According to an embodiment, a wearable electronic device may include a motion sensor, an audio sensor, a display, a memory, and a processor electrically connected to the motion sensor, the audio sensor, and the memory. The processor may be configured to obtain motion sensing information via the motion sensor, obtain an audio signal corresponding to the motion sensing information via the audio sensor, identify a tooth-brushing hand motion type corresponding to the motion sensing information, identify an audio signal pattern corresponding to the tooth-brushing hand motion type, and identify, based on the tooth-brushing hand motion type and the audio signal pattern, a tooth-brushing hand motion. Other embodiments may also be possible.

A method for detecting a user's intent in an adaptive lower limb device includes providing the adaptive lower limb device. The lower limb device includes a device control unit. The device control unit includes activity controllers and at least one accelerometer. The acceleration features are measured via the at least one accelerometer. The measured acceleration features are determined whether they correspond to a tapping movement initiated by a user with an intent to switch from a first one of the plurality of activity controllers to a second one of the plurality of activity controllers. If the measured accelerating features correspond to the tapping movement, the control unit of the adaptive lower limb device is switched from the first one of the activity controllers to the second one of the activity controllers.

A device for monitoring the health state is made in a chip including a semiconductor die integrating an electric potential sensor and a cardiac parameter determination unit. The potential sensor is configured to detect potential variations on the body of a living being and associated with a heart rhythm and to generate a cardiac signal. The cardiac parameter determination unit is configured to receive the cardiac signal and determine cardiac parameters indicative of a health state. In particular, the cardiac parameter determination unit is configured to detect triggering events and to determine features of the cardiac signal in time windows defined by the triggering events. The die also integrates a decision unit, configured to receive the cardiac parameters and generate a health signal based on a comparison with threshold values. The cardiac parameters include heart rate and QRS-complex.

In one embodiment, an ablation system includes a catheter including at least one electrode, and configured to be inserted into a chamber of a heart of a living subject, an ablation power generator configured to apply an electrical signal to the at least one electrode to ablate tissue of the chamber, at least one body surface patch configured to be applied to a body surface of the living subject, and provide at least one position signal, and a processor configured to compute an index of a measurement of diaphragm movement responsively to the at least one position signal, and perform an action responsively to the computed index.

The embodiments of the present disclosure disclose a system and method. The system may include at least one storage device configured to storage computer instruction; and at least one processor, in communication with the storage device. When executing the computer instructions, the at least one processor is configured to direct the system to perform operations including: obtaining a sensing signal of at least one sensing device; identifying a signal feature of the sensing signal; and determining, based on the signal feature, an operation of a target object associated with the at least one sensing device.

Measurement of an induced shear wave in tensioned tissue of a given individual is provided to a machine learning system trained to determine absolute load from shear wave signal data. The machine learning system uses a teaching set linking shear wave signal data to absolute load, however, does not require normal calibration data based on measured loads allowing reduced or no calibration for absolute load determinations.

Disclosed herein are an apparatus and a method for estimating the behavior of a user based on an image converted from sensing data. The apparatus for estimating the behavior of a user based on an image converted from sensing data includes memory for storing at least one program, and a processor for executing the program, wherein the program performs acquiring sensing data measured by one or more behavior measurement devices worn by the user, converting sensing data of the user obtained for a predetermined time period into images, and estimating the behavior of the user from the images of the user based on a pre-trained model.

A system includes an electronic contact lens that obtains sensor measurements from integrated motion sensors or other types of sensors and a processing module that estimates a mental state of an individual based on the sensor measurements. The processing module identifies patterns of eye movements and analyzes how these patterns change over time. Based on anatomical relationships between eye movement and mental state, the processing module estimates characteristics of the individual such as fatigue, intoxication, injury, or a medical condition that have known effects on eye movement patterns. The electronic contact lens system generates an output indicative of the estimated mental state to alert the individual to the detected condition or to initiate an automated action.

A computing device may be communicably coupled to a first pacemaker implanted in a heart of a patient and a second pacemaker implanted in the heart of the patient. The computing device may receive, from the first pacemaker, first race responsive pacing data, and may receive, from the second pacemaker, second rate responsive pacing data. The computing device may synchronize, based at least in part on the first rate responsive pacing data and the second rate responsive pacing data, rate responsive pacing of the first pacemaker and the second pacemaker.

The various embodiments of the method of the present invention include a method to improving or expanding the capacity of a sleep analysis unit or laboratory, a method sleep analysis testing a patient admitted for diagnosis or treatment of another primary medical condition while being treated or diagnosed for that condition, a method of sleep analysis testing a patient that cannot be easily moved or treated in a sleep analysis unit or laboratory and other like methods.