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TRADING OFF PREDICTION ACCURACY AND POWER CONSUMPTION FOR CONTEXT-AWARE WEARABLE COMPUTING Presented By: Jeff Khoshgozaran
Motivation
Wearable devices sensing user information Context-Aware Mobile Computing
Previous work Power consumption in full power mode
Quickly depletes a critically constrained resource High sampling rate to provide accuracy
Computational and space-intensive solutions Lack of scalability for knee and hip-worn sensors
eWATCH
A context-aware wearable platform Several sensors including two-axis accelerometer Three power states for sensing and classifying data
Full Power Active CPU, active peripherals (~ 30 ms)
Idle State Core clock turned off, active peripherals Waiting for the next sample For SR=6 Hz, time interval~166ms
Low Power State is active most of the time Inactive CPU and peripherals Active real-time clock Scheduling next wake up Using selective sample algorithm
www.chronicle.pitt.edu, http://www.cmu.edu/
Time/Frequency-Domain-Based Classification
5 second windows for computing features Time-based
Using means, variances, median, etc. Frequency-based
Using FFT on values of both accelerometer axes separately
http://www.seas.gwu.edu/~ayoussef/cs225/
Human movement is periodic
Frequency-based Approaches good for classifying accelerometer data
Less expressive for very low sampling rates
Battery Lifetime Vs. Sampling Rate
8.17.6
More costly computation and more dimensions
While important, computation is not the dominant factor in reducing energy consumption
Using SVM for Classification
A multi-class SVM used for actual classification Detect and exploit complex patterns in data Good for representing complex patterns Good for excluding unstable patterns (= overfitting) Computationally expensive training Very efficient classification (hardware friendly) Guassian Radial Basis Function used as kernel to
classify non-linear data The class of kernel methods implicitly defines the class of
possible patterns by introducing a notion of similarity between data
Implicit and non-linear embedding of data in high-dimensional spaces
Separated by a hyperplane in feature space
Power-optimized Classification Experiments
Training data captured by 3 test participants
Each activity recorded for 10 minutes Data was split into different recorded activities Data was partitioned into blocks of 5 seconds Used to extract time/frequency domain features Labeled examples used for training multi class
SVM Prediction accuracy & power consumption
computed
Running/J oggingWalkingStandingSitting/WorkingClimbing/Descending Stairs
Results
For all but extremely low frequency ranges, frequency based features perform superiorly.
Optimum sampling frequency of 6 Hz85% Increase
Selective Sampling vs. Prediction Accuracy
What: Further reduce energy consumption How: Selective Sampling Why: Human activity: a continuous process
Person more likely to continue an activity than to change to another at a point in time
Selective sampling schedules classification Reduces number of observations Saving energy from continuous monitoring to few points in
time Objective: keeping accuracy as high as possible
At is the user’s activity at time t
Selective Sampling (cont.)
Select a set of observation times to maximize correct prediction of user’s activity for times when no sampling/classification is made
Minimize the expected loss:
Conditional plans
Maximum # of observations
Expected loss over all activity sequences a
Selected observation times for a
Minimize uncertainty
: Sequence of decisions: depending on observations so far, decides when next observation should be made
Entropy and dynamic programming used to find optimal
4 Schemes to Select the Conditional Plan
Uniform Spacing Selects observation times at equally spaced intervals
Random Spacing B random length observation times selected at random
Exponential Backoff Maintains a maximum step size ∆max
If cur. act=last detected act., multiply ∆max by α else ∆max=1
Actual step size ∆ chosen uniformly at random from [1, ∆max]
Next observation made at t+ ∆ Entropy-based
Minimizing uncertainty using the entropy criterion Taking transition probabilities of states into account
More frequent sampling for activities with short durations
Selective Sampling Experiments
Four new objects performing hour-long activities Subjects were indirectly asked to perform
representative tasks at random times User activities manually annotated by an
observer Resulting in <activity,duration> pairs sampled at
6Hz Data then partitioned into sequences of 5
seconds These blocks labeled with annotations and
classified using pre-trained classifiers in the frequency domain
Results Continuous Sampling
Competitive for low
frequencies
Factor of 2 improvement
Using annotated data as “exact classification”No SVM (focusing on sampling)
Using classifier output instead of annotationsError=Sampling + Classification
Overall error dominated by classification from SVM and not by sampling
Classification accuracy lower than previous experiments due to 1.new subjects 2. noisy real-world environment
Roughly similar behavior to above experiment
@ 6Hz factor of 2.5 improvement
Conclusion
High efficiency and accuracy for low range frequency of 1-10 Hz.
Competitive classification accuracy for the highly erratic and ambiguous (but convenient) wrist-based sensing
Four selective sampling strategies to further reduce the resource usage
Comments
Using FFT for each dimension separately looses the correlation of among dimensions
Semi-controlled user behavior for test data generation
Authors assume continuous state change in a close set of predefined activities i.e., at any given time, one of these activities are taking place