A natural language-based presentation of cognitive stimulation to people with dementia in assistive technology: A pilot study

References

• Prince M, Prina M, Guerchet M. Alzheimer’s disease international. London: Alzheimer’s Disease International. World Alzheimer Report 2013.
   Google Scholar
• Livingston G, Johnston K, Katona C, Paton J, Lyketsos C. Systematic review of psychological approaches to the management of neuropsychiatric symptoms of dementia. American Journal of Psychiatry 2005;162:1996–2021.
   PubMed Web of Science ®Google Scholar
• Olazarán J, Reisberg B, Clare L, et al. Nonpharmacological therapies in Alzheimer’s disease: a systematic review of efficacy. Dementia and Geriatric Cognitive Disorders 2010;30:161–78.
   PubMed Web of Science ®Google Scholar
• Robinson L, Iliffe S, Brayne C, et al. Primary care and dementia: 2. long-term care at home: psychosocial interventions, information provision, carer support and case management. International Journal of Geriatric Psychiatry 2010;25:657–64.
   Google Scholar
• Quayhagen MP, Quayhagen M. Testing of a cognitive intervention stimulation for demenia care-giving dyads. Neuropsychologial Rehabilitation 2001;11:319–32.
   Web of Science ®Google Scholar
• Moniz-Cook E, Agar S, Gibson G, et al. A preliminary study of the effects of early intervention with people with dementia and their families in a memory clinic. Aging & Mental Health 1998;2:199–211.
   Web of Science ®Google Scholar
• Sitzer D, Twamley E, Jeste D. Cognitive training in Alzheimer’s disease: a meta- analysis of the literature. Acta Psychiatrica Scandinavica 2006;114:75–90.
   PubMed Web of Science ®Google Scholar
• Aguirre E, Spector A, Hoe J, et al. Maintenance Cognitive Stimulation Therapy (CST) for dementia: a single-blind, multi-centre, randomized controlled trial of Maintenance CST vs. CST for dementia. Trials 2010;11:46.
   Google Scholar
• Orrell M, Spector A, Thorgrimsen L, et al. A pilot study examining the effectiveness of maintenance cognitive stimulation therapy (MCST) for people with dementia. International Journal of Geriatric Psychiatry 2005;20:446–51.
   PubMed Web of Science ®Google Scholar
• Aguire E, Woods RT, Spector A, Orrell M. Cognitive stimulation for dementia: a systematic review of the evidence of effectiveness from randomized controlled trials. Ageing Research Reviews 2013;12(1):253–62.
   PubMed Web of Science ®Google Scholar
• Spector A, Orrell M, Woods B. Cognitive stimulation for the treatment of Alzheimer’s disease. Expert Review of Neurotherapeutics 2008;8(5):751–57.
   PubMedGoogle Scholar
• Agudo-Prado S, Pascual-Sevillano A, Fombona-Cadavieco J. Uses of digital tools among the Elderly. Comunicar Scientific Journal of Media Communication 2012;39:193–20.
   Web of Science ®Google Scholar
• Onder G, Zanetti O, Giacobini E, Frisoni GB, Bartorelli L, Carbone G, Lambertucci P, Silveri MC, Bernabei R. Reality orientation therapy combined with cholinesterase inhibitors in Alzheimer’s disease: randomised controlled trial. British Journal of Psychiatry 2005;187:450–55.
   PubMed Web of Science ®Google Scholar
• Milders M, Bell S, Lorimer A, MacEwan T, McBain A. Cognitive stimulation by caregivers for people with dementia. Geriatric Nursing 2013;34(4):267–73.
   PubMed Web of Science ®Google Scholar
• Cipriani G, Bianchetti A, Trabucchi M. Outcomes of a computer- based cognitive rehabilitation program on Alzheimer’s disease patients compared with those on patients affected by mild cognitive impairment. Archives of Gerontology and Geriatrics 2006;43(2006):327–35.
   PubMed Web of Science ®Google Scholar
• González-Palau F, Franco M, Bamidis P, Losada R, Parra E, Papageorgiou S, Vivas A. The effects of a computer-based cognitive and physical training program in a healthy and mildly cognitive impaired aging sample. Aging & Mental Health 2014;18(7):838–46.
   PubMed Web of Science ®Google Scholar
• Hofmann M, Rosler A, Schwarz W, Muller-Spahn F, Krauchi K, Hock C, Seifritz E. Interactive computer-training as a therapeutic tool in Alzheimer’s disease. Comprehensive Psychiatry 2003;44:213–19.
   PubMed Web of Science ®Google Scholar
• Kueider A, Parisi J, Gross A, Rebok G. Computerized cognitive training with older adults: a systematic review. PloS ONE 2012;7(7).
   PubMed Web of Science ®Google Scholar
• Faucounau V, Wu YH, Boulay M, de Rotrou J, Rigaud S. Cognitive intervention programmes on patients affected by mild cognitive impairment: promising intervention tool for MCI? The Journal of Nutrition, Health and Aging 2010;14(1):31–35.
   Google Scholar
• Nalin M, Baroni I, Kruijff-Korbayova I, Canamero L, Lewis M, Beck A, Cuayahuitl H, Sanna A. Children’s adaptation in multi-session interaction with a Humanoid Robot. In: Proceedings of the 21st IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). Paris, France: IEEE: the Institute of Electrical and Electronics Engineers; 2012.
   Google Scholar
• Rieser V, Lemon O. Learning effective multimodal dialogue strategies from wizard-of-Oz data: bootstrapping and evaluation. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics (ACT-HLT). Columbus, OH: ACL: the Association for Computational Linguistics; 2008.
   Google Scholar
• Schröder M, Charfuelan M, Pammi S, Steiner I. Open source voice creation toolkit for the MARY TTS Platform. In: Proceedings of INTERSPEECH 2011. International Speech Communication Association (ISCA). Paris, France.
   Google Scholar
• Zue V, Glass J. Conversational interfaces: advances and challenges. Proceedings of the IEEE 2000;80(8):1166–80.
   Web of Science ®Google Scholar
• Anderson S, Liberman N, Bernstein E, Foster S, Cate E, Levin B, Hudson R. Recognition of elderly speech and voice-driven document retrieval. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Phoenix, AZ; 1999.
   Google Scholar
• Pang B, Lee L, Vaithyanathan S. Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA: ACL: the Association for Computational Linguistics; 2002.
   Google Scholar
• Perez-Rosas V, Mihalcea R, Morency LP. Utterance-level multimodal sentiment analysis. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL). Sofia, Bulgaria: ACL: the Association for Computational Linguistics; 2013.
   Google Scholar
• McGraw I, Prabhavalkar R, Alvarez R, Gonzalez Arenas M, Rao K, Rybach D, Alsharif O, Sak H, Gruenstein A, Beaufays F, Parada C.Personalized Speech Recognition On Mobile Devices. Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP) In: Proceedings of International Confer- ence on Acoustics, Speech and Signal Processing (ICASSP). Shanghai, China: IEEE, Institute of Electrical and Electronics Engineers.; 2016.
   Google Scholar
• Lei X, Lin H, Heigold G. Deep neural networks with auxiliary Gaussian mixture models for real-time speech recognition. In: Proceedings of the IEEE in- ternational Conference on Acoustics, Speech, and Signal Processing (ICASSP). Vancouver, Canada: IEEE: the Institute of Electrical and Electronics Engineers; 2013.
   Google Scholar
• Liao H, Pundak G, Siohan O, Carroll M, Coccaro N, Jiang QM, Sainath TN, Senior A, Beaufays F, Bacchiani M. Large vocabulary automatic speech recognition for children. In: Inter-Speech. Dresden, Germany; 2015.
   Google Scholar
• Balsis S, Benge J, Lowe D, Geraci L, Doody R. How do scores on the ADAS-Cog, MMSE, and CDR-SOB correspond? The Clinical Neuropsychologist 2015;29:1002–1009.
   PubMed Web of Science ®Google Scholar
• Perneczky R, Wagenpfeil S, Komossa K, Grimmer T, Diehl J, Kurz A. Mapping scoresonto stages: mini-mental state examination and clinical de mentia rating. American Journal of Geriatric Psychiatry 2006;14:139–44.
   PubMed Web of Science ®Google Scholar