Systems and methods for correlating inputs found external to a user to inputs measured from the user are disclosed, using wearables, various types of non-wearable sensors, and other external data sources and mobile device technology. Pattern matching and rules can be used to provide useful suggestions or control external machines based on the correlated inputs.

Embodiments herein relate to ear-wearable systems and devices that can detect allergic reactions. In an embodiment, an ear-wearable device is included having a control circuit, a microphone, and a sensor package. The ear-wearable device can be configured to evaluate at least one of signals from the microphone, signals from the sensor package, signals from an external sensor, and contextual factor data, and detect an allergic reaction based on the evaluation. In an embodiment, an ear-wearable device system is included having a first ear-wearable device and a second ear-wearable device. In an embodiment, a method of predicting or detecting the onset or presence of an allergic reaction with an ear-wearable system is included. Other embodiments are also included herein.

Embodiments herein relate to ear-wearable systems and devices for detecting heat stress and related methods. In an embodiment, an ear-wearable heat stress risk assessment system is included having a control circuit, a microphone, and a sensor package. The system is configured to process signals of one or more sensors of the sensor package and/or the microphone, detect dehydration symptoms, environmental conditions, and activity levels of a device wearer based on the processed signals, and determine a heat stress risk level based on detected dehydration symptoms, environmental conditions, and activity levels of the device wearer. Other embodiments are also included herein.

The invention provides a multi-sensor system that uses an algorithm based on adaptive filtering to monitor a patient's respiratory rate. The system features a first sensor selected from the following group: i) an impedance pneumography sensor featuring at least two electrodes and a processing circuit configured to measure an impedance pneumography signal; ii) an ECG sensor featuring at least two electrodes and an ECG processing circuit configured to measure an ECG signal; and iii) a PPG sensor featuring a light source, photodetector, and PPG processing circuit configured to measure a PPG signal. Each of these sensors measures a time-dependent signal which is sensitive to respiratory rate and, during operation, is processed to determine an initial respiratory rate value. An adaptive digital filter is determined from the initial respiratory rate. The system features a second sensor (e.g. a digital 3-axis accelerometer) that attaches to the patient's torso and measures an ACC signal indicating movement of the chest or abdomen that is also sensitive to respiratory rate. This second signal is processed with the adaptive filter to determine a final value for respiratory rate.

The present invention provides for a data acquisition system for EEG and other physiological conditions, preferably wireless, and method of using such system. The wireless EEG system can be used in a number of applications including both studies and clinical work. These include both clinical and research sleep studies, alertness studies, emergency brain monitoring, and any other tests or studies where a subject's or patient's EEG reading is required or helpful. This system includes a number of features, which enhance this system over other systems presently in the marketplace. These features include but are not limited to the having multiple channels for looking at a number of physiological features of the subject or patient, a built in accelerometer for looking at a subject's or patient's body motion, a removable memory for data buffering and storage, capability of operating below 2.0 GHz, which among other things allows for more channels, movement artifact correction including video, pressure sensors capable of measuring or determining airflow, tidal volume and ventilation rate, and capability of manual and automatic RF sweep.

Semi-autonomous vehicle apparatus which is controlled by a plurality of control sources includes a vehicle which may function autonomously and apparatus for control of the vehicle by either an onboard driver or a driver not situated onboard. The vehicle may also be controlled by an off-vehicle computational device. Hierarchy setting apparatus determines which one or combination of the possible control entities take priority. Persons using the apparatus are identified by either a password or, preferably by providing identification based on a biologic feature. Management of impaired vehicle operators is provided for.

An elongate medical device shaft may comprise an elongate body and an annular electrode disposed on the elongate body. The annular electrode may define a longitudinal axis and have an outer diameter. The outer diameter may be greater at an axial center of the electrode than at an axial end of the electrode. Additionally or alternatively, the elongate body may comprise three longitudinal sections having three wall thicknesses. The middle wall thickness may be less than the proximal and distal wall thicknesses and the distal wall thickness may be less than the proximal wall thickness. Additionally or alternatively, the shaft may comprise an inner cylindrical structure and an outer tube. The outer tube may comprise a first radial layer and a second radial layer that is radially-outward of the first radial layer, the first radial layer, second radial layer, and inner structure having different stiffnesses.

A method for quantitative flow analysis of a fluid flowing in a conduit from a sequence of consecutive image frames of such a conduit, where such image frames are timely separated by a certain time interval, the method comprising: a) selecting a start image frame and an end image frame from the sequence either automatically or upon user input; b) determining a centerline of the conduit in the start image frame; c) determining a centerline of the conduit in the end image frame; d) selecting a common start point on the centerline of the start image frame and on the centerline of the end image frame either automatically or upon user input; e) selecting an end point on the centerline of the start image frame; f) selecting an end point on the centerline of the end image frame; g) calculating centerline distance between the start point and the end point of the start image frame; h) calculating centerline distance between the start point and the end point of the end image frame; and i) calculating a local flow velocity as a function of the centerline distances of g) and h) and a time interval between the start image frame and the end image frame.
A corresponding imaging device and computer program are also disclosed.

A method for quantitative flow analysis of a fluid flowing in a conduit from a sequence of consecutive image frames of such a conduit, where such image frames are timely separated by a certain time interval, the method comprising: a) selecting a start image frame and an end image frame from the sequence either automatically or upon user input; b) determining a centerline of the conduit in the start image frame; c) determining a centerline of the conduit in the end image frame; d) selecting a common start point on the centerline of the start image frame and on the centerline of the end image frame either automatically or upon user input; e) selecting an end point on the centerline of the start image frame; f) selecting an end point on the centerline of the end image frame; g) calculating centerline distance between the start point and the end point of the start image frame; h) calculating centerline distance between the start point and the end point of the end image frame; and i) calculating a local flow velocity as a function of the centerline distances of g) and h) and a time interval between the start image frame and the end image frame.
A corresponding imaging device and computer program are also disclosed.

An Example of a system for providing a patient with pain management may include a sleep monitoring circuit, a pain relief device, and a control circuit. The sleep monitoring circuit may be configured to sense one or more sleep signals from the patient and to determine a sleep state of the patient using the one or more sleep signals. The one or more sleep signals may include one or more physiological signals corresponding to the sleep state of the patient. The pain relief device may be configured to deliver one or more pain relief therapies. The control circuit may be configured to control the delivery of the one or more pain relief therapies using therapy parameters and to adjust the therapy parameters based on the determined sleep state.