Abstract
Alzheimer’s disease has become a worldwide problem. Cognitive training can effectively slow the progression of Alzheimer’s disease and improve the quality of life of patients with Alzheimer’s disease. Spatial orientation is an important aspect of cognitive training. Due to the immersive and interactive features of VR (Virtual Reality) technology, VR technology has been gradually applied to cognitive training systems. This paper designs and implements a VR technology-based intelligent cognitive rehabilitation system for Alzheimer’s disease for assessing and training the spatial orientation of patients with Alzheimer’s disease. First, pre-assess the physiological status and operational ability of patients with Alzheimer’s disease. Then, build a realistic environment, guide through endogenous orientation and auditory orientation, and complete orientation training with different difficulty levels. Finally, the system provides relevant data for correlation analysis, combined with computer technology, to help doctors understand the patient’s condition to complete further treatment.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Janca, A., Hiller, W.: ICD-10 checklists—a tool for clinicians’ use of the ICD-10 classification of mental and behavioral disorders. Compr. Psychiatry 37(3), 180–187 (1996)
Small, G.W., Rabins, P.V., Barry, P.P., et al.: Diagnosis and treatment of Alzheimer disease and related disorders. Consensus statement of the American Association for Geriatric Psychiatry, the Alzheimer’s Association, and the American Geriatrics Society. JAMA, J. Am. Med. Assoc. 278(16), 1363–1371 (1997)
McLaren, A.N., Lamantia, M.A., Callahan, C.M.: Systematic review of non-pharmacologic interventions to delay functional decline in community-dwelling patients with dementia. Aging Ment. Health 17(6), 655–666 (2013)
Jennifer, R., Caroline, V.H., Martin, B.: Cognitive interventions in healthy older adults and people with mild cognitive impairment: a systematic review. Ageing Res. Rev. 12(1), 263–275 (2013)
Gomez, G.: Effects of a personalized music intervention among older adults with dementia. J. Am. Med. Dir. Assoc. 17(3), B25–B26 (2016)
Van Bogaert, P., Van Grinsven, R., Tolson, D., et al.: Effects of SolCos model-based individual reminiscence on older adults with mild to moderate dementia due to Alzheimer disease: a pilot study. J. Am. Med. Dir. Assoc. 14(7), 528.e9–528.e13 (2013)
McKeown, J., Ryan, T., Ingleton, C, et al.: You have to be mindful of whose story it is’: the challenges of undertaking life story work with people with dementia and their family carers. Dement. (Lond. Engl.) 14, 238–256 (2013)
Robert, P.: Recommendations for the use of serious games in people with Alzheimer’s disease, related disorders and frailty. Front. Aging Neurosci. 6, 54 (2014)
Manera, V., Petit, P.-D., Derreumaux, A., et al.: Kitchen and cooking’, a serious game for mild cognitive impairment and Alzheimer’s disease: a pilot study. Front. Aging Neurosci. 7, 24 (2015)
Maskey, M., Lowry, J., Rodgers, J., et al.: Reducing specific phobia/fear in young people with autism spectrum disorders (ASDs) through a virtual reality environment intervention. PLoS ONE 9(7), e100374 (2014)
Hartanto, D., Kampmann, I.L., Morina, N., et al.: Controlling social stress in virtual reality environments. PLoS ONE 9(3), e92804 (2017)
Repetto, C., Gaggioli, A., Pallavicini, F., et al.: Virtual reality and mobile phones in the treatment of generalized anxiety disorders: a phase-2 clinical trial. Pers. Ubiquitous Comput. 17(2), 253–260 (2013)
Standen, P.J., Threapleton, K., Connell, L., et al.: Patients’ use of a home-based virtual reality system to provide rehabilitation of the upper limb following stroke. Phys. Ther. 95(3), 350–359 (2015)
Hua, Y., Qiu, R., Yao, W., et al.: The effect of virtual reality distraction on pain relief during dressing changes in children with chronic wounds on lower limbs. Pain Manag. Nurs. 16(5), 685–691 (2015)
Trelease, R.B., Nieder, G.L.: Transforming clinical imaging and 3D data for virtual reality learning objects: HTML5 and mobile devices implementation. Anat. Sci. Educ. 6(4), 263–270 (2013)
Palter, V.N., Grantcharov, T.P.: Individualized deliberate practice on a virtual reality simulator improves technical performance of surgical novices in the operating room: a randomized controlled trial. Ann. Surg. 259(3), 443–448 (2014)
Coluccia, E., Louse, G.: Gender differences in spatial orientation: a review. J. Environ. Psychol. 24(3), 329–340 (2004)
Marotta, A., Lupiáñez, J., Casagrande, M.: Investigating hemispheric lateralization of reflexive attention to gaze and arrow cues. Brain Cogn. 80(3), 361–366 (2012)
Tan, W., Yuan, Y., Chen, A., Mao, L., Ke, Y., Lv, X.: An approach for pulmonary vascular extraction from chest CT images. J. Healthc. Eng. (2019). https://doi.org/10.1155/2019/9712970
Tan, W., et al.: an approach to extraction midsagittal plane of skull from brain CT images for oral and maxillofacial surgery. IEEE Access 7, 118203–118217 (2019)
Huang, J., Peng, M., Wang, H., Cao, J., Gao, W., Zhang, X.: A probabilistic method for emerging topic tracking in microblog stream. World Wide Web 20(2), 325–350 (2017)
Zhang, J., Tao, X., Wang, H.: Outlier detection from large distributed databases. World Wide Web 17(4), 539–568 (2014)
Li, H., Wang, Y., Wang, H., Zhou, B.: Multi-window based ensemble learning for classification of imbalanced streaming data. World Wide Web 20(6), 1507–1525 (2017)
Khalil, F., Wang, H., Li, J.: Integrating markov model with clustering for predicting web page accesses. In: Proceeding of the 13th Australasian World Wide Web Conference (AusWeb07), pp. 63–74 (2007)
Khalil, F., Li, J., Wang, H.: An integrated model for next page access prediction. Int. J. Knowl. Web Intell. 1(1), 48–80 (2009)
Ma, J., Sun, L., Wang, H., Zhang, Y., Aickelin, U.: Supervised anomaly detection in uncertain pseudoperiodic data streams. ACM Trans. Internet Technol. (TOIT) 16(1), 4 (2016)
Hu, H., Li, J., Wang, H., Daggard, G.: Combined gene selection methods for microarray data analysis. In: Gabrys, B., Howlett, Robert J., Jain, Lakhmi C. (eds.) KES 2006. LNCS (LNAI), vol. 4251, pp. 976–983. Springer, Heidelberg (2006). https://doi.org/10.1007/11892960_117
Peng, M., Zeng, G., Sun, Z., Huang, J., Wang, H., Tian, G.: Personalized app recommendation based on app permissions. World Wide Web 21(1), 89–104 (2018)
Acknowledgments
The authors would like to thank the editor and reviewers for their valuable advices that have helped to improve the paper quality. This work is supported by the Fundamental Research Funds for the Central Universities (N181602014), National Key Research and Development Program of China (2018YFC1314501).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Hang, Y., Ge, W., Jiang, H., Li, H., Tan, W. (2019). VR Technology-Based Intelligent Cognitive Rehabilitation System for Alzheimer’s Disease. In: Wang, H., Siuly, S., Zhou, R., Martin-Sanchez, F., Zhang, Y., Huang, Z. (eds) Health Information Science. HIS 2019. Lecture Notes in Computer Science(), vol 11837. Springer, Cham. https://doi.org/10.1007/978-3-030-32962-4_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-32962-4_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32961-7
Online ISBN: 978-3-030-32962-4
eBook Packages: Computer ScienceComputer Science (R0)