The present disclosure describes clinical workflows, methods, and systems used to perform an indirect to direct subjective refraction to a patient with a mobile smartphone application that works as a encrypted remote administration tool in any portable electronic device and interconnect both systems via by 4G, 5G, 6G, or Wifi. According to various embodiments, an eye doctor may utilize a remote administration tool (RAT) or (RAS) remote access software application on a portable electronic device (PED) (smartphone, tablet, or laptop) to view and control the main control base (MCB) anywhere in the world to interconnect both systems. The eye doctor can perform an on-demand live subjective vision refraction via RAT technology. Furthermore, the eye doctor can control the (MCB) that can control, exam chair, digital phoropter, vision chart software, robotic phoropter arm, exam chair height, exam room lights, and near robotic chart arm anywhere in the world.

Treating kenosis may comprise the following steps: measuring the electrodermal activity of a person by means of an EDA sensor; assessing, on the basis of the electrodermal activity measured, whether the person is currently affected by kinetosis; generating electrical pulses at an electrode in contact with the person's skin in order to treat the kinetosis on the basis of the assessment as to whether the person is currently affected by kinetosis. The sensor and the electrode may be integrated in a device that can be worn on the person's body.

A method including generating, by a state engine from data describing behaviors of users in an environment external to the state engine, an executable process. An agent executes the executable process by determining, from the data describing the behaviors of the users, a problem of at least some of the users, and selects, based on the problem, a chosen action to alter the problem. At a first time, a first electronic communication describing the chosen action to the at least some of the users is transmitted. Ongoing data describing ongoing behaviors of the users is monitored. A reward is generated based on the ongoing data to change a parameter of the agent. The parameter of the agent is changed to generate a modified agent. The modified agent executes the executable process to select a modified action. At a second time, a second electronic communication describing the modified action is transmitted.

An evaluation unit (100) classifies a current treatment situation for a signal connected medical system. A receiving module (110) receives a plurality of treatment signals (112) that indicates a value (116) for a patient parameter (115) and a signal quality. A processing module stores values of patient parameters received at past times with a respective time stamp (125) indicating the time and a quality index (127) assigned to the respective value or group of values and detects a presence of a trigger signal (124) and calculates an evaluation score (134) using a stored calculation rule (132). The calculation uses the stored values of patient parameters as a function of the respective time stamp. The processing module calculates a quality indicator (138) using a stored quality metric (136) depending on the associated quality indices of the values of patient parameters necessary for the calculation of the evaluation score.

A predictive patient health machine learning model is trained based on baseline health data configured as directed graphs. Patient-healthcare system encounter data formed at least in part by electronic medical records (EMRs) is gathered. The patient-healthcare system encounter data is configured as directed graphs to generate graphed health data and the predictive patient health machine learning model is trained on that graphed health data.

Embodiments of a risk assessment and intervention platform architecture are disclosed, where the risk assessment and intervention platform can predict and reduce negative medical outcomes. Embodiments of the risk assessment and intervention platform may include components with controls or interface elements to receive instructions from clinicians to set parameters for interventions and then to enroll, track, and monitor patient activity through the medical treatment plans. Embodiments of a scoring engine are configured based on a specific medical treatment plan and intervention protocols to initiate intervention as needed.

Embodiments of a risk assessment and intervention platform architecture are disclosed, where the risk assessment and intervention platform can predict and reduce negative medical outcomes. Embodiments of the risk assessment and intervention platform may include components with controls or interface elements to receive instructions from clinicians to set parameters for interventions and then to enroll, track, and monitor patient activity through the medical treatment plans. Embodiments of a scoring engine are configured based on a specific medical treatment plan and intervention protocols to initiate intervention as needed.

Disclosed herein is a hair health assessment, treatment, and styling diagnosis and recommendation method and system. The techniques use science and technology in order to aid users in achieving optimal health and beauty of their hair. The present disclosure allows a user to provide hair samples, such as digital images of hair, that can be analyzed using Machine Learning (ML) and Artificial Intelligence (AI) technology to provide recommendations including hair health assessments, hair care regimes, hair care products, and hair care services in an accurate, efficient, and automated manner. The present disclosure may employ a digital image capture device, either stand alone device or an embedded camera of a mobile device or a microscopic attachment, in order to capture digital images of their hair at a smartphone camera magnification level from a selfie image or microscopic-level of magnification, as a hair sample to be analyzed by the system.

Disclosed herein is a hair health assessment, treatment, and styling diagnosis and recommendation method and system. The techniques use science and technology in order to aid users in achieving optimal health and beauty of their hair. The present disclosure allows a user to provide hair samples, such as digital images of hair, that can be analyzed using Machine Learning (ML) and Artificial Intelligence (AI) technology to provide recommendations including hair health assessments, hair care regimes, hair care products, and hair care services in an accurate, efficient, and automated manner. The present disclosure may employ a digital image capture device, either stand alone device or an embedded camera of a mobile device or a microscopic attachment, in order to capture digital images of their hair at a smartphone camera magnification level from a selfie image or microscopic-level of magnification, as a hair sample to be analyzed by the system.

Systems and methods for electronically creating and modifying a fitness plan are disclosed. The method may include receiving electronic user data, collecting electronic fitness data, and displaying a suggestion for a fitness activity based on the electronic user data and the electronic fitness data.