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The human activity recognition in the IoT environment plays the central rolein the ambient assisted living, where the human activities can be representedas a concatenated event stream generated from various smart objects. From theconcatenated event stream, each activity should be distinguished separately forthe human activity recognition to provide services that users may need. In thisregard, accurately segmenting the entire stream at the precise boundary of eachactivity is indispensable high priority task to realize the activityrecognition. Multiple human activities in an IoT environment generate varyingevent stream patterns, and the unpredictability of these patterns makes theminclude redundant or missing events. In dealing with this complex segmentationproblem, we figured out that the dynamic and confusing patterns cause majorproblems due to: inclusive event stream, redundant events, and shared events.To address these problems, we exploited the contextual relationships associatedwith the activity status about either ongoing or terminated/started. Todiscover the intrinsic relationships between the events in a stream, weutilized the LSTM model by rendering it for the activity segmentation. Then,the inferred boundaries were revised by our validation algorithm for a bitshifted boundaries. Our experiments show the surprising result of high accuracyabove 95%, on our own testbed with various smart objects. This is superior tothe prior works that even do not assume the environment with multi-useractivities, where their accuracies are slightly above 80% in their testenvironment. It proves that our work is feasible enough to be applied in theIoT environment.

Contextual Relationship-based Activity Segmentation on an Event Stream in the IoT Environment with Multi-user Activities