Vikramaditya R. Jakkula, G. Michael Youngblood and Diane J. Cook AAAI ’06 Workshop on Computational Aesthetics July 16, 2006.

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Presentation transcript:

Vikramaditya R. Jakkula, G. Michael Youngblood and Diane J. Cook AAAI ’06 Workshop on Computational Aesthetics July 16, 2006

 UN report also predicts the number of people 60 and over will triple, increasing from 606 million in 2000 to nearly 1.9 billion by 2050  Medicare home health agency benefit payments increased between 2004 and 2005 from $10.5 billion to $12.5 billion  The need for, smart and cost effective, in-home health monitoring technology to replace the existing home caregivers arises.  Early prediction based on the observations would aid to an improved quality of life at home.

 The evaluation environment is a student apartment with a deployed Argus and X-10 network.  There are over 150 sensors deployed in the MavPad that include light, temperature, humidity, and switches.

Example Sensor Readings Includes Motion, Temperature, Light, Humidity Example motion on bed distance traveled at home Example Instrumental Activities X-10 based activities Low Level Metrics Mid-Level Metrics High- Level Metrics

 First: we look for correlations and perform t-tests to find any similarities among the classification metrics.  Second: we use the machine learning technique, the k-nearest neighbor algorithm, to predict the state of wellness the inhabitant will experience on the following day.

Experiment One Results: T-test and correlations on collected metrics. T-test and correlations on collected metrics.

Figure 2: Comparison of Upper, Lower and Lowest body motion in bed.

Figure: 3 Comparison of number of visits to the bathroom, closet and kitchen.

Figure: 4 Comparison of Distance traveled in home, calorie intake and monitored pulse.

Figure 5: Comparison of Happy Vs Health state of Inhabitant.

Figure 6: Comparison of X10 readings and sensor firing which constitutes the high level metrics.

Experiment Two Results: Prediction Using K-Nearest Neighbor. Prediction Using K-Nearest Neighbor.

 Level of happiness classified into three (yes– happy, ok–mediocre, no–not happy) on scale of values 6 to 10 to be yes, 5 to be ok, and below 5 is no.  Used Weka, application of K-Nearest Neighbor and other techniques for comparison.  k-nearest neighbors’ out performs other learning techniques by accuracy of % and with error of %

Table 11: Sample predictions on test split.

Learning AlgorithmAccuracy (%) J % IB % SVM (SMO)65% KNN % Table 12: Comparison of Accuracy of prediction techniques.

 A simple sensor network in smart home can be used to detect lifestyle patterns.  Performance of the classifier would be expected to improve given a larger collected dataset.  The k-nearest neighbor technique performed better than other commonly used techniques.

 Datasets still have many interesting findings.  Continue Collecting Data for a longer time period.  Plans to address the problem of automating the SF-36® health survey form.

Thank You