A Multi Agent Approach to Facilitate the Identification of Interleaved Activities

This paper presents a Multi-agent approach to identifying interleaved activities in a smart environment. The use of binary contact sensors was explored to identify Activities of Daily Living with assistance from a system made up of agents. Activities were identified when an activity trigger event was detected. Upon detection, a time window would activate around the trigger event, prompting the activity agents to identify which of their events were present within the set time window, thus enabling them to calculate a percentage of likeliness that the activity was their own. As a result, the highest percentage of activity matches would be displayed as having occurred. To evaluate this approach, 36 interleaved activities were processed and compared with a single agent system in addition to 28 non-interleaved activities. As a benchmark, the results were compared to that of another study. Results presented a precision, recall and F-measure of 0.69, 0.81 and 0.74. This paper concluded that the Multi Agent System (MAS) is a promising approach for identifying interleaved activities when compared to methods that fail when presented with data that is not in a set order. However, several limitations are present which need to be overcome to make the results more accurate when compared to other approaches.