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A Practical Approach to Recognizing Physical Activities Jonathan Lester, Tanzeem Choudhury, and Gaetano Borriello In Proceedings of the Fourth International Conference on Pervasive Computing (2006) Benjamin Stokes, Presenter -- 1/24/11 For CS 546: Intelligent Embedded Systems
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The Challenge Personal activity recognition (in highly constrained use contexts) Healthcare sector: demand growing, currently relies on paid observer or self- reporting; Deficiencies: cost, accuracy, scope, coverage and obtrusiveness.
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Proposed Solution To investigate three pressing constraints: 1.Unpredictable sensor location (wrist, waist, shoulder) 2.Minimal training across individuals 3.Cost (and sensor) minimizing
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Technical Preview
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Related Links MSP Research Initiative @ University of Washington & Intel Research Seattle: http://seattle.intel-research.net/MSP/ http://seattle.intel-research.net/MSP/ Wiki for the Mobile Sensing Platform http://ubi.cs.washington.edu/wiki/index.php/Main_Page http://ubi.cs.washington.edu/wiki/index.php/Main_Page Jonathan Lester research page (with details on the Mobile Sensing Platform) http://www.cs.washington.edu/homes/jlester/research.html http://www.cs.washington.edu/homes/jlester/research.html –Includes a great white paper on their MSP design justification and experience
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Experimental Design 12 volunteers given a sequence of activities over several days (observer annotates true event) 8 different activities (sitting, standing, walking, walking up/down stairs, riding elevator, brushing teeth – selected as useful for elder care) Recognition trained & tested via activity classification algorithm developed earlier
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Technical Design
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Available Sensors
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Raw Data (12 hrs gathered) (Data from 2nd Data Set)
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Data Preprocessing 18,000 samples of data per second so must summarize… Result: 4 Hz @ 651 features
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Analysis Stage: Classification Window: 15 second sliding (decreases error; reveals transitions) with 5 second overlap For each activity… True activity type is observed by a human All events of that type are divided into 4 “folds” (for training/testing, i.e., 3 :1)
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Investigating Sensor Location Several options: 1. Any location (of three) = ideal …or… 2.Shoulder only 3.Waist only 4.Wrist only
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Learning Model Often, learning models are either: a)discriminative: to learn the class boundaries without regard for densities b)generative: to learn the class densities …they have a mix. Specifically: 1.Top 50 features as most discriminating (< 10%) 2.To recognize activities, Hidden Markov Models (HMMs – i.e., a simple Bayesian network); includes “temporal smoothing” (Image Source: Wikipedia)
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Confusion Matrix (for the “location independent” condition) Precision/recall
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Variation Across Users Can anyone benefit? How much training? Train on 1-12 users (folds 3:1), test on all 12 Approaches 80% accuracy if testing on outsiders (approaches 95% accuracy if tested within group)
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Fewer sensors possible? Which are most important? Accelerometer (motion of user) Audio (changing environment) Barometric pressure (env.; in GPS for altitude) Compare best sensor : top three (all locations) 38.96% recall : 81.38% …so use three!!
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Findings Summary Investigated… (1) location sensitivity, Can recognize context within our constraints! (and works across locations) (2) variations across users, Can be pre-trained by other individuals. (3) which sensor modalities. Can use fewer & cheaper sensors.
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Critiques, Future Research Curious: Defensive about dual analysis techniques? Limitation: Excluded unclassified activities (Overlooks 5 of 12 hours low ambiguity tolerance.) Conceptual need: meta-classification to connect activities (e.g., “making the rounds in hospital”) Suggestion: Cluster population groups for performance. (Here it was just “healthy individuals.”) Suggestion: Consider time series data? (e.g., sitting typically followed by standing, which precedes walking)
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Related Links MSP Research Initiative @ University of Washington & Intel Research Seattle: http://seattle.intel-research.net/MSP/ http://seattle.intel-research.net/MSP/ Wiki for the Mobile Sensing Platform http://ubi.cs.washington.edu/wiki/index.php/Main_Page http://ubi.cs.washington.edu/wiki/index.php/Main_Page Jonathan Lester research page (with details on the Mobile Sensing Platform) http://www.cs.washington.edu/homes/jlester/research.html http://www.cs.washington.edu/homes/jlester/research.html –Includes a great white paper on their MSP design justification and experience
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