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.

Patient sleep is staged using personalized circadian models built with data collected by wearable devices over daytime and nighttime hours, thus capturing a patient's personal circadian rhythms. The circadian model is used to identify sleep intervals in incoming nightly data for the patient. The identified sleep intervals are analyzed by the machine learning system which stages epochs of sleep. Methods include receiving patient heart rate data from over a plurality of circadian cycles; creating a circadian model for the patient with a defined operation for applying sleep labels to new data from the wearable device; applying the circadian model to nightly test data from the device to identify a sleep interval; and assigning, with a classifier, sleep stages to epochs of the sleep interval.

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.

Exercise-based watch faces and complications for use with a portable multifunction device are disclosed. The methods described herein for exercise-based watch faces and complications provide indications of time and affordances representing applications (e.g., a workout application or a weather application). In response to detecting a user input corresponding to a selection of the affordance (e.g., representing a workout application), a workout routine can optionally be begun. Further disclosed are non-transitory computer-readable storage media, systems, and devices configured to perform the methods described herein, as well as electronic devices related thereto.

Athletic activity may be tracked while providing encouragement to perform athletic activity. For example, energy expenditure values and energy expenditure intensity values may be calculated and associated with a duration and type of activity performed by an individual. These values and other movement data may be displayed on an interface in a manner to motivate the individual and maintain the individual's interest. The interface may track one or more discrete “sessions”. The sessions may be associated with energy expenditure values during a duration that is within a second duration, such as a day, that is also tracked with respect to variables, such as energy expenditure. Other individuals (e.g., friends) may also be displayed on an interface through which a user's progress is tracked. This may allow the user to also view the other individuals' progress toward completing an activity goal and/or challenge.

A machine learning model is trained for user activity detection and context detection on a mobile device. The machine learning model is configured to learn a statistical relationship between an always-on sensing modality of the mobile device and actual user context. Rather than user annotations, the machine learning model is enhanced and personalized for the always-on sensing modality by automated annotations obtained from non-always-on sensing modalities. The non-always-on sensing modality opportunistically provides an imperfect label of user context, where the imperfect label has a known associated probability of error.

A wireless, patient-worn, physiological sensor configured to, among other things, help manage a patient that is at risk of forming one or more pressure ulcers is disclosed. According to an embodiment, the sensor includes a base having a top surface and a bottom surface. The sensor also includes a substrate layer including conductive tracks and connection pads, a top side, and a bottom side, where the bottom side of the substrate layer is disposed above the top side of the base. Mounted on the substrate layer are a processor, a data storage device, a wireless transceiver, an accelerometer, and a battery. In use, the sensor senses a patient's motion and wirelessly transmits information indicative of the sensed motion to, for example, a patient monitor. The patient monitor receives, stores, and processes the transmitted information.

A method for monitoring a health parameter in a person is disclosed. The method involves transmitting radio waves below the skin surface of a person and across a range of stepped frequencies, receiving radio waves on a two-dimensional array of receive antennas, the received radio waves including a reflected portion of the transmitted radio waves across the range of stepped frequencies, generating data that corresponds to the received radio waves, wherein the data includes amplitude and phase data, deriving data from at least one of the amplitude and phase data, and determining a value that is indicative of a health parameter in the person in response to the derived data.

A method for uterine activity monitoring may include: acquiring a plurality of signals from a plurality of sensors during uterine activity; processing the plurality of signals to extract a plurality of uterine electrical activity characteristics; analyzing the plurality of uterine electrical activity characteristics; and classifying the uterine activity as one of: a preterm labor contraction, a labor contraction, a Braxton-Hicks contraction, and a state of no contraction. A method of assessing over time a pre-term birth risk of a pregnant female may include: calculating a baseline pre-term birth risk score based on a user input; acquiring, over time, a signal from a sensor; analyzing the signal to extract a parameter of interest, such that the parameter of interest comprises a physiological parameter; and calculating an instant pre-term birth risk score based, at least in part, on the parameter of interest and the user input.

A method for assessing the orthopaedic performance of a joint of a patient can comprise implanting at least a first and second RF wirelessly detectable markers in first and second bones associated with a site and determining and storing their positions before a surgical procedure is performed. The procedure can be carried out on the site and the positions of the first and second markers can be detected and stored after the procedure has been completed. The detected positions can be used to generate a representation of the orthopaedic performance of the joint after the procedure.