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

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 Human movement is periodic Frequency-based Approaches good for classifying accelerometer data Less expressive for very low sampling rates

Battery Lifetime Vs. Sampling Rate 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

Results For all but extremely low frequency ranges, frequency based features perform superiorly. Optimum sampling frequency of 6 Hz 85% 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 A t 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 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 annotations Error=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 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