A duration-based online reminder system. Issue 3 (19th August 2014)

• Record Type: Journal Article
• Authors: Chaurasia, Priyanka; McClean, Sally; Nugent, Chris D.; Scotney, Bryan
• Editors: Khalil, Ismail
• Is Part Of: International journal of pervasive computing and communications. Volume 10:Issue 3(2014)
• Publication Date: 2014-08-19
• Subjects: Ubiquitous computing -- Periodicals; Mobile computing -- Periodicals; Computer network protocols -- Periodicals; Computer network architectures -- Periodicals; Application software -- Development -- Periodicals; 004.6
• DOI: 10.1108/IJPCC-07-2014-0042
• Languages: English
• ISSNs: 1742-7371

Abstract

Abstract : Purpose: Persons with a cognitive impairment, such as those with Alzheimer's disease, suffer from deficiencies in cognitive skills which reduce their independence; such patients can benefit from the provision of further assistance such as reminders for carrying out instrumental activities of daily living (iADLs). In this paper we discuss an online sensor-based support system which we believe can be useful in such scenarios. Design/methodology/approach: The system proposed processes data from a network of sensors that have the capability of sensing user interactions and on-going iADLs in the living environment itself. A probabilistic learning model is built that computes joint probability distributions over different activities representing users' behavioural patterns in performing activities. This probability model can underpin an intervention framework that prompts the user with the next step in the iADL when inactivity is being observed. This prompt for the next step is inferred from the conditional probability taken into consideration the iADL steps that have already been completed, in addition to contextual information relating to the time of day and the amount of time already spent on the activity. The originality of the work lies in combining partially observed sensor sequences and duration data associated with the iADLs. The prediction of the next step is then adjusted as further steps are completed and more time is spent towards the completion of the activity, thus updating the confidence that the prediction is correct. A reminder is only issued when there has been sufficient inactivity on the part of the patient and the confidence is high that the prediction is correct. Findings: Our results verify that by including duration information the prediction accuracy of the model is increased and the confidence level for the next step in the iADL is also increased. As such, there is approximately a 10% rise in the prediction performance in the case of single sensor activation in comparison to an alternative approach which did not consider activity durations. Conclusions: We conclude that incorporating progressive duration information into partially observed sensor sequences of iADLs has the potential to increase performance of a reminder system for patients with a cognitive impairment, such as Alzheimer's disease. Originality/value: Activity duration information can be a potential feature in measuring the performance of a user and distinguishing different activities. Our results verify that by including duration information the prediction accuracy of the model is increased and the confidence level for the next step in the activity is also increased. The use of duration information in online prediction of activities can also be associated to monitoring the deterioration in cognitive abilities and in making a decision about the level of assistance required. Such improvements have significance in building more accurate reminder systems that precisely predict activities and assist its users, thus, improving the overall support provided for living independently.