Noctrix Health, Inc.

Fitbit, Inc.

Jonathan David Charlesworth

Shriram Raghunathan

Conor Joseph Heneghan

John Craig Colborn

Shelten Gee Jao Yuen

Subramaniam Venkatraman

Logan Niehaus

Hao-Wei Su

Douglas Richard Jeffrey

Alexander Statan

Techniques to help improve efficiency or effectiveness of treating a disorder such as RLS or PLMD, such as by issuing neural electrostimulations to a particular patient, while varying one or more amplitude parameters (e.g., at least one of electrostimulation current amplitude, electrostimulation voltage amplitude, or electrostimulation pulsewidth duration). A corresponding patient-subjective or patient-objective response can be observed. A characteristic electrostimulation intensity relationship can be generated, for example, based on the determined respective at least one of RLS or PLMD response indication threshold amplitude parameters and the plurality of corresponding neural electrostimulation durations. Once this characteristic electrostimulation intensity relationship has been generated, it can then be used to control issuing subsequent neural electrostimulations to the particular patient according to (1) at least one goal and (2) a variable operating point based upon the generated characteristic electrostimulation intensity relationship.

Variable operating point neural electrostimulation such as to treat RLS

Electrostimulation therapy electrode patch

Device for delivery of electrostimulation therapy

Approaches described herein can capture an audio signal using at least one microphone while a user of an electronic device is determined to be asleep. At least one audio frame can be determined from the audio signal. The at least one audio frame represents a spectrum of frequencies detected by the at least one microphone over some period of time. One or more sounds associated with the at least one audio frame can be determined. Sleep-related information can be generated. The information identifies the one or more sounds as potential sources of sleep disruption.

Detecting and measuring snoring

Systems and methods for treating a patient having symptoms of restless legs syndrome (RLS) or Periodic Limb Movement Disorder (PLMD) using high-frequency stimulation by applying a high-frequency pulsed electrostimulation therapy signal to a peroneal nerve or a branch thereof, where the therapy signal is above a motor threshold of a muscle innervated by the peroneal nerve or branch thereof. Surface EMG (sEMG) response to neurostimulation can be used to evaluate patient responsivity to neurostimulation, or to evaluate neurostimulation efficacy, such as to compare various neurostimulation parameter settings and to select between such settings to meet or balance between one or more goals. The sEMG response can be obtained with the muscle at rest, or during muscle activation.

Peripheral nerve stimulation for Restless Legs Syndrome

A system for monitoring or treating RLS or PLMD can include or use a leg-wearable electrostimulation device for acquiring auxiliary data from a subject at a first leg location at which the device is worn, the first leg-wearable electrostimulation device including or using a first neurostimulation delivery device, an impedance sensor configured for determining an impedance via the first and second skin electrodes, and an auxiliary sensor configured for detecting the auxiliary data of the first leg location at which the device is worn.

Peripheral nerve stimulation for RLS or PLMD with sensors for monitoring of sleep-related data

Physiological variables, metrics, biomarkers, and other data points can be used, in connection with a non-invasive wearable device, to screen for, and predict, mental health issues and cognitive states. In addition to metrics such as heart rate, sleep data, activity level, gamification data, and the like, information such as text message and email data, as well as vocal data obtained through a phone and/or a microphone, may be analyzed, provided user authorization. Applying predictive modeling, one or more of the monitored metrics can be correlated with mental states and disorders. Identified patterns can be used to update the predictive models, such as via machine learning-trained models, as well as to update individual event predictions. Information about the mental state predictions, and updates thereto, can be surfaced to the user accordingly.

Determining mental health and cognitive state through physiological and other non-invasively obtained data

Approaches described herein can determine one or more breathing phase patterns over a period of time using audio data captured by at least one microphone. The audio data can include one or more snores. A breathing phase pattern included within the period of time can be determined based at least in part on sensor data captured by one or more sensors in the electronic device. A determination can be made that a first breathing phase pattern represented by the audio data and a second breathing phase pattern represented by the sensor data are correlated. A determination can be made that the first breathing phase pattern represented by the audio data and the second breathing phase pattern represented by the sensor data both correspond to a user wearing the electronic device.

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San Francisco, CA, United States of America

Jonathan David Charlesworth