Azimuth-Based Assessment of Spatial Orientation Performance: A Diagnostic Tool for Cognitive Impairment

Document Type : Original Article


1 Department of Educational Science, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran

2 Department of Educational Science, Faculty of Humanities,Tarbiat Modares University, Tehran, Iran

3 Education and Learning Sciences, Wageningen University and Research, Wageningen, Netherlands


Background: Geographical/geometric indicators like azimuth provide a real-time and low-cost method of measuring spatial performance during navigation, especially in view of their accessibility on mobile phones in the form of a compass or GPS. This study aimed to investigate azimuth-based assessment of spatial orientation performance and its potential in diagnosing cognitive problems. Methods: This was a descriptive survey, and included multivariate logistic regression and multi-layer neural network analysis. We measured the spatial orientation performance of participants using an azimuth-based compass. Their demographic data, including age, gender, years of driving experience, field of study, and cognitive health status were collected. The statistical population consisted of 52 females and 48 males, 18 of whom had experienced cognitive problems in their lives. The participants were from different ethnic backgrounds living in the US, and in the age range of 20-85. The census method was then applied. Multivariate data analysis was conducted to illustrate the effectiveness of each feature variable on spatial orientation performance. Logistic regression was run by fitting a logit function, and multi-layer neural network analysis was developed and evaluated to predict the risk of cognitive problems based on spatial orientation performance and four other features under study. Results: Multivariate analysis showed that participants’ age (P=0.005), years of driving experience (p <0.001), and cognitive problems (p <0.001) contributed to predicting spatial orientation performance. Those who had experienced cognitive problems deviated 13.50 degrees from the destination with increasing age. The accuracy of the fitted logit model by NN analysis over training process was 0.8, indicating that the model can predict 8 out of 10 cases accurately. Conclusion: According to the findings, azimuth-based assessment of performance and other demographic data could be an appropriate means of determining individuals’ spatial orientation ability, especially when performed on larger and more homogeneous groups.


Diersch N, Wolbers T. The potential of virtual reality for spatial navigation research across the adult lifespan. Journal of Experimental Biology. 2019 Feb 6;222(Suppl 1). doi:10.1242/jeb.187252
Robillard M, Roy-Charland A, Cazabon S. The role of cognition on navigational skills of children and adolescents with autism spectrum disorders. Journal of Speech, Language, and Hearing Research. 2018 Jul 13;61(7):1579-90. doi:10.1044/2018_JSLHR-S-17-0206  PMid:29933432
Schaat S, Koldrack P, Yordanova K, Kirste T, Teipel S. Real-Time Detection of Spatial Disorientation in Persons with Mild Cognitive Impairment and Dementia. Gerontology. 2020;66(1):85-94.  doi:10.1159/000500971
Descloux V, Maurer R. Perspective taking to assess topographical disorientation: Group study and preliminary normative data. Applied Neuropsychology: Adult. 2020 May 3;27(3):199-218.  doi:10.1080/23279095.2018.1528262
Bodien YG, Martens G, Ostrow J, Sheau K, Giacino JT. Cognitive impairment, clinical symptoms and functional disability in patients emerging from the minimally conscious state. NeuroRehabilitation. 2020 Jan 1(Preprint):1-0. doi:10.3233/NRE-192860 
Howett D, Castegnaro A, Krzywicka K, Hagman J, Marchment D, Henson R, Rio M, King JA, Burgess N, Chan D. Differentiation of mild cognitive impairment using an entorhinal cortex-based test of virtual reality navigation. Brain. 2019 Jun 1;142(6):1751-66. doi:10.1093/brain/awz116
Vannier‐Nitenberg C, Dauphinot V, Bongue B, Sass C, Bathsavanis A, Rouch I, Deville N, Beauchet O, Krolak‐Salmon P, Fantino B. Performance of cognitive tests, individually and combined, for the detection of cognitive disorders amongst community‐dwelling elderly people with memory complaints: the EVATEM study. European Journal of Neurology. 2016 Mar;23(3):554-61. doi:10.1111/ene.12888
Cogné M, Taillade M, N’Kaoua B, Tarruella A, Klinger E, Larrue F, Sauzeon H, Joseph PA, Sorita E. The contribution of virtual reality to the diagnosis of spatial navigation disorders and to the study of the role of navigational aids: A systematic literature review. Annals of physical and rehabilitation medicine. 2017 Jun 1;60(3):164-76. doi:10.1016/  PMid:27017533
Coughlan G, Laczó J, Hort J, Minihane AM, Hornberger M. Spatial navigation deficits—overlooked cognitive marker for preclinical Alzheimer disease?. Nature Reviews Neurology. 2018 Aug;14(8):496-506.  doi:10.1038/s41582-018-0031-x  PMid:29980763
Verghese J, Lipton R, Ayers E. Spatial navigation and risk of cognitive impairment: A prospective cohort study. Alzheimer's & Dementia. 2017 Sep 1;13(9):985-92. doi:10.1016/j.jalz.2017.01.023  PMid:28264767 PMCid:PMC5582021
Vlček K, Laczó J. Neural correlates of spatial navigation changes in mild cognitive impairment and Alzheimer’s disease. Frontiers in behavioral neuroscience. 2014 Mar 17;8:89. doi:10.3389/fnbeh.2014.00089  PMid:24672452 PMCid:PMC3955968
Ventola CL. Mobile devices and apps for health care professionals: uses and benefits. Pharmacy and Therapeutics. 2014 May;39(5):356. PMID: 24883008
Flanagin VL, Fisher P, Olcay B, Kohlbecher S, Brandt T. A bedside application-based assessment of spatial orientation and memory: approaches and lessons learned. Journal of neurology. 2019 Sep 1;266(1):126-38.  PMid:31240446 PMCid:PMC6722154
Sholl MJ, Acacio JC, Makar RO, Leon C. The relation of sex and sense of direction to spatial orientation in an unfamiliar environment. Journal of Environmental Psychology. 2000 Mar 1;20(1):17-28.  doi:10.1006/jevp.1999.0146
Allahyar M, Hunt E. The assessment of spatial orientation using virtual reality techniques. International Journal of Testing. 2003 Sep 1;3(3):263-75. doi:10.1207/S15327574IJT0303_5
Wolbers T, Hegarty M. What determines our navigational abilities?. Trends in cognitive sciences. 2010 Mar 1;14(3):138-46. doi:10.1016/j.tics.2010.01.001 PMid:20138795
Lester AW, Moffat SD, Wiener JM, Barnes CA, Wolbers T. The aging navigational system. Neuron. 2017 Aug 30;95(5):1019-35.  doi:10.1016/j.neuron.2017.06.037  PMid:28858613 PMCid:PMC5659315
Rusconi ML, Suardi A, Zanetti M, Rozzini L. Spatial navigation in elderly healthy subjects, amnestic and non amnestic MCI patients. Journal of the neurological sciences. 2015 Dec 15;359(1-2):430-7.  doi:10.1016/j.jns.2015.10.010  PMid:26478129
Rodgers MK, Sindone III JA, Moffat SD. Effects of age on navigation strategy. Neurobiology of aging. 2012 Jan 1;33(1):202-e15. doi:10.1016/j.neurobiolaging.2010.07.021  PMid:20832911 PMCid:PMC4283776
Boone AP, Gong X, Hegarty M. Sex differences in navigation strategy and efficiency. Memory & cognition. 2018 Aug 1;46(6):909-22. doi:10.3758/s13421-018-0811-y  PMid:29790097
 Scali RM, Brownlow S, Hicks JL. Gender differences in spatial task performance as a function of speed or accuracy orientation. Sex roles. 2000 Sep 1;43(5-6):359-76. doi:10.1023/A:1026699310308
 Tzuriel D, Egozi G. Gender differences in spatial ability of young children: The effects of training and processing strategies. Child development. 2010 Sep;81(5):1417-30. doi:10.1111/j.1467-8624.2010.01482.x  PMid:20840231
Amick MM, Grace J, Ott BR. Visual and cognitive predictors of driving safety in Parkinson's disease patients. Archives of Clinical Neuropsychology. 2007 Nov 1;22(8):957-67
Bernardi G, Cecchetti L, Handjaras G, Sani L, Gaglianese A, Ceccarelli R, Franzoni F, Galetta F, Santoro G, Goebel R, Ricciardi E. It's not all in your car: functional and structural correlates of exceptional driving skills in professional racers. Frontiers in human neuroscience. 2014 Nov 11;8:888.  doi:10.3389/fnhum.2014.00888
Schultheis MT, Weisser V, Ang J, Elovic E, Nead R, Sestito N, Fleksher C, Millis SR. Examining the relationship between cognition and driving performance in multiple sclerosis. Archives of physical medicine and rehabilitation. 2010 Mar 1;91(3):465-73. doi:10.1016/j.apmr.2009.09.026  PMid:20298841
Weisberg SM, Schinazi VR, Newcombe NS, Shipley TF, Epstein RA. Variations in cognitive maps: Understanding individual differences in navigation. Journal of Experimental Psychology: Learning, Memory, and Cognition. 2014 May;40(3):669. doi:10.1037/a0035261  PMid:24364725
Bairaktarova D, Reyes M, PE MN. Spatial skills development of engineering students: Identifying instructional tools to incorporate into existing curricula. age. 2015 Jun 14;26:1. doi:10.18260/p.24726
Gunderson EA, Ramirez G, Beilock SL, Levine SC. The relation between spatial skill and early number knowledge: the role of the linear number line. Developmental psychology. 2012 Sep;48(5):1229.  doi:10.1037/a0027433  PMid:22390659
Hawes Z, Ansari D. What explains the relationship between spatial and mathematical skills? A review of evidence from brain and behavior. Psychonomic Bulletin & Review. 2020 Jan 21:1-8.  doi:10.3758/s13423-019-01694-7 PMid:31965485
Hawes Z, Moss J, Caswell B, Seo J, Ansari D. Relations between numerical, spatial, and executive function skills and mathematics achievement: A latent-variable approach. Cognitive Psychology. 2019 Mar 1;109:68-90. doi:10.3389/fpsyg.2018.00755  PMid:29915547 PMCid:PMC5994429
Young CJ, Levine SC, Mix KS. The connection between spatial and mathematical ability across development. Frontiers in psychology. 2018 Jun 4;9:755. doi:10.3389/fpsyg.2018.00755  PMCid:PMC5994429
Lavrakas PJ. Encyclopedia of survey research methods. Sage publications; 2008 Sep 12. doi:10.4135/9781412963947.n61
De Winter JC, Gosling SD, Potter J. Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data. Psychological methods. 2016 Sep;21(3):273. doi:10.1037/met0000079  PMid:27213982
Dreiseitl S, Ohno-Machado L. Logistic regression and artificial neural network classification models: a methodology review. Journal of biomedical informatics. 2002 Oct 1;35(5-6):352-9. doi:10.1016/S1532-0464(03)00034-0
Liu J, Shang S, Zheng K, Wen JR. Multi-view ensemble learning for dementia diagnosis from neuroimaging: An artificial neural network approach. Neurocomputing. 2016 Jun 26;195:112-6. doi:10.1016/j.neucom.2015.09.119
Pradhan B, Lee S. Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environmental Earth Sciences. 2010 May 1;60(5):1037-54. doi:10.1007/s12665-009-0245-8
Elshorbagy A, Simonovic SP, Panu US. Performance evaluation of artificial neural networks for runoff prediction. Journal of Hydrologic Engineering. 2000 Oct;5(4):424-7. doi:10.1061/(ASCE)1084-0699(2000)5:4(424
Lino A, Rocha Á, Sizo A. Virtual teaching and learning environments: automatic evaluation with artificial neural networks. Cluster Computing. 2019 May 1;22(3):7217-27. doi:10.1007/s10586-017-1122-y
YAKUT E, GUNDUZ M, DEMİRCİ A. Comparison of classification success of human development index by using ordered logistic regression analysis and artificial neural network methods. J Appl Quant Methods. 2015 Sep 1;10(3):15-34. doi:10.20491/isader.2015415532
Sikder MF, Uddin MJ, Halder S. Predicting students yearly performance using neural network: A case study of BSMRSTU. In2016 5th International Conference on Informatics, Electronics and Vision (ICIEV) 2016 May 13 (pp. 524-529). IEEE. doi:10.1109/ICIEV.2016.7760058