USING DATA FROM WEARABLE MONITORS TO IDENTIFY COHORTS

• US 20140122496A1
• Filed: 10/22/2013
• Published: 05/01/2014
• Est. Priority Date: 02/16/2007
• Status: Abandoned Application

• Current Assignee: JB IP Acquisitions LLC
• Original Assignee: BodyMedia Incorporated (AliphCom Corp.)
• Inventors: Stivoric, John M., Teller, Eric, Andre, David, Monocello, John A. III
• Application Number:
  US14/060,004
• Publication Number:
  US 20140122496A1
• Time in Patent Office: Days
• US Class Current: 707/740
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/0826 Detecting or evaluating apnoea events A61B5/4818 takes precedence
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/168 Evaluating attention deficit, hyperactivity
• CPC Class Codes: A61B 5/4806 Sleep evaluation A61B5/4821 takes precedence; devices for inducing sleep A61M21/02
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: A61B 5/4815 Sleep quality
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/4833 Assessment of subject's compliance to treatment
• CPC Class Codes: A61B 5/4836 Diagnosis combined with treatment in closed-loop systems or methods A61B5/0036 takes precedence
• CPC Class Codes: A61B 5/6801 specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/6802 Sensor mounted on worn items
• CPC Class Codes: A61B 5/72 Signal processing specially adapted for physiological signals or for diagnostic purposes character recognition, recognising digital ink, document-oriented image-based pattern recognition G06V30/00; recognition or understanding of postal images G06V30/424
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61B 5/747 in case of emergency, i.e. alerting emergency services
• CPC Class Codes: A61B 5/7475 : User input or interface means, e.g. keyboard, pointing device, joystick
• CPC Class Codes: A61M 2021/0027 : by the hearing sense
• CPC Class Codes: A61M 2021/005 : images, e.g. video
• CPC Class Codes: A61M 21/00 : Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
• CPC Class Codes: A61M 21/02 : for inducing sleep or relaxation, e.g. by direct nerve stimulation, hypnosis, analgesia for massage A61H; electrotherapy A61N, e.g. applying alternating or intermittent electric currents for producing anaesthesia A61N1/36021
• CPC Class Codes: G06F 16/24575 : using context
• CPC Class Codes: G06F 16/248 : Presentation of query results
• CPC Class Codes: G06F 16/25 : Integrating or interfacing systems involving database management systems
• CPC Class Codes: G06F 16/285 : Clustering or classification
• CPC Class Codes: G06F 16/38 : Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/90344 : by using string matching techniques
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 5/04 : Inference or reasoning models
• CPC Class Codes: G06Q 30/0242 : Determining effectiveness of advertisements
• CPC Class Codes: G06Q 30/0251 : Targeted advertisements
• CPC Class Codes: G06Q 30/0269 : based on user profile or attribute
• CPC Class Codes: G06Q 30/0271 : Personalized advertisement
• CPC Class Codes: G06Q 30/0277 : Online advertisement
• CPC Class Codes: G06Q 40/08 : Insurance
• CPC Class Codes: G09B 19/00 : Teaching not covered by other main groups of this subclass teaching or practice apparatus for gun-aiming or gun-laying F41G3/26
• CPC Class Codes: G09B 5/00 : Electrically-operated educational appliances working with questions and answers G09B7/00; simulators G09B9/00; advertising or displaying in general G09F
• CPC Class Codes: G16B 40/00 : ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 50/00 : ICT programming tools or database systems specially adapted for bioinformatics
• CPC Class Codes: G16B 99/00 : Subject matter not provided for in other groups of this subclass
• CPC Class Codes: G16H 10/60 : for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients

Abstract

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.

Claims

1. A computer-system-implemented method, 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;

collecting another set of data components of the individual from a source separate from the wearable device;

accessing a database by said computer system comprising data structures of other individuals, wherein said data structures of other individuals are related to known traits of the individuals;

the computer system—

(i) utilizing collected data components from the wearable sensor device to identify activities in which the individual is engaged during the individual'"'"'s daily life;

(ii) assembling a data structure that includes at least one component from the collected data components from the wearable sensor device and at least one of said another set of data components;

(iii) accessing said database to determine a similarity of the data structure with data structures of other individuals; and

(iv) associating individuals with similar data structures into an aggregate data group of individuals who share at least one trait.

2. The method of claim 1, wherein the aggregate data groups are categorized by at least one of activity type, activity level, health state and medical condition.

3. The method of claim 1, wherein individuals are associated with a plurality of aggregate data groups.

4. The method of claim 1, further comprising publishing a publication identifying an individual'"'"'s aggregate data group association.

5. The method of claim 1, wherein the source separate from the wearable device is a user input.

6. 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.

7. 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.

8. 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.

9. 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.