SYSTEMS AND METHODS USING A WEARABLE DEVICE TO PREDICT THE INDIVIDUALS TYPE AND A SUITABLE THERAPY REGIME
The methods and systems described herein may involve determining at least one lifeotype of at least one individual, analyzing the at least one lifeotype, and delivering content to at least one individual based on the analysis. The methods and systems described herein may involve providing a game, determining at least one lifeotype of at least one player of the game, analyzing the at least one lifeotype, and affecting the game play based on the analysis. The methods and systems described herein may involve providing an interactive space, determining at least one lifeotype of at least one individual in the space, analyzing the at least one lifeotype, and modifying at least one attribute of the space based on the analysis.
1 . A computer-system-implemented method of recommending a therapy regime for an individual, the computer system having at least one programmed processor to implement the method, the method comprising:
continuously collecting data components of an individual from a wearable sensor device;
the computer system—
(i) assembling a data structure that includes at least a plurality of the components from the collected data components from the wearable sensor device;
(ii) accessing a database having data components for other individuals to determine at least one of a similarity and a match of the data structure for the individual with data structures of other individuals;
(iii) based on the determination, determining a type for the individual; and
(iv) based on the type and the individual's collected data components, recommending a therapy regimen for the individual.
2 . The method of claim 1 , further comprising publishing the recommended therapy regimen.
3 . The method of claim 1 , wherein the data source separate from the wearable device is a user input.
4 . The method of claim 1 , wherein said another set of data components is selected from the group consisting of: derived data, analytical status data, contextual data, continuous data, discrete data, time series data, event data, raw data, processed data, metadata, third party data, physiological state data, psychological state data, survey data, medical data, genetic data, environmental data, transactional data, economic data, socioeconomic data, demographic data, psychographic data, sensed data, continuously monitored data, manually entered data, inputted data, continuous data and real-time data.
5 . The method of claim 1 , wherein said set of data components from a wearable sensor device is selected from the group consisting of: derived data, analytical status data, contextual data, continuous data, discrete data, time series data, event data, raw data, processed data, metadata, third party data, physiological state data, psychological state data, survey data, medical data, genetic data, environmental data, transactional data, economic data, socioeconomic data, demographic data, psychographic data, sensed data, continuously monitored data, manually entered data, inputted data, continuous data and real-time data.
6 . The method of claim 1 , wherein at least one data component collected from a wearable sensor device is a data component that is derived from a plurality of sensors that is distinct from the output of any single sensor.
7 . The method of claim 1 , wherein the assembled data structure that includes at least one component from the collected data components from the wearable sensor device includes at least one data component collected from a wearable sensor device is a data component that is derived from a plurality of sensors that is distinct from the output of any single sensor.