Methods and systems implement a pain assessment regularizing system to autonomously observe pained expressions and physiological measurements of a patient, in order to systematically collect data inputs which may be converted to pain assessment factors. The pain assessment regularizing system, by collecting this data, may combine it with clinical appraisals of pain intensity and patient self-reporting of pain intensity, weighing each factor appropriately in a manner sensitive to the progression of a patient care program, so as to lessen confounding effects of subjective pain assessment. The pain assessment regularizing system may generate a time series of regularized pain assessment factors, and further forecast a regularized pain assessment trend. A clinician may further operate the pain assessment regularizing system to review a visualization of both the time series and the forecast, providing the clinician with rigorously sampled and analytically predicted data which cannot be derived through manual and mental efforts.

Disclosed are a multiple physiological data collection device and system that collect, manually mark, sampling—physiological data for machine learning and AI analyses in a single operation background. Physiological data uploaded by sensing devices of different type and function, described in different formation, recorded at different times and/or pertaining to different person can be processed in one system. The invented system comprises a data uploading device, a data storage device and a data edition device and, optionally, an automated data analysis device.

Disclosed are a multiple physiological data collection device and system that collect, manually mark, sampling—physiological data for machine learning and AI analyses in a single operation background. Physiological data uploaded by sensing devices of different type and function, described in different formation, recorded at different times and/or pertaining to different person can be processed in one system. The invented system comprises a data uploading device, a data storage device and a data edition device and, optionally, an automated data analysis device.

There is provided a method for determining dementia risk factors by a server using deep learning. In this instance, the method for determining dementia risk factors includes acquiring biometric information from each subject corresponding to a first control group through a wearable device, acquiring measurement information for each subject corresponding to the first control group, deriving a first dementia risk factor based on the biometric information and the measurement information for each subject, and deriving a second dementia risk factor related to the first dementia risk factor via deep learning performed based on the biometric information related to the first dementia risk factor and control group information.

Various embodiments of systems, devices and methods for improving the accuracy of an analyte sensor and for detecting sensor fault conditions are disclosed. According to some embodiments, these systems, devices, and methods can utilize a first data collected by a glucose sensor and a second data collected by a secondary sensing element. In some embodiments, the secondary sensing element can be one of a lactate sensing element, a ketone sensing element, or a heart rate monitor, among others.

The present specification describes methods and systems for modifying a media, such as Virtual Reality, Augmented Reality, or Mixed Reality (VR/AR/MxR) media based on a vision profile and a target application. In embodiments of the specification, a Sensory Data Exchange (SDE) is created that enables identification of various vision profiles for users and user groups. The SDE may be utilized to modify one or more media in accordance with each type of user and/or user group.

The present specification describes methods and systems for modifying a media, such as Virtual Reality, Augmented Reality, or Mixed Reality (VR/AR/MxR) media based on a vision profile and a target application. In embodiments of the specification, a Sensory Data Exchange (SDE) is created that enables identification of various vision profiles for users and user groups. The SDE may be utilized to modify one or more media in accordance with each type of user and/or user group.

A system includes a communication interface for receiving information that includes data collected from an array of neural activity sensors that were placed on a patient during a session of applied stimuli. A processor is configured to analyze the received information to obtain a frequency spectrum for each sensor for a given stimulus of the applied stimuli. Neural network frequencies that correspond to an indicated impaired functionality of the nervous system of the patient are selected. For each selected frequencies, a spatial map of neural activity is generated. Each of the generated spatial maps is compared with retrieved corresponding spatial maps to identify treatment frequencies from among the selected neural network frequencies. A treatment protocol is generated for input into an electromagnetic field generator to cause the generator to apply to the patient an electromagnetic field at each identified treatment frequency.

A system includes a communication interface for receiving information that includes data collected from an array of neural activity sensors that were placed on a patient during a session of applied stimuli. A processor is configured to analyze the received information to obtain a frequency spectrum for each sensor for a given stimulus of the applied stimuli. Neural network frequencies that correspond to an indicated impaired functionality of the nervous system of the patient are selected. For each selected frequencies, a spatial map of neural activity is generated. Each of the generated spatial maps is compared with retrieved corresponding spatial maps to identify treatment frequencies from among the selected neural network frequencies. A treatment protocol is generated for input into an electromagnetic field generator to cause the generator to apply to the patient an electromagnetic field at each identified treatment frequency.

Disclosed herein are apparatuses for manipulation of an ear device within the ear canal of a subject. Also disclosed herein are methods of using said apparatuses for the insertion, removal, or other manipulations of the ear device. Also disclosed herein are methods of making the apparatuses, and kits containing the apparatus with instructions for use.