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.

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.

Disclosed herein is a gait analysis apparatus that is configured to provide multidimensional measures of the gait of an individual as the individual traverses the gait analysis apparatus. The gait analysis apparatus may be configured to provide a gait measuring processing device with the multidimensional measurements. Based on the multidimensional measurements, the gait measuring process device may, for example, diagnose the test subject with lameness or particular neuromuscular dysfunctions (NM) disease and/or injury, monitor progression of lameness or a particular NM disease and/or injury over time, determine a static weight as the test subject is traversing, monitor the static weight of the test subject over an extended period time, and/or determine which measurements may be used as biomarkers to identify lameness or the particular NM disease and/or injury. A system including a gait analysis apparatus is also disclosed.

Analysis of keystroke dynamics performed by an individual can be used for assessment and monitoring of the individual's motor function. Keystroke events related to a user pressing one or more keys on a keyboard or regions on a touch screen may be analyzed to identify a plurality of distributions of keystroke event intervals. The plurality of distributions may be analyzed to identify one or more features indicative of variation among the distributions and indicative of the user's motor function. Monitoring of a user's motor function may include comparing a value for a feature for one plurality of distribution to a second value for the same feature for another plurality of distributions.

A method of making an electrode for a wearable electronic includes providing an adhesive print media layer. A surface treatment is performed to a top surface of the print media layer. An elastic conductive ink is deposited onto the print media layer. The elastic conductive ink comprises a conductive particulate disposed in a binder. A diffusion bond is formed between the top surface of the print media layer and the elastic conductive ink. The diffusion bond forming is facilitated by the surface treatment and even after the diffusion bond is formed, the top surface of the diffusion bonded electrode remains conductive.

The present disclosure relates to a neuromodulation and/or neurostimulation system comprising at least the following components: at least one sensing unit, at least control unit, at least one stimulation unit, at least one Central Nervous System (CNS) stimulation module, at least one Peripheral Nervous System (PNS) stimulation module, wherein at least one of the components of the neuromodulation and/or neurostimulation system is implantable.