The Jigsaw Continuous Sensing Engine for Mobile Phone Applications!
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1 The Jigsaw Continuous Sensing Engine for Mobile Phone Applications! Hong Lu, Jun Yang, Zhigang Liu, Nicholas D. Lane, Tanzeem Choudhury, Andrew T. Campbell" CS Department Dartmouth College Nokia Research Center Palo Alto
2
3 Accelerometer" GPS" Light Sensor" Compass" Microphone" Camera" Gyroscope"
4 Accelerometer" GPS" Bluetooth" Compass" CO2" Barometer" Microphone" Camera" Gyroscope" EEG Sensors"
5 However "
6 Pipeline Overview" Interpret" Observe" Admission" Control" Features" Classifier" Actions"
7 Pipeline Overview" Interpret" Main challenges of continuous sensing and inference " on mobile phone: " - classification accuracy under various phone context" - resource efficiency"
8 Robust Motion Based Physical Activity Classification Problems: Calibration, Uncontrolled phone context " specialized sensor" fixed placement"
9 Robust Motion Based Physical Activity Classification Problems: Calibration, Uncontrolled phone context " In the real life "
10 One activity, Two distinct patterns Accelerometer samples of cycling while phone " (A) in the pant pocket, (B) in the backpack"
11 Jigsaw Accelerometer Pipeline" Auto Calibration" Projection " & Filtering" Transition detection" Projection" 25 Features" Split-and-merge" Classification" Classifier" Temporal smoothing"
12 Jigsaw Accelerometer Pipeline" Auto Calibration" Transition detection" The calibration process " provide parameters for normalization " - Sensitivity (scaling factor), K" - Offset, b" hide hardware variance" ensure the data quality" Projection" 25 Features" Classifier" Temporal smoothing"
13 Jigsaw Accelerometer Pipeline" Auto Calibration" Transition detection" Capture stationary moments" Use linear least square estimator" to calculate" g - Sensitivity" 2 x + g 2 y + g 2 z =1 - Offset " Projection" 25 Features" Classifier" Temporal smoothing"
14 Jigsaw Accelerometer Pipeline" Auto Calibration" Transition detection" Calibration Results" measurement error" - without calibration 5% ~15%" - user driven 0.55%" - auto calibration " 0.76% N95" 0.58% iphone 3G " Projection" 25 Features" Classifier" Temporal smoothing"
15 Jigsaw Accelerometer Pipeline" Gravity Estimation" & Projection" Transition detection" Projection" Estimate gravity direction by long" term average" Discard transition activity" Project samples to global coordinate " 25 Features" Classifier" Temporal smoothing"
16 Jigsaw Accelerometer Pipeline" Split one activity into subclasses (different body positions)." Merge the inference result back" After inference. " Split-and-merge" Classification" Transition detection" Projection" 25 Features" Classifier" Temporal smoothing"
17 Jigsaw Accelerometer Pipeline" Transition detection" Projection" 25 Features" Split-and-merge" Classification" Classifier" Temporal smoothing"
18 Jigsaw Accelerometer Pipeline" Auto Calibration" Projection " & Filtering" Transition detection" Projection" 25 Features" Split-and-merge" Classification" Classifier" Temporal smoothing"
19 Jigsaw Accelerometer Pipeline" Results:" 16 people" 5 body positions"
20 Jigsaw Accelerometer Pipeline" CPU Usage:" 1 ~ 3 % "0.9 ~ 3.7%"
21 Light Weight Audio Classification Main Problem: Computational Efficiency" High data rate " CPU usage on iphone 3G"! Playing 256kbps AAC, ~8%"! Audio classification using Gaussian Mixture Model (GMM) classifier " - 10 activities, ~20%" - 20 activities, ~35% "
22 Jigsaw Audio Pipeline" Raw Waveform" Admission Control & Duty Cycling" Acoustic Features" Sound Detection " & Duty Cycling" Feature Extraction" Voice Classification" Other" Voice Detection" Voice" Temporal Smoothing" GMM" Classifier" Activity Sound" Classification" Smoothing" Inference Result"
23 Jigsaw Audio Pipeline" Admission Control & Duty Cycling" Sound Detection & Duty Cycling" Acoustic Features" sound Voice Classification" Other" sound Voice" low duty cycling continuous sensing GMM" Classifier" silence silence Temporal Smoothing"
24 Jigsaw Audio Pipeline" Voice Detection" Admission Control & Duty Cycling" Acoustic Features" Voice Classification" Other" Voice" GMM" Classifier" Temporal Smoothing"
25 Jigsaw Audio Pipeline" Voice Detection" Admission Control & Duty Cycling" Acoustic Features" Voice Classification" Other" Voice" GMM" Classifier" Temporal Smoothing"
26 Jigsaw Audio Pipeline" Activity Sound Classification" 20-MFCCs feature " GMM classifier" Admission Control & Duty Cycling" Acoustic Features" Voice Classification" Other" Voice" GMM" Classifier" Temporal Smoothing"
27 Jigsaw Audio Pipeline" Admission Control & Duty Cycling" Acoustic Features" Voice Classification" Other" Voice" GMM" Classifier" F = [f1, f2, f3, fn]" label = brushing teeth " Temporal Smoothing"
28 Jigsaw Audio Pipeline" Admission Control & Duty Cycling" Acoustic Features" Voice Classification" Other" Voice" Temporal Smoothing" GMM" Classifier" F = [f1, f2, f3, fn]" label = brushing teeth "
29 Jigsaw Audio Pipeline" Admission Control & Duty Cycling" Acoustic Features" Voice Classification" Other" Voice" Temporal Smoothing" GMM" Classifier" F = [f1, f2, f3, fn ]" cosine distance" F = [f1, f2, f3, fn]" label = brushing teeth "
30 Jigsaw Audio Pipeline" Admission Control & Duty Cycling" Acoustic Features" Voice Classification" Other" Voice" Temporal Smoothing" GMM" Classifier" F = [f1, f2, f3, fn ]"!" F = [f1, f2, f3, fn]" label = washing hands "
31 Jigsaw Audio Pipeline" Admission Control & Duty Cycling" Acoustic Features" Voice Classification" Other" Voice" GMM" Classifier" F = [f1, f2, f3, fn ]" label = washing hand " Temporal Smoothing"
32 Jigsaw Audio Pipeline" Admission Control & Duty Cycling" Acoustic Features" Voice Classification" Other" Voice" Temporal Smoothing" GMM" Classifier" F = [f1, f2, f3, fn ]" label = washing hand "
33 Jigsaw Audio Pipeline" Admission Control & Duty Cycling" Acoustic Features" Voice Classification" Other" Voice" Temporal Smoothing" GMM" Classifier" F = [f1, f2, f3, fn ]" label = washing hand "
34 Jigsaw Audio Pipeline" Admission Control & Duty Cycling" Acoustic Features" Voice Classification" Other" Voice" Temporal Smoothing" GMM" Classifier" F = [f1, f2, f3, fn ]" label = washing hand "
35 Jigsaw Audio Pipeline" Admission Control & Duty Cycling" Acoustic Features" Voice Classification" Other" Voice" Temporal Smoothing" GMM" Classifier" CPU benchmark:" - similarity measure 0.02ms" - 7-class GMM classifier ~11ms"
36 Jigsaw Audio Pipeline" Admission Control & Duty Cycling" Voice" Acoustic Features" Voice Classification" Temporal Smoothing" GMM" Classifier" Other" Tuning the similarity threshold"! a tight threshold" - invoke GMM more frequent" - more accurate but costly"! a loose threshold" - invoke GMM less often" - more efficient by less accurate" F = [f1, f2, f3, fn ]" threshold" F = [f1, f2, f3, fn]" label = washing hands "
37 Jigsaw Audio Pipeline" Admission Control & Duty Cycling" Acoustic Features" Voice Classification" Other" Voice" GMM" Classifier" Temporal Smoothing"
38 Jigsaw Audio Pipeline" Result" Classification Accuracy for different activity sounds"
39 Jigsaw Audio Pipeline" CPU Usage:" 7 ~ 17 % "6 ~ 15%"
40 Jigsaw Adaptive GPS Sensing Main challenge: battery efficiency"
41 Jigsaw Adaptive GPS Sensing Main challenge: battery efficiency"
42 Jigsaw Adaptive GPS Sensing Hard to design a fixed duty cycle scheme that works for everyone" by Google Ridefinder"
43 Jigsaw Adaptive GPS Sensing The goal: fine tune the GPS sampling interval by: " 1. Battery budget 2. Tracking Duration 3. User s mobility pattern"
44 Jigsaw Adaptive GPS Sensing Markov Decision Process (MDP) based optimization." battery drain model" time trans model" MDP optimizer" mobility trans model" Action = {a sample every interval(a),1 a 6}, " for a = 1, 2..., 6, the interval(a) is 20min, 10min, 5min, 2min, 1min, 5s."
45 Jigsaw Adaptive GPS Sensing Markov Decision Process (MDP) based optimization." - Reward function," - Policy Iteration algorithm is used to optimize the total reward" over the time duration T."
46 Jigsaw Adaptive GPS Sensing The output of MDP is a policy table. For each (energy level, time tick, mobility level) tuple there is a corresponding sampling interval. "
47 Jigsaw Adaptive GPS Sensing Jigsaw Adaptive GPS Sensing in Action "
48 Jigsaw Adaptive GPS Sensing Evaluation" Mobility State Distribution in Location Traces"
49 Jigsaw Adaptive GPS Sensing 20min" 10min" 5min" 4min" 3min" 2min" 1min" 5s" Error vs. Power Tradeoff for Weekday Traces"
50 Summary Jigsaw addressed the robustness and efficiency challenges of phone sensing and inference. It provides:" robust accelerometer activity classification" computational efficient audio classification" location tracking adaptive to battery budget, tracking duration, and user s real-time mobility." Future Work" add more sensing modalities" high level inference combining multiple modalities" port to Google Android platform"
51
52 Cheers!" Questions?"
The Jigsaw Continuous Sensing Engine for Mobile Phone Applications
The Jigsaw Continuous Sensing Engine for Mobile Phone Applications Hong Lu, Jun Yang, Zhigang Liu, Nicholas D. Lane, Tanzeem Choudhury, Andrew T. Campbell Dartmouth College, {hong,niclane,campbell}@cs.dartmouth.edu,{tanzeem.choudhury}@dartmouth.edu
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