{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T09:17:20Z","timestamp":1763457440082,"version":"3.41.2"},"reference-count":34,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2014,8,26]],"date-time":"2014-08-26T00:00:00Z","timestamp":1409011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,8,26]]},"abstract":"<jats:sec>\n               <jats:title content-type=\"abstract-heading\">Purpose<\/jats:title>\n               <jats:p> \u2013 This paper aims to discuss an online sensor-based support system which is believed to be useful for persons with a cognitive impairment, such as those with Alzheimer\u2019s disease, suffering 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). <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title>\n               <jats:p> \u2013 The system proposed processes data from a network of sensors that have the capability of sensing user interactions and ongoing iADLs in the living environment itself. A probabilistic learning model is built that computes joint probability distributions over different activities representing users\u2019 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, taking 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. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Findings<\/jats:title>\n               <jats:p> \u2013 The 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 per cent rise in the prediction performance in the case of single-sensor activation in comparison to an alternative approach which did not consider activity durations. Thus, it is concluded 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\u2019s disease. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title>\n               <jats:p> \u2013 Activity duration information can be a potential feature in measuring the performance of a user and distinguishing different activities. The 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.<\/jats:p>\n            <\/jats:sec>","DOI":"10.1108\/ijpcc-07-2014-0042","type":"journal-article","created":{"date-parts":[[2014,10,2]],"date-time":"2014-10-02T09:26:57Z","timestamp":1412242017000},"page":"337-366","source":"Crossref","is-referenced-by-count":11,"title":["A duration-based online reminder system"],"prefix":"10.1108","volume":"10","author":[{"given":"Priyanka","family":"Chaurasia","sequence":"first","affiliation":[]},{"given":"Sally","family":"McClean","sequence":"first","affiliation":[]},{"given":"Chris","family":"D. Nugent","sequence":"first","affiliation":[]},{"given":"Bryan","family":"Scotney","sequence":"first","affiliation":[]}],"member":"140","reference":[{"key":"key2020123000164900200_b1","doi-asserted-by":"crossref","unstructured":"Allen, J.F.\n                and \n                  Ferguson, G.\n                (1994), \u201cActions and events in interval temporal logic\u201d, Journal of Logic and Computation, Vol. 4 No. 5, pp. 531-579.","DOI":"10.1093\/logcom\/4.5.531"},{"key":"key2020123000164900200_b2","doi-asserted-by":"crossref","unstructured":"Aztiria, A.\n               , \n                  Izaguirre, A.\n                and \n                  Augusto, J.C.\n                (2010), \u201cLearning patterns in ambient intelligence environments: a survey\u201d, Artificial Intelligence Review, Vol. 34 No. 1, pp. 35-51.","DOI":"10.1007\/s10462-010-9160-3"},{"key":"key2020123000164900200_b3","doi-asserted-by":"crossref","unstructured":"Bartolomeu, P.\n               , \n                  Fonseca, J.\n                and \n                  Vasques, F.\n                (2008), \u201cChallenges in health smart homes\u201d, Proceedings of the Workshop on Ambient Technologies for Diagnosing and Monitoring Chronic Patients (ATDMCP-08) included in the 2nd International Conference on Pervasive Computing Technologies for Healthcare, Tampere.","DOI":"10.1109\/PCTHEALTH.2008.4571016"},{"key":"key2020123000164900200_b4","doi-asserted-by":"crossref","unstructured":"Boger, J.\n               , \n                  Hoey, J.\n               , \n                  Poupart, P.\n               , \n                  Boutilier, C.\n               , \n                  Fernie, G.\n                and \n                  Mihailidis, A.\n                (2006), \u201cA planning system based on Markov decision processes to guide people with dementia through activities of daily living\u201d, IEEE Transactions on Information Technology in Biomedicine, Vol. 10 No. 2, pp. 323-333.","DOI":"10.1109\/TITB.2006.864480"},{"key":"key2020123000164900200_b5","unstructured":"Chaurasia, P.\n               , \n                  Scotney, B.\n               , \n                  McClean, S.\n                and \n                  Nugent, C.\n                (2010), \u201cAn intervention framework for cognitively impaired patients\u201d, 5th International Workshop on Ubiquitous Health and Wellness, Copenhagen, Denmark."},{"key":"key2020123000164900200_b6","doi-asserted-by":"crossref","unstructured":"Chaurasia, P.\n               , \n                  Scotney, B.\n               , \n                  McClean, S.\n               , \n                  Zhang, S.\n                and \n                  Nugent, C.\n                (2010), \u201cIncorporating duration information in activity recognition\u201d, KSEM\u201910 Proceedings of the 4th international conference on Knowledge Science, Engineering and Management LNCS, Springer, Berlin, Vol. 6291, pp. 245-255.","DOI":"10.1007\/978-3-642-15280-1_24"},{"key":"key2020123000164900200_b7","doi-asserted-by":"crossref","unstructured":"Chaurasia, P.\n               , \n                  McClean, S.\n               , \n                  Scotney, B.\n                and \n                  Nugent, C.\n                (2012), \u201cDuration discretisation for activity recognition\u201d, Technology and Health Care, Vol. 20 No. 4, pp. 277-295.","DOI":"10.3233\/THC-2012-0677"},{"key":"key2020123000164900200_b9","unstructured":"Craig, D.\n               , \n                  Nugent, C.\n                and \n                  Mulvenna, M.\n               , (2006), \u201cHealthcare technologies for older people: what do physicians think?\u201d, Smart Homes and Beyond: ICOST 2006, 4th International Conference on Smart Homes and Health, Telematics, IOS Press, Belfast, Northern Ireland, UK, pp. 331-334."},{"key":"key2020123000164900200_b10","doi-asserted-by":"crossref","unstructured":"Du, K.\n               , \n                  Zhang, D.\n               , \n                  Musa, M.W.\n               , \n                  Mokhtari, M.\n                and \n                  Zhou, X.\n                (2008), \u201cHandling activity conflicts in reminding system for elders with Dementia\u201d, Second International Conference on Future Generation Communication and Networking, Vol. 2, Hainan Island, 13-15 December, pp. 416-421.","DOI":"10.1109\/FGCN.2008.117"},{"key":"key2020123000164900200_b11","unstructured":"El-Zabadani, H.\n               , \n                  Helal, S.\n               , \n                  Schmalz, M.\n                and \n                  Mann, W.\n                (2006), \u201cPerVision: an integrated pervasive computing\/computer vision approach to tracking objects in a self-sensing space\u201d, Smart Homes and Beyond: ICOST 2006, 4th International Conference on Smart Homes and Health Telematics, IOS Press, Amsterdam, p. -."},{"key":"key2020123000164900200_b12","doi-asserted-by":"crossref","unstructured":"Forsyth, E.\n                and \n                  Ritzline, P.D.\n               , (1998), \u201cAn overview of the etiology, diagnosis, and treatment of Alzheimer disease\u201d, Physical Therapy, Vol. 78 No. 12, pp. 1325-1331.","DOI":"10.1093\/ptj\/78.12.1325"},{"key":"key2020123000164900200_b13","doi-asserted-by":"crossref","unstructured":"Helal, S.\n               , \n                  Mann, W.\n               , \n                  El-Zabadani, H.\n               , \n                  King, J.\n               , \n                  Kaddoura, Y.\n                and \n                  Jansen, E.\n                (2005), \u201cThe gator tech smart house: a programmable pervasive space\u201d, Computer, IEEE Computer Society, Vol. 38 No. 3, pp. 50-60.","DOI":"10.1109\/MC.2005.107"},{"key":"key2020123000164900200_b14","unstructured":"Iachine, I.\n                and \n                  Begreber, B.\n                (2001), \u201cBasic survival analysis\u201d, Group, Vol. 216, p. -."},{"key":"key2020123000164900200_b15","unstructured":"Jakkula, V.R.\n                and \n                  Cook, D.J.\n                (2007), \u201cUsing temporal relations in smart environment data for activity prediction\u201d, International Conference on Machine Learning (ICML) Workshop on the Induction of Process Models (IPM\/ICML 2007), Corvalis."},{"key":"key2020123000164900200_b16","doi-asserted-by":"crossref","unstructured":"Jakkula, V.R.\n                and \n                  Cook, D.J.\n                (2008), \u201cAnomaly detection using temporal data mining in a smart home environment\u201d, Methods of Information in Medicine, Vol. 47 No. 1, pp. 70-75.","DOI":"10.3414\/ME9103"},{"key":"key2020123000164900200_b17","doi-asserted-by":"crossref","unstructured":"Jakkula, V.R.\n               , \n                  Cook, D.J.\n                and \n                  Crandall, A.S.\n            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Garg, L.\n               , \n                  Chaurasia, P.\n               , \n                  Scotney, B.\n                and \n                  Nugent, C.\n                (2012), \u201cUsing model-based clustering to discretise duration information for activity recognition\u201d, 24th IEEE International Symposium on Computer-Based Medical Systems, CBMS, Bristol, UK, pp. 1-7."},{"key":"key2020123000164900200_b21","unstructured":"McFadden, T.\n                and \n                  Indulska, J.\n                (2004), \u201cContext-aware environments for independent living\u201d, 3rd National Conference for Emerging Researchers in Ageing, Brisbane, pp. 147-151."},{"key":"key2020123000164900200_b19","doi-asserted-by":"crossref","unstructured":"Martin, T.\n               , \n                  Majeed, B.\n               , \n                  Lee, B.\n                and \n                  Clarke, N.\n                (2007), \u201cA third-generation telecare system using fuzzy ambient intelligence\u201d, Computational Intelligence for Agent-based Systems, Vol. 72, pp. 155-175.","DOI":"10.1007\/978-3-540-73177-1_6"},{"key":"key2020123000164900200_b22","unstructured":"Moeschberger, M.L.\n                and \n                  Klein, J.P.\n                (2003), Survival Analysis: Techniques for Censored and Truncated Data, Springer, New York, NY."},{"key":"key2020123000164900200_b23","unstructured":"M\u00f6rchen, F.\n                (2006), \u201cA better tool than Allen\u2019s relations for expressing temporal knowledge in interval data\u201d, Proceedings the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA."},{"key":"key2020123000164900200_b24","unstructured":"Nazerfard, E.\n               , \n                  Rashidi, P.\n                and \n                  Cook, D.\n                (2011), \u201cUsing association rule mining to discover temporal relations of daily activities\u201d, Toward Useful 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1-4.","DOI":"10.4108\/ICST.PERVASIVEHEALTH2009.6056"},{"key":"key2020123000164900200_b27","doi-asserted-by":"crossref","unstructured":"Pollack, M.E.\n               , \n                  Brown, L.\n               , \n                  Colbry, D.\n               , \n                  McCarthy, C.E.\n               , \n                  Orosz, C.\n               , \n                  Peintner, B.\n               , \n                  Ramakrishnan, S.\n                and \n                  Tsamardinos, I.\n                (2003), \u201cAutominder: an intelligent cognitive orthotic system for people with memory impairment\u201d, Robotics and Autonomous Systems, Vol. 44 Nos 3\/4, pp. 273-282.","DOI":"10.1016\/S0921-8890(03)00077-0"},{"key":"key2020123000164900200_b28","unstructured":"Russo, J.\n               , \n                  Sukojo, A.\n               , \n                  Helal, A.S.\n               , \n                  Davenport, R.\n                and \n                  Mann, W.C.\n                (2004), \u201cSmartWave\u2013intelligent meal preparation system to help older people live independently\u201d, Toward a human-friendly assistive environment: ICOST\u2019, 2nd International Conference on Smart Homes and Health Telematics, IOS Press, Vol. 14, pp. 122-135."},{"key":"key2020123000164900200_b29","unstructured":"Singla, G.\n               , \n                  Cook, D.J.\n                and \n                  Schmitter-Edgecombe, M.\n                (2008), \u201cIncorporating temporal reasoning into activity recognition for smart home residents\u201d, Proceedings of the AAAI Workshop on Spatial and Temporal Reasoning, Chicago, Illinois, USA, p. -."},{"key":"key2020123000164900200_b30","doi-asserted-by":"crossref","unstructured":"Van Kasteren, T.\n               , \n                  Noulas, A.\n               , \n                  Englebienne, G.\n                and \n                  Kr\u00f6se, B.\n                (2008), \u201cAccurate activity 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