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Assessment of wheelchair skills based on analysis of driving style

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Abstract

In this paper, we propose a learning assessment method based on the analysis of learner’s behavioural style. This method was first applied for wheel-chair driving tasks because it is simple and risk-free, but unusual for users. Seven classic performance indicators based on joystick control were used to characterise the users’ driving style. We assumed that the learning effectiveness of the users can be evaluated by comparing their driving style with the reference ones, which could be extracted from experienced users. The evaluation was carried out for six novice users and eight experienced users. The users were asked to carry out several typical driving tasks for seven trials at first. The fuzzy C-means clustering method was used with the data of the experienced users to obtain the reference driving styles. Next, an evaluation was performed for novice users by comparing their driving styles with the reference ones. The results showed that, for all of the experienced users, their driving styles could be classified into two reference types. In addition, there was no significant difference in their driving styles from one trial to another, even for a user with disabilities, which means that their driving style was stable. On the other hand, novice users had switching behaviours during the learning phase; however, after eight additional trials, each novice user’s driving style converged to one of the two identified reference types, meaning that the novice users could achieve a stable performance after learning, which was also validated by an expert therapist.

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Notes

  1. http://www.pleia.uvsq.fr/.

  2. http://www.ceremh.org, national center on mobility aid.

References

  • Bédard M, Parkkari M, Weaver B, Riendeau J, Dahlquist M (2010) Assessment of driving performance using a simulator protocol: validity and reproducibility. Am J Occup Ther 64(2):336–340

    Article  Google Scholar 

  • Bezdek J, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10:191–203

    Article  Google Scholar 

  • Bezet O, Cherfaoui V, Bonnifait P (2006) A system for driver behavioral indicators processing and archiving. In: 9th IEEE conference on intelligent transportation systems (ITSC’06), vol 45, pp 799–804

  • Canale M, Malan S (2002) Analysis and classification of human driving behaviour in an urban environment. Cogn Tech Work 4(3):197–206

    Article  Google Scholar 

  • Candiotti J, Kamaraj D, Daveler B, Chung C, Grindle G, Cooper R, Cooper R (2019) Usability evaluation of a novel robotic power wheelchair for indoor and outdoor navigation. Arch Phys Med Rehabil 100:627–637

    Article  Google Scholar 

  • Chase J, Bailey DM (1990) Evaluating the potential for powered mobility. Am J Occup Ther 44(12):1125–1129

    Article  Google Scholar 

  • Chen J, Yeasin M, Sharma R (2003) Visual modelling and evaluation of surgical skill. Pattern Anal Appl 6:1–11

    Article  MathSciNet  Google Scholar 

  • Cooper R, Thorman T, Cooper R, Dvorznak M, Fitzgerald S, Ammer W, Song-Feng G, Boninger M (2002) Driving characteristics of electric-powered wheelchair users: how far, fast, and often do people drive? Arch Phys Med Rehabil 83(2):250–255

    Article  Google Scholar 

  • Deepan C, Dicianno B, Schmid M et al (2014) Quantifying power wheelchair driving ability. In: Rehabilitation engineering and assistive technology Society of North America conference, Indianapolis

  • Dong W, Li J, Yao R, Li C, Yuan T, Wang L (2016) Characterizing driving styles with deep learning. ArXiv e-prints

  • Dunn J (1974) A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. J Cybern 3(3):1149–1158

    MathSciNet  Google Scholar 

  • Durkin J (2002) The need for a child led assessment tool for powered mobility users. Technol Disabil 14(4):163–171

    Article  Google Scholar 

  • Durkin J (2009) Discovering powered mobility skills with children: ’responsive partners’ in learning. Int J Ther Rehabil 16(6):331–341

    Article  Google Scholar 

  • Fehr L, Langbein W, Skaar S (2000) Adequacy of power wheelchair control interfaces for persons with severe disabilities: a clinical survey. J Rehabil Res Dev 37(3):353–360

    Google Scholar 

  • Field D, Livingstone R (2018) Power mobility skill progression for children and adolescents: a systematic review of measures and their clinical application. Dev Med Child Neurol 60(10):997–1011

    Article  Google Scholar 

  • Ge Y, Qu W, Jiang C, Du F, Sun X, Zhang K (2014) The effect of stress and personality on dangerous driving behavior among chinese drivers. Accid Anal Prev 73:34–40

    Article  Google Scholar 

  • Javanmardi S, Bideaux E, Trégouët JF, et al (2017) Driving style modelling for eco-driving applications. In: The 20th World congress of the international federation of automatic control, Toulouse, pp 13866–13871

  • Johnson DA, Trivedi MM (2011) Driving style recognition using a smartphone as a sensor platform. In: 14th International IEEE conference on intelligent transportation systems, pp 1609–1615

  • Kirby R (1997) Principles of wheelchair design and prescription. In: Principles of neurologic rehabilitation, pp 465–481

  • Kirby R, Miller W, Routhier F et al (2015) Effectiveness of a wheelchair skills training program for powered wheelchair users: a randomized controlled trial. Arch Phys Med Rehabil 96(11):2017–2026

    Article  Google Scholar 

  • Kirby R, Smith C, Parker K et al (2017) The wheelchair skills program manual. Dalhousie University, Nova Scotia

    Google Scholar 

  • Kruskal W, Wallis W (1952) Use of ranks in one-criterion variance analysis. J Am Stat Assoc 47:583–621

    Article  Google Scholar 

  • Lanatà A, Valenza G, Greco A, Gentili C, Bartolozzi R, Bucchi FEA (2015) How the autonomic nervous system and driving style change with incremental stressing conditions during simulated driving. IEEE Trans Intell Transp Syst 16(3):1505–1517

    Article  Google Scholar 

  • Lefevre S, Carvalho A, Gao YQ et al (2015) Driver models for personalized driving assistance. Veh Syst Dyn 53(12):1705–1720

    Article  Google Scholar 

  • Levine S, Bell D, Jaros L et al (1999) The navchair assistive wheelchair navigation system. IEEE Trans Rehabil Eng 7(4):443–451

    Article  Google Scholar 

  • Livingstone R (2010) A critical review of powered mobility assessment and training for children. Disabil Rehabil Assist Technol 5(6):392–400

    Article  Google Scholar 

  • Ly MV, Martin S, Trivedi MM (2013) Driver classification and driving style recognition using inertial sensors. In: IEEE intelligent vehicles symposium (IV), pp 1040–1045

  • MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Cam LML, Neyman J (eds) Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol 1. University of California Press, Berkeley, pp 281–297

    Google Scholar 

  • Miller DP, Slack MG (1995) Design and testing of a low-cost robotic wheelchair prototype. Auton Robots 2(1):77–88

    Article  Google Scholar 

  • Miro J, Black R, Andonovski B, Dissanayake G (2013) Development of a novel evidence-based automated powered mobility device competency assessment. In: IEEE international conference on rehabilitation robotics, Seattle

  • Moghaddam A, Pineau J, Frank J, Archambault P, Routhier F (2011) Mobility profile and wheelchair driving skills of powered wheelchair users: sensor-based event recognition using a support vector machine classifier. In: 33rd Annual international conference of the IEEE EMBS, Boston, pp 7336–7339

  • Mohamed NA, Ahmed MN, Farag A (1999) Modified fuzzy c-mean in medical image segmentation. In: IEEE international conference proceedings of the acoustics, speech, and signal processing, ICASSP ’99, vol 6. IEEE Computer Society, Washington, DC, pp 3429–3432

  • Montesano L, Diaz M, Bhaskar S, Minguez J (2010) Towards an intelligent wheelchair system for users with cerebral palsy. IEEE Trans Neural Syst Rehabil Eng 18(2):193–202

    Article  Google Scholar 

  • Newell A, Gregor P (1999) Extra-ordinary human–machine interaction: what can be learned from people with disabilities? Cogn Tech Work 1(2):78–85

    Article  Google Scholar 

  • Nilsson L, Durkin J (2014) Assessment of learning powered mobility use—applying grounded theory to occupational performance. J Rehabil Res Dev 51(6):963–974

    Article  Google Scholar 

  • Nuttin M, Demeester E, Vanhooydonck D, Brussel HV (2001) Shared autonomy for wheelchair control: attempts to assess the user’s autonomy. In: Levi P, Schanz M (eds) Autonome mobile systeme, Informatik Aktuell. Springer, Berlin, pp 127–133

    Google Scholar 

  • Pellegrini N, Guillon B, Prigent H, Pellegrini M, Orlikovski D, Raphael J, Lofaso F (2004) Optimization of power wheelchair control for patients with severe Duchenne muscular dystrophy. Neuromuscul Disord 14(5):297–300

    Article  Google Scholar 

  • Ramze M, Lelieveldt B, Reiber J (1998) A new cluster validity index for the fuzzy c-mean. J Pattern Recognit Lett 19:237–246

    Article  Google Scholar 

  • Rath JJ, Senouth C, Popieul JC (2019) Personalised lane keeping assist strategy: adaptation to driving style. IET Control Theory Appl 13(1):106–115

    Article  Google Scholar 

  • Sathyanarayana A, Sadjadi S, Hansen JH (2012) Leveraging sensor information from portable devices towards automatic driving maneuver recognition. In: IEEE 15th International conference on intelligent transportation systems (ITSC), pp 660–665

  • Schwarz C, Gaspar J, Brown T (2018) The effect of reliability on drivers’ trust and behavior in conditional automation. Cognition Tech Work, pp 1–14

  • Shan S, Sandham W, Granat M, Sterr A (2005) Mri fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization. IEEE Trans Inf Technol Biomed 9(3):459–467

    Article  Google Scholar 

  • Sorrento G, Archambault P, Routhier F, Dessureault D, Boissy P (2011) Assessment of joystick control during the performance of powered wheelchair driving tasks. J Neuroeng Rehabil 8:31

    Article  Google Scholar 

  • Suzurikawa J, Kamo M, Iida N et al (2016) Quantitative evaluation of changes in joystick operation induced by downhill turning prevention control for a power wheelchair. IEEJ Trans Electron Inf Syst 136(9):1318

    Google Scholar 

  • Sysoev M, Kos A, Guna J, Pogacnik M (2017) Estimation of the driving style based on the users’ activity and environment influence. Sensors (Basel) 17(10):1–15

    Google Scholar 

  • van der Slikke R, Berger M, Bregman D, Veeger H (2016) From big data to rich data: the key features of athlete wheelchair mobility performance. J Biomech 49(14):3340–3346

    Article  Google Scholar 

  • Wang Y, Qu W, Ge Y, Sun X, Zhang K (2018) Effect of personality traits on driving style: psychometric adaptation of the multidimensional driving style inventory in a chinese sample. PLoS One 13(9):1–17

    Google Scholar 

  • Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80–83

    Article  Google Scholar 

  • William D (2011) What is assessment for learning? Stud Educ Eval 37(1):3–14

    Article  Google Scholar 

  • Winyard G, Luker C, Nichols P (1976) The uses and usefulness of electrically powered indoor wheelchairs. Rheumatol Rehabil 15(4):254–263

    Article  Google Scholar 

  • Wood JM, Worringham C, Kerr G, Mallon K, Silburn P (2005) Quantitative assessment of driving performance in Parkinson’s disease. Neurol Neurosurg Psychiatry 76:176–180

    Article  Google Scholar 

  • Yang L, Ma R, Zhang H, Guan W, Jiang S (2018) Driving behavior recognition using eeg data from a simulated car-following experiment. Accid Anal Prev 116:30–40

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank all the subjects in the evaluations. We would also like to thank the Foundation Motrice (http://www.lafondationmotrice.org/) and the CEREMH for their assistance on the test.

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Authors and Affiliations

  1. LIGM, ESIEE PARIS, 2 BD Blaise Pascal, 93162, Noisy-le-Grand, France

    Ting Wang

  2. LISV, University of Versailles Saint-Quentin-en-Yvelines, 10-12 Avenue de l’Europe, 78140, Vélizy, France

    Amina Gacem, Eric Monacelli & Olivier Rabreau

  3. ESIEE PARIS, 2 BD Blaise Pascal, 93162, Noisy-le-Grand, France

    Tarik Al-ani

Authors
  1. Amina Gacem
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  2. Eric Monacelli
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  3. Ting Wang
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  4. Olivier Rabreau
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  5. Tarik Al-ani
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Correspondence to Ting Wang.

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Conflict of interest

Amina Gacem declares that he has received research grants from the Foundation Motrice. Eric Monacelli, Olivier Rabreau, Ting Wang and Tarik Al-ani declare that they have no conflict of interest.

Informed consent in studies with human subjects

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Informed consent was obtained from all patients for being included in the study.

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Gacem, A., Monacelli, E., Wang, T. et al. Assessment of wheelchair skills based on analysis of driving style. Cogn Tech Work 22, 193–207 (2020). https://doi.org/10.1007/s10111-019-00563-6

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