trading off prediction accuracy and power consumption for context- aware wearable computing...

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