method of processing sensor data by a continuous analyte sensor includes: evaluating sensor data using a first function to determine whether a real time glucose value meets a first threshold; evaluating sensor data using a second function to determine whether a predicted glucose value meets a second threshold; activating a hypoglycemic indicator if either the first threshold is met or if the second threshold is predicted to be met; and providing an output based on the activated hypoglycemic indicator.

An electronic system for bioimpedance signal acquisition, comprises: a current signal injection module configured for generating a current signal to be applied to a subject; a bioimpedance signal measurement module configured for measuring a bioimpedance signal based on a voltage generated by the current signal; a data quality detection module configured for detecting an AC or a DC level of the measured bioimpedance signal and detecting whether the AC or DC level is within or outside an AC reference value range and a DC reference value range, respectively; and a signal adaptation module configured for modifying at least one parameter of the current signal injection module or the bioimpedance signal measurement module based on said detection of the AC or DC level in relation to the AC reference value range and the DC reference value range, respectively.

This disclosure relates generally to a method and a system for determining quality of PPG signal. The PPG signals are extensively used for deducing health parameters of patients to infer the physiological conditions of heart, blood pressure, breathing patterns of the patients. However, analysis based on PPG signals is extremely challenging and is accurate only on high quality PPG signals. However, the existing techniques for determining quality of PPG signal (that are collected using wearable devices) require huge training or use complicated algorithms and cannot be used for real-time analysis. The disclosed methods and system for PPG quality assessment is based on the frequency domain analysis, wherein heart and respiratory components in the frequency spectrum are used effectively to derive the quality checker metric which is further used to estimate a plurality of optimal thresholds that is used for determining the quality of PPG signals at real-time.

A signal measurement circuit comprises: a sensor electrode layer connected via a sensor cable to a measurement amplifier circuit; an active shielding layer, which runs along a side of the sensor electrode layer that faces away from the patient; and a first insulating layer that runs between the sensor electrode layer and the active shielding layer. The sensor electrode layer and the active shielding layer are embodied to be electrically conductive.

Systems and methods for predicting problem behavior in individuals with developmental and behavior disabilities. A plurality of sensors are configured to collect multimodal data signals of a subject individual including a wearable upper body motion sensing device with a plurality of inertial measurement units (IMUs). An electronic controller is configured to receive output signals from each of IMUs and to model an upper body position of the subject individual based on the output signals from the IMUs. A trained machine-learning model is then applied by providing an input data set that includes multimodal signal data (e.g., including signal data from at least one IMU) and/or features extracted from the multimodal signal data. The machine-learning model is trained to produce as output an indication of whether a precursor to the problem behavior is detected and, in response to detecting the precursor, a notification (or alarm) is generated.