Data-Driven Activity Prediction: Algorithms, Evaluation Methodology, and Applications

Bryan Minor, Janardhan Rao Doppa, Diane J. Cook

Abstract: We consider a novel problem called Activity Prediction, where the goal is to predict the future activity occurrence times from sensor data. In this paper, we make three main contributions. First, we formulate and solve the activity prediction problem in the framework of imitation learning and reduce it to simple regression learning problem. This approach allows us to leverage powerful regression learners; is easy to implement; and can reason about the relational and temporal structure of the problem with negligible computational overhead. Second, we present several evaluation metrics to evaluate a given activity predictor, and discuss their pros and cons in the context of real-world applications. Third, we evaluate our approach using real sensor data collected from 24 smart home testbeds. We also embed the learned predictor into a mobile device based activity prompter and evaluate the app on multiple participants living in smart homes. Our experimental results indicate that the activity predictor learned with our approach performs better than the baseline methods, and offers a simple and reliable approach to prediction of activities from sensor data.