WIA 2019 - Enhancing AI in Wearable Devices using Topological Data Analysis
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2019 Women in Analytics Conference Namita Lokare, Ph.D. womeninanalytics.com
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WIA 2019 - Enhancing AI in Wearable Devices using Topological Data Analysis
- 1. LVL © 2019 Enhancing AI in Wearable Devices using Topological Data Analysis Namita Lokare, Ph.D. Sr. Biomedical Algorithm Engineer, LVL Technologies, Inc. Austin, Texas
- 2. LVL © 2019 Dr. Alireza Dirafzoon Turner Richmond Dr. Edgar Lobaton
- 3. LVL © 2019 March 2003 September 2012 September 2018
- 4. LVL © 2019 Head band Ear buds Shoes Chest band Wrist watch Smart textile
- 5. LVL © 2019Gartner, November 2018 STATISTICS
- 6. LVL © 2019 Users by age Female users 50.6%49.4% Male users Statista Global Consumer Survey, July 2018 Users by gender STATISTICS
- 7. Image taken from https://learn.genetics.utah.edu/content/cotton/genome/
- 8. LVL © 2019 Meet Jimmy! Activity Data
- 9. LVL © 2019
- 10. Topological Data Analysis Filtrations are a sequence of increasing subsequences ∅ = #$ ⊆ #& ⊆ ⋯ ⊆ #()& ⊆ *( = K Filtrations produces a natural inclusion map, ∅ = #$ ↪ #& ↪ ⋯ ↪ #()& ↪ K - - - - • Edelsbrunner, H. & Harer, J. “Computational Topology: an Introduction”, 2010 Topological Data Analysis 10
- 11. LVL © 2019 Persistence Diagram Dendrogram Plot Comparing representations that capture the evolution of clusters
- 12. LVL © 2019 Stability of Persistence Diagrams *Cohen-Steiner, David, Herbert Edelsbrunner, and John Harer. "Stability of persistence diagrams." Discrete & Computational Geometry 37, no. 1 (2007): 103-120. ! "#$, "#& ≤ !((*, +)
- 13. How do we generate the point clouds?
- 14. LVL © 2019 Time Delay Embedding Equations to create a Lorenz attractor !" !# = % (' − ") !' !# = " * − + − ' !+ !# = "' − ,+ Chaotic Lorenz attractor
- 15. LVL © 2019 Chaotic time series produced by Lorenz’s first equation Incomplete Measurements?
- 16. LVL © 2019 Chaotic Lorenz attractor in a three dimensional phase Reconstruction of Lorenz attractor embedded in a three dimensional phase space by using only the time series from the first Lorenz equation. Time Delay Embedding
- 17. LVL © 2019 Time Delay Embedding !" # = [&" # , &" # + ) , … , &" # + + − 1 ) ] *Takens, Floris. "Detecting strange attractors in turbulence." In Dynamical systems and turbulence, Warwick 1980, pp. 366-381. Springer Berlin Heidelberg, 1981. *Lorenz, Edward N. "Deterministic nonperiodic flow." Journal of the atmospheric sciences 20.2 (1963): 130-141. *Images are taken from http://www.node99.org/tutorials/ar/
- 18. LVL © 2019 Let’s test it on Jimmy’s data! Let’s put it to use!
- 19. LVL © 2019 Time Delay Embedding on Activity Data
- 20. Persistence Diagram Compute PCs for each joint that explain 95% of the variation Data streams !" # ∶ ℝ → ℝ' from joints Pick 10-second Window Project to PC Directions to obtain (",* # : ℝ → ℝ TDE: Compute ,",* and delay embedding for each - and . Obtain point cloud /",* by subsampling delay embedding TDA: Compute 012 from /",* and maximum persistent interval length 3",* Features 45 = 32,2 … 389,8: are used for classification using k-NN AI Pipeline for Recognition
- 21. LVL © 2019 ! = 0.345 ! = 0.5373 ! = 0.415 ! = 0.6967 Comparing Features between Activities
- 22. LVL © 2019 Visualizing Feature Separation
- 23. LVL © 2019 Performance on Test Data
- 24. LVL © 2019 1 32 4 65 7 8 13246578 1.Row 2.Carry Box 3.Bicycle 4.Rest 5.Set Dinner 6.Walk 7.Type 8.Laying Performance on Test Data
- 25. LVL © 2019 Modifications for real-time recognition
- 27. LVL © 2019
- 28. LVL © 2019
- 29. LVL © 2019 MEASURE WHAT MATTERS LVL © 2017
- 30. LVL © 2019
- 31. LVL © 2019 The elderly can lose up to 1% body water every night, waking up to a state of unrecognized poor hydration
- 32. LVL © 2019 Thank you! Stay LVL Drink for your health
- 33. References • Richmond, T., Lokare, N., & Lobaton, E. (accepted 2017, May). “Robust Trajectory-based Density Estimation for Geometric Structure Recovery,” European Signal and Image Processing Conference (EUSIPCO). • Lokare, N., Benavides, D., Juneja, S., & Lobaton, E. (2016, December). “Hierarchical Activity Clustering Analysis for Robust Graphical Structure Recovery,” IEEE Global Conf. on Signal and Information Processing (GlobalSIP), 2016. • Dirafzoon, A., Lokare, N., & Lobaton, E. (2016, December). “Action Classification from Motion Capture Data using Topological Data Analysis,” IEEE Global Conf. on Signal and Information Processing (GlobalSIP).