Personal lifestyle and health 161014
•Als PPTX, PDF herunterladen•
4 gefällt mir•799 views
Talk at Wearable 2016 Symposium in Lausanne. This presentation talks about use of wearables and other sensors for quantifying lifestyle and relating it to build model of personal health.
Empfohlen
Empfohlen
Weitere ähnliche Inhalte
Ähnlich wie Personal lifestyle and health 161014
Ähnlich wie Personal lifestyle and health 161014 (20)
Mehr von Ramesh Jain
Mehr von Ramesh Jain (20)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
Personal lifestyle and health 161014
- 1. 10/15/2016 1
- 2. Society exists only as a mental concept; in the real world there are only individuals. -- Oscar Wilde
- 3. Wearables: Watches, camera, … I don’t care for money; I care for what money buys. I don’t care for wearables; I care for what wearables do for me.
- 4. Disruption in Healthcare When needed, I go to the source of healthcare. Can healthcare come to me in time?
- 5. Event Mining Machine Learning Individual Model ‘Likely to get severe heart attack in 10 minutes – get him help immediately.’ • Individual Model from data. • Health, social, personal. • Actionable Predictive use. • Better disease models. Healthcare 2020 Objective Self
- 6. Important Revolution in Health In the mid-20th century, the primary causes of death worldwide shifted from infections to chronic conditions.
- 7. Dream: Immortality Problem: Longest people can live is 122 Yrs. Realizable Dream: 100+ Years of Happiness
- 8. Personal Health What’s Lifestyle got to do with it?
- 9. High Blood Pressure Heart Disease Allergies Thyroid Imbalance Diabetes Chronic Fatigue Auto Immune Disease Cancer Stress Poor Diet Lack of Sleep Lack of Exercise Nutrition Deficiencies Genetics Toxins
- 10. Needed: Medical Emancipation Doctor Knows the Best. Patient Knows the Best.
- 11. Smartphone + Wearables := Personalized 24/7 Recording Stethoscope
- 13. Big 3: Lifestyle Factors Physical Activities Popular now: Output and state Food Starting to happen: Input Environmental Factors Coming soon: Surround
- 14. Healthcare Analytics is Data Rich Now Past: Data is expensive and small • Input data is mostly from clinical trials • Models are small since data is limited • Personal information is anecdotal Today: Data is cheap and large • Longitudinal data • Heterogeneous data • Diverse data from Electronic Health Records and wearable/smartphone
- 15. Until recently, you were a folder. Now You are Your Data. Disruption Time
- 16. “Shallow men believe in luck or in circumstance. Strong men believe in cause and effect.” ― Ralph Waldo Emerson
- 17. If you can't measure it, you can't control it! Measure Understand Control Improve
- 18. OBJECTIVE SELF (FUTURE WORK) OBJECTIVE SELF Anecdotal Diarizing data Quantified Self
- 19. Data Streams to Objective Self Daily Life Activities
- 20. Data Streams Data Variety: From Data Streams to Abstracted Event Streams Data Management Event Streams 20
- 21. Event Stream
- 22. DailyLife Activity ATUS: American Time Use Survey
- 23. Daily Life Activities: How we enter them? Chronicle of Daily Life Activities
- 24. We collect diverse signals.
- 25. Running Example 25 Daily Events WalkingMeeting Break Arrive Home Exercise Asthma Attack Pollution Events 91 4 6 10 15 19 Low Temperature Events Increase Suddenly High Decrease SteadilyMedium Low 7 14 Leave Home Framework Heart rate Events NormalHigh Elevated
- 26. Definitions • Time Interval: [∂, ts, te] ∂+ = ts , ∂− = te • Semi Interval: [∂+/−, t] • Point Event (pE): e = (ν, [ E, t]) • Interval Event (iE): e = (v, [E, ts, te]) • Semi-interval Event (sE): e = (v, [E+/−, t]) 26 Life Events WalkingMeeting Break Arrive Home Exercise Asthma Attack 91 4 6 10 15 197 14 Leave Home Framework
- 27. Definitions (Cont.) • Event Stream: ES(i) = {e1 (i), e2 (i), ..., en (i)} • Multi-Event Stream: ES = {ES(1), ES(2), ..., ES(∣I∣)} • Pattern: ρ = (X1 ⊙1 X2 ⊙2 ... ⊙k−1 Xk ) Xi ∈ { pE, iE, sE } and ⊙i ∈ { ; , ;ω∆t , , ∣ } 27 Life Events WalkingMeeting Break Arrive Home Exercise AsthmaAttack 91 4 6 10 15 197 14 Leave Home ( Meeting ; Break) ( Exercise+ ;ω[5] AsthmaAttack) ( Walking ; ArriveHome) Framework
- 28. Representations Laleh Jalali lalehj@ics.uci.edu Interactive Knowledge Discovery from Data Streams 28 Daily Events WalkingMeeting Break Arrive Home Exercise Asthma Attack Pollution Events 91 4 6 10 15 19 1 Low 8 1 0 24 Temperature Events Increase Suddenly High 1 Decrease SteadilyMedium Low 247 1 4 7 14 Leave Home (( Exercise+ Pollution.High) ;ω[5] AsthmaAttack) (( Exercise+ Temp.Low ) ;ω[5] AsthmaAttack) (( Exercise+ (Pollution.High | Temp.Low )) ;ω[5] AsthmaAttack) Framework
- 29. Pattern Mining Operators Sequential Co-occurrence Concurrent Co-occurrence 29 Laleh Jalali lalehj@ics.uci.edu Interactive Knowledge Discovery from Data Streams Framework
- 30. 0.25 0.31 0.47 0.11 0.68 0 0.01 0.33 0.64 0.19 0 0.16 0.52 0.12 0.09 0 0.11 0.67 0.1 0 0.7 0.03 0.52 0.13 0 0.75 0.71 0.23 0.25 0 0.43 0.66 0.1 0.2 0.35 Δt E1 E2 E3 E4 E5 E6 E1 E2 E3 E4 E5 E6 0.88 Co-occurrence Matrix Visualization 0.25 0.31 0.52 0.11 0.23 00.33 0.23 0.4 0.28 0 0.23 0.1 0.45 0 0.11 0.56 0.12 0 0.45 0.4 0.52 0.23 0 0.12 0.23 0.31 0 0.23 0.56 0.1 0.33 0.25 0.280.82 0.82 E1 E2 E3 E4 E5 E6 E1 E2 E3 E4 E5 E6 30
- 31. Cause - Effect Pattern Structure event1 event2 event3 event4 Effect Time lag between events Sequence of events Events in parallel Cause 1 Cause 2 Cause 3 (No medication ; Exercise) Asthma attack (Exercise Pollen high) Asthma attack (Exercise (Pollen high | pollution high)) Asthma attack (Exercise Pollution high) Asthma attack Exercise Asthma attack Δt Formulate and query complex patterns:
- 32. Pattern Mining High level Pattern Formulation Pattern Query … Data Streams Event Streams Semi-interval Event Sequences Data-Driven Analysis Hypothesis-Driven Analysis While air pressure is high, pollution starts increasing gradually, within T time units asthma outbreak happens. ((pollution_inc_steadily ;ωT asthma_outbreak ) || airpressure_stayshigh) Example: Interactive Visualization Interactive Event Mining
- 33. User Interface for Interactive Knowledge Discovery and Model Building Data-Driven Hypothesis- Driven
- 34. Pollution and Meteorological Data
- 35. Asthma Risk Factor Recognition 1) Pollution increases suddenly followed by high wind while temperature increases slightly will cause an asthma Outbreak within 2 days. 2) Thunderstorm followed by temperature decreases steadily will cause an asthma outbreak within 1 day.
- 36. Results Temperature fluctuation has the most impact in the fall and winter seasons and it is not a risk factor during spring or summer During spring and summer, when rain suddenly increases to a very high level, an asthma outbreak is more probable The effect of PM2.5 is not noteworthy in the fall and winter seasons.
- 37. Results (Cont.) • When PM2.5 increases followed by temperature stay high within 3 days, then asthma outbreak is probable. • When wind decreases followed by PM2.5 increases within 5 days, then asthma outbreak is probable. • When rain increases followed by PM2.5 stay low within 4 days then an asthma outbreak is probable.
- 38. • Exposure to polluted air is a risk factor of asthma attack within X hour? Personicle case Environmental factors
- 39. Spicy Indian food and 2 glasses of wine result in severe acidity and sleepless nights. Personicle Food Stream t1 t2 t3 t4 t5
- 40. 40 Act Orient Observe Decide Decision Making in Complex Situations John Boyd Military Strategist OODA
- 41. Situation awareness is knowing what’s going on around you. 41 Observe + Orient = Situational Awareness Orient: Baselines, Goals, and Action Plans Situation awareness is required for every decision in life. Act Orient Observe Decide
- 42. Dashboards Display Data and Information 42 Operators use relevant information to understand situation to decide relevant Action.
- 43. 10/15/2016 43 Calendar PESi FMB (Individual’s Feeling) Accelerometer Location Fitness Data (Nike, Fitbit) Data Ingestion & Aggregation Heart Rate Location (Move) Food Log FMB (People’s Feeling, Location) ESOzone CO2 SO2 PM 2.5 Pollen (Tree, Grass) Air Quality Index Data Ingestion & Aggregation Social Media (News, Tweets) Weather Macro Situation Recognition Predictive Analytics Personal Situation Recognition Persona Asthma Allergy App Server Data Collection MacroSituationPersonalSituation Need and Resources Recommendation
- 44. Great Opportunity: Wearable • Wearable building personal models. • Using personal model to make people aware of their situation. • Informing relevant people about the situation. • Helping people make Right Decision, Right Moment, Right Place.
Hinweis der Redaktion
- sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals, human motions, etc… Not only heterogeneous, and unstructured, but time is a very important dimension. They all generate Events What about frameworks that consume events for knowledge discovery? In particular, the accommodation of time into mining techniques provides a window into the temporal arrangement of events and, thus, introduce the ability to suggest cause and effect that are overlooked when the temporal component is ignored or treated as a simple numerical attribute. Moreover, temporal data mining has the ability to mine the behavioral aspects of a system as opposed to mining rules that describe their states at a point in time. Hence, temporal data mining helps understanding why rather than merely understanding what.
- We can formulate complex patterns using the pattern formulation operators. These patterns will be translated to their corresponding automata
- Fundamentally people can’t glean insights from machine data when they don’t know where to look or what to look for. Goal: Study the problem of event-driven causality from a general and rigorous point of view. Contributions: Design and implementation of a framework that facilitates qualitative causal inference on event-based multimedia data streams. Define meaningful event streams. Define a high level pattern formulation language. Design fast algorithms to find causal patterns. User interfaces that enable analyst to interact smoothly with data, ask better questions, and make better decisions.
- Analyst can import pre-processed data (i.e. event streams) and choose visual bottom-up operators to explore data, generate a basic model and derive a preliminary insight. Then she can seed a hypothesis and grow it step by step using the top-down pattern formulation operators. A good hypothesis is not the one that is necessarily correct, but one that opens up a new path of investigation. In complex problem domains this path cannot be fully perceived in advance. So analyst must be provided with appropriate operators to carry out new analyses based on the original hypothesis. Data Selection Panel: To select different event and event streams from database. Data-driven Operator Panel: These operators are used for pattern mining from event streams. Hypotheses-driven Operator Panel: These operators are used for pattern formulation and pattern query from input events. Visualization panels: Results of pattern mining and pattern query are displayed visually in form of co-occurrence matrices and histograms.
- We have meteorological and pollution data streams. Tweets related to asthma and some burst point in asthma histogram is also shown. How can we understand risk factors from these time-series data?
- We use our awesome framework to study complex risk factor patterns :D