An Activity-Embedding Approach for Next-Activity Prediction in a Multi-User Smart Space

Abstract

Since the advent of the IoT era, various IoT devices have proliferated, transforming ordinary spaces into smart spaces such as smart home, smart office, and smart building. To provide user-friendly service to people, the majority of previous studies have focused on activity recognition and prediction in singleuser environments such as ambient assisted living (AAL) and activities of daily living (ADL). However, unlike single-user environments, many real world environments are comprised of multiple activities occurring concurrently in a multi-user smart space. In the presence of multiple activities and multiple users, the process of next-activity prediction rarely produces just a single candidate for the next activity. Thus, we present in this paper an approach that generates multiple next- A ctivity candidates. Our approach is motivated by a specific word-embedding algorithm that is typically used for natural language processing (NLP) to map words into a vector space. By using a similar embedding approach in a multi-user smart space, we map activities to vector coordinates in a vector space. After the vectorization, a long short-term memory (LSTM) network can be trained to predict a single vector coordinate for next activity from a group of previously occurring activities; then from this single prediction, multiple next-activity candidates are selected by choosing several vectors near the LSTM's single output. We tested our approach using real data generated from the multi-user smart space testbed on our campus. After the embedding of activities into a vector space, we were able to find semantically meaningful relations between the resultant vectors. In addition, next- A ctivity prediction had a success rate of approximately 82%. Our activity embedding and next- A ctivity prediction method can be utilized together in multi-user smart spaces to develop smart service systems such as a service recommendation system.