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Robust Features for Activities Recognition

When developing smart home system techniques for predicting a person’s activities most are trained and evaluated by the same group of people. However, in practice, it is difficult and expensive to manually label each activity the participants perform. In this poster, we describe the findings of our initial investigations on simple features that we can use to predict an inhabitant’s activities based on the machine learning model that is trained on activities done by different residents. Our study indicates that ultrasonic sensors’ readings, duration and on/off states can accurately predict (86%) of the other resident’s activities, even when the activities are interleaved with each other.