A personal monitoring system includes one or more passive biometric sensors and a communication device. A passive biometric sensor is operable to sense a body condition of a body in accordance with a sense signal at a sense frequency to produce sensed data of a body condition. The passive biometric sensor is further operable to transmit an e-field signal via the body regarding the sensed data, wherein the e-field signal is in accordance with an e-field transmit/receive frequency. The communication device is operable to receive the e-field signal via the body. The communication device is further operable to recover the sensed data from the received e-field signal.

Trained machine learning models can be used for analysis of signals obtained through an in-ear or on-body device. Signals can be analyzed to determine information related to activities such as eating, chewing, drinking, coughing, or sneezing. In addition, data from an in-ear thermometer or other data sensors can be analyzed in conjunction with the machine learning models to provide data or recommendations to a user on a user device or initiate an action.

Hand held weight units of light weight manufactured as a solid unit, a shell unit with core insert combinations or modular units with interlocking ends. Shell units with core inserts and modular interlocking units allow for the changing of held weight by inserting or removing inserts or by locking or unlocking of modular weight unit sets creating varying held weight. The weight units are primarily used with upper body exercises during aerobic exercises in the home, outdoors, or in a gym setting such as walking or running to vary the intensity of workout during use.

This disclosure describes devices, systems, and methods related to therapy devices that are operable in multiple operating modes. An exemplary therapy device is configured to be coupled to a patient and includes a stimulation generator and a controller coupled to the stimulation generator. The controller is configured to transition the stimulation generator from operating in a first operating mode to operating in a second operating mode responsive to determining activity of the patient.

A system for monitoring and measuring itch of a patient includes a wearable device including an actigraph sensor; and a controller electrically connected to the wearable device and including a processor and a memory. The wearable device is configured to measure, via the actigraph sensor, a movement of the patient; and send data indicative of the measured movement of the patient to the controller. The controller is configured to receive, via the processor, the data indicative of the measured movement; and determine, via the processor, a scratching movement of the patient based on the data indicative of the measured movement.

A method and system for activity classification. A pressure sensor receives input data resulting from physical activity of a subject performing an activity. The input data includes pressure data from at least one pressure sensor, and may include other data acquired through other types of sensors. A deep learning neural network is applied to the input data for identifying the activity. The neural network is trained with reference to training data from a training database. The training data may include empirical data from a database of previous data of corresponding activities, synthesized data prepared from the empirical data or simulated data. The training data may include data from physical activity of the subject being monitored by the system. Different aspects of the neural network may be trained with reference to the training data, and some aspects may be locked or opened depending on the application and the circumstances.

In an example method, a mobile device obtains sample data generated by one or more sensors over a period of time, where the one or more sensors are worn by a user. The mobile device determines that the user has fallen based on the sample data, and determines, based on the sample data, a severity of an injury suffered by the user. The mobile device generates one or more notifications based on the determination that the user has fallen and the determined severity of the injury.

Embodiments are disclosed for a machine learning (ML) gesture recognition with a framework for adding user-customized gestures. In an embodiment, a method comprises: receiving sensor data indicative of a gesture made by a user, the sensor data obtained from at least one sensor of a wearable device worn on a limb of the user; generating a current encoding of features extracted from the sensor data using a machine learning model with the features as input; generating similarity metrics between the current encoding and each encoding in a set of previously generated encodings for gestures; generating similarity scores based on the similarity metrics; predicting the gesture made by the user based on the similarity scores; and performing an action on the wearable device or other device based on the predicted gesture.

An electronic battery-operated medical bracelet device that contains the technology to accurately detect collapse/fall or other medical emergencies, through the monitoring of the heart rate, oxygen saturation, heart rhythm, and patient input. The bracelet will also display the vital readings to the user if the readings return as abnormal, in an attempt to persuade the user to seek medical attention before it becomes worse. Once a medical emergency is detected, the device alerts surrounding people and paramedics of the emergency and distributes medical and personal information different for surrounding people and paramedics. This includes medical conditions, health insurance information, next of kin, etc. This information can be uploaded to the device's onboard memory drive and can share the information or steps to reach the information, through an onboard speaker system, Bluetooth pairing, and near-filled communication (NFC) to which paramedics have special access.

Methods, apparatus and systems for radio-based sleep tracking are described. In one example, a described system comprises: a transmitter configured to transmit a first wireless signal through a wireless multipath channel in a venue; a receiver configured to receive a second wireless signal through the wireless multipath channel, wherein the second wireless signal differs from the first wireless signal due to the wireless multipath channel which is impacted by a sleeping motion of an object in the venue; and a processor. The processor is configured for: obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the second wireless signal, wherein each channel information (CI) of the TSCI comprises N1 components, wherein N1 is a positive integer larger than one, computing N1 component-wise analytics each associated with one of the N1 components of the TSCI, identifying N2 largest component-wise analytics among the N1 component-wise analytics, wherein N2 is a positive integer less than N1 computing at least one first motion statistics based on the N2 largest component-wise analytics of the TSCI, and monitoring the sleeping motion of the object based on the at least one first motion statistics.