2-layer erroneous-plan recognition for dementia patients in smart homes

Authors:
Clifton Phua;Victor Siang-Fook Foo;Jit Biswas;Andrei Tolstikov;Aung-Phyo-Wai Aung;Jayachandran Maniyeri;Weimin Huang;Mon-Htwe That;Duangui Xu;Alvin Kok-Weng Chu
Affiliations:
Institute for Infocomm Research, Data Mining Department;Networking Protocols Department, Institute for Infocomm Research;Networking Protocols Department, Institute for Infocomm Research;Networking Protocols Department, Institute for Infocomm Research;Networking Protocols Department, Institute for Infocomm Research;Networking Protocols Department, Institute for Infocomm Research;Computer Vision and Image Understanding, Institute for Infocomm Research;Computer Vision and Image Understanding, Institute for Infocomm Research;Temasek Polytechnic, Computer Engineering Department;Temasek Polytechnic, Computer Engineering Department
Venue:
Healthcom'09 Proceedings of the 11th international conference on e-Health networking, applications and services
Year:
2009

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Abstract

People with dementia lose their ability to learn, solve problems, and communicate. And they are all around us. To potentially replace some of their diminished memory and problem-solving abilities, Erroneous-Plan Recognition (EPR) aims to detect defects or faults in the execution of correct plans by the dementia patient, and send timely audio and visual prompts to the dementia patient and caregiver in order to correct these faults. The scope of this work is for the patient who lives alone in a smart home. One challenge is that the definition of plan can be very subjective. It is necessary to regard a plan as an Activity of Daily Living (ADL), choose the ADLs to monitor, and deploy available sensors to acquire data. With the sensor data, there can be activity recognition, followed by plan recognition. Another challenge is the highly random and erroneous behaviour of dementia patients. Multiple, sequential, and independent layers of error detection can be arranged in a prioritised manner to detect specific errors first, and provide an error probability if no specific errors are detected. On the whole, most of the EPR results are very good as they are at least 0.9, indicating that the data is linearly separable. The 2-layer EPR system, which uses the blacklist and whitelist as Layer 1 and naive Bayes classifier as Layer 2, is significantly more accurate than each individual layer. In fact, 5 out of 6 actors have an accuracy above 0.9. With the encouraging results, there will be more technical and domain challenges which we can address in the near future.