Explainable Recommendations for Wearable Sensor Data

This paper presents an approach to explore sensor data and learn rules based on the patterns detected in the data. Our approach is a direct modification of the Apriori algorithm with a lookback mechanism that allows us to consider specific temporal windows. The inferred knowledge can be used to provide users with predictions based on historical data as well as personalized, explainable recommendations towards achieving a goal.