Improving the accuracy of erroneous-plan recognition system for Activities of Daily Living
Using ambient intelligence to assist people with dementia in carrying out their Activities of Daily Living (ADLs) independently in smart home environment is an important research area, due to the projected increasing number of people with dementia. We present herein, a system and algorithms for the automated recognition of ADLs; the ADLs are in terms of plans made up encoded sequences of micro-context information gathered by sensors in a smart home. Previously, the Erroneous-Plan Recognition (EPR) system was developed to specifically handle the wide spectrum of micro contexts from multiple sensing modalities. The EPR system monitors the person with dementia and determines if he has executed a correct or erroneous ADL. However, due to the noisy readings of the sensing modalities, the EPR system has problems in accurately detecting the erroneous ADLs. We propose to improve the accuracy of the EPR system by two new key components. First, we model the smart home environment as a Markov decision process (MDP), with the EPR system built upon it. Simple referencing of this model allows us to filter erroneous readings of the sensing modalities. Second, we use the reinforcement learning concept of probability and reward to infer erroneous readings that are not filtered by the first key component.We conducted extensive experiments and showed that the accuracy of the new EPR system is 26.2% higher than the previous system, and is therefore a better system for ambient assistive living applications.