Abstract: A method for generating a report on medication prescription for treating a condition of a subject that affects attention: it includes receiving movement data of said patient: categorizing the movement data: receiving profile information of said subject: transmitting a query to a database of profiles of subjects having received a prescription for improving attention, wherein the query includes a plurality of criteria based on the profile information of the subject and the fidgeting data: receiving the profiles of subjects having received a prescription for improving attention corresponding to the query: and generating a prescription recommendation in accordance with one or more prescriptions indicated for the received profiles of subjects.

Abstract: The system and method disclosed collects user reported, self-monitored On-task/Off-task Behavior, Fidgeting Behaviors and Walking/Running behaviors as quantified by motion sensors and an intelligent scheduling system. The collected data tells the reminder device what environment a user is scheduled to be in at any point in time in order to appropriately collect behavioral information and use said information to encourage users to be mindful of their own actions and behaviors in order to increase time spent on-task.

Abstract: The system and method disclosed utilizes a wearable device to collect data generated pursuant to user's kinesthetic movement. The instant innovation filters and cleans the data, then slices the data into subsets. The subsets are analyzed with a Machine Learning algorithm to identify signal patterns indicative of statistically significant behavioral metrics, and the instant innovation returns insights based in part on relationships between said behavioral metrics. These insights are returned to an observer in the form of a report.

Abstract: The system and method disclosed collects user reported, self-monitored On-task/Off-task Behavior, Fidgeting Behaviors and Walking/Running behaviors as quantified by motion sensors and an intelligent scheduling system. The collected data tells the reminder device what environment a user is scheduled to be in at any point in time in order to appropriately collect behavioral information and use said information to encourage users to be mindful of their own actions and behaviors in order to increase time spent on-task.

Abstract: The system and method disclosed collects user reported, self-monitored On-task/Off-task Behavior, Fidgeting Behaviors and Walking/Running behaviors as quantified by motion sensors and an intelligent scheduling system. The collected data tells the reminder device what environment a user is scheduled to be in at any point in time in order to appropriately collect behavioral information and use said information to encourage users to be mindful of their own actions and behaviors in order to increase time spent on-task.

Abstract: The system and method disclosed utilizes a wearable device to collect data generated pursuant to user's kinesthetic movement. The instant innovation filters and cleans the data, then slices the data into subsets. The subsets are analyzed with a Machine Learning algorithm to identify signal patterns indicative of statistically significant behavioral metrics, and the instant innovation returns insights based in part on relationships between said behavioral metrics. These insights are returned to an observer in the form of a report.

Abstract: The system and method disclosed collects user reported, self-monitored On-task/Off-task Behavior, Fidgeting Behaviors and Walking/Running behaviors as quantified by motion sensors and an intelligent scheduling system. The collected data tells the reminder device what environment a user is scheduled to be in at any point in time in order to appropriately collect behavioral information and use said information to encourage users to be mindful of their own actions and behaviors in order to increase time spent on-task.