Embodiments herein relate to ear-wearable devices configured to detect patterns indicative of an occurrence, prodrome or sequelae of an anoxic or hypoxic neurological injury. In an embodiment, an ear-wearable device is included. The ear-wearable device can be configured to monitor signals from a microphone and/or a motion sensor to detect a pattern or patterns indicative of an occurrence of an anoxic or hypoxic neurological injury. In some embodiments, a method of monitoring an ear-wearable device wearer for an occurrence of an anoxic or hypoxic neurological injury is included. The method can include gathering signals from one or more of a microphone, a motion sensor, or another sensor of an ear-wearable device and monitoring the signals to detect a pattern or patterns indicative of an occurrence of an anoxic or hypoxic neurological injury. Other embodiments are also included herein.

An apparatus for monitoring a user's health and/or sleep and/or the efficacy of a treatment (e.g. a sleep treatment or other type of health treatment) can include use of a wearable electronic device. The device can include an array of sensors for collecting user data. The user data can be used by the device to evaluate criteria related to the user's health to monitor efficacy of a treatment. In addition, or alternatively, the collected data can be transmitted to a central server and/or input/output device for evaluating different criteria for monitoring the user's health and/or sleep as well as the efficacy of a treatment being provided to the user.

The present disclosure provides methods and devices for assessing movement. In some exemplary embodiments, a system for assessing movement is provided. In some embodiments, the system includes a handheld device. In some embodiments, the handheld device includes a plurality of sensors configured to record motion and position data, a body having a plurality of sides, a battery, and a proximal end configured to interact with a touchscreen of a computing device.

A movement-assessment device and methods for using the testing device includes a peg board device having a plurality of apertures on a top surface, and a plurality of photo-optical gate sensors. A computing device, comprising a touchscreen interface, communicates with the peg board device.

A tremor suppression device includes a garment wearable on an anatomical region and including electrodes contacting the anatomical region when the garment is worn on the anatomical region, and an electronic controller configured to: detect electromyography (EMG) signals as a function of anatomical location and time using the electrodes; identify tremors as a function of anatomical location and time based on the EMG signals; and apply neuromuscular electrical stimulation (NMES) at one or more anatomical locations as a function of time using the electrodes to suppress the identified tremors.

The present invention falls within the field of medical diagnosis, more particularly in the field of computational methods for headache diagnosis. In short, the invention consists of an intelligent headache management system that comprises a plurality of remote modules connected with at least one analysis module, the remote modules being responsible for the collection of information related to the user and said information collection is carried out by at least one information collection device connected to said remote module. The information is transmitted to an analysis module, which comprises means to store and process the information transmitted by the remote module. The results from the computational processing of said information are transmitted to at least one remote module, being available to the user. The remote module comprises information collection devices for the collection of information actively provided by the user and for the automatic collection of information from the user and comprises stimulus devices for the emission of sensory stimuli to provoke biological responses from the user.

An action state estimation apparatus is provided that includes a sampling portion, an action state model storage, and an estimation calculation portion. The sampling portion samples a displacement measurement signal within a predetermined time and generates displacement measurement data based on the sampled displacement measurement signal. The action state model storage stores an action state model modeled by associating the displacement measurement data with a loaded state of a muscle of the test subject. The estimation calculation portion then estimates the loaded state by setting the displacement measurement data as an input vector and using the action state model.

The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.