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Gesture Recognition-Based Interaction with Smartwatch and Electric Wheelchair for Assistive Mobility and Navigation

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IOT with Smart Systems

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 251))

Abstract

Smartwatches are becoming increasingly accessible to end users, thereby placing the power of mobile technology at their fingertips. On the other hand, electric powered wheelchairs are designed to assist people with mobility impairments to move with ease. However, people with cognitive difficulties may not be able to navigate the wheelchair as required and may require additional support from wearable devices. This paper presents a gesture recognition-based navigation for the wheelchair using a smartwatch. Users are able to navigate the wheelchair using gestures that would be easily interpreted by the smartwatch. This capability opens up new mobility possibilities never before envisaged.

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References

  1. Wearables, statistics. https://www.statista.com/outlook/319/100/wearables/worldwide (Accessed Nov 2020)

  2. Mourcou, Q., Fleury, A., Dupuy, P., Diot, B., Franco, C., & Vuillerme, N.: Wegoto: A Smartphone-based approach to assess and improve accessibility for wheelchair users. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1194–1197. IEEE (2013, July)

    Google Scholar 

  3. Bhat, A., Badri, P., Reddi, U.S.: Wearable Devices: The Next Big Thing in CRM. Cognizant 20–20 Insights (2014)

    Google Scholar 

  4. Fereidouni, S., Sheikh Hassani, M., Talebi, A., Rezaie, A.H.: A novel design and implementation of wheelchair navigation system using Leap Motion sensor. Disabil. Rehabil. Assistive Technol. 1–7 (2020)

    Google Scholar 

  5. Schwesinger, D., Shariati, A., Montella, C., Spletzer, J.: A smart wheelchair ecosystem for autonomous navigation in urban environments. Auton. Robot. 41(3), 519–538 (2017)

    Article  Google Scholar 

  6. Debnath, T., Abadin, A.Z., Hossain, M.A.: Android controlled smart wheelchair for disabilities. Glob. J. Comput. Sci. Technol. (2018)

    Google Scholar 

  7. Civitarese, G., Mascetti, S., Butifar, A., Bettini, C.: Automatic detection of urban features from wheelchair users’ movements. In: 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 1–10. IEEE (2019, March)

    Google Scholar 

  8. Mascetti, S., Civitarese, G., El Malak, O., Bettini, C.: SmartWheels: Detecting urban features for wheelchair users’ navigation. Pervasive Mobile Comput. 62, 101115 (2020)

    Google Scholar 

  9. Zang, K., Shen, J., Huang, H., Wan, M., Shi, J.: Assessing and mapping of road surface roughness based on GPS and accelerometer sensors on bicycle-mounted smartphones. Sensors 18(3), 914 (2018)

    Article  Google Scholar 

  10. Cavanini, L., Cimini, G., Ferracuti, F., Freddi, A., Ippoliti, G., Monteriù, A., Verdini, F.: A QR-code localization system for mobile robots: application to smart wheelchairs. In: 2017 European Conference on Mobile Robots (ECMR), pp. 1–6. IEEE (2017, September)

    Google Scholar 

  11. UPase, S.U.: Speech recognition based robotic system of wheelchair for disable people. In: 2016 International Conference on Communication and Electronics Systems (ICCES), pp. 1–5. IEEE (2016, October)

    Google Scholar 

  12. Kim, J.E., Bessho, M., Sakamura, K.: Towards a smartwatch application to assist students with disabilities in an IoT-enabled campus. In: 2019 IEEE 1st Global Conference on Life Sciences and Technologies (LifeTech), pp. 243–246. IEEE (2019, March)

    Google Scholar 

  13. Gaggi, O., Palazzi, C.E., Ciman, M., Bujari, A.: Stepbywatch: A smartwatch-based enhanced navigation system for visually impaired users. In: 2018 15th IEEE Annual Consumer Communications and Networking Conference (CCNC), pp. 1–5. IEEE (2018, January)

    Google Scholar 

  14. Bardaro, G., Bascetta, L., Ceravolo, E., Farina, M., Gabellone, M., Matteucci, M.: MPC-based control architecture of an autonomous wheelchair for indoor environments. Control. Eng. Pract. 78, 160–174 (2018)

    Article  Google Scholar 

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Markus, E.D., Ntsinyi, T., Monacelli, E. (2022). Gesture Recognition-Based Interaction with Smartwatch and Electric Wheelchair for Assistive Mobility and Navigation. In: Senjyu, T., Mahalle, P., Perumal, T., Joshi, A. (eds) IOT with Smart Systems. Smart Innovation, Systems and Technologies, vol 251. Springer, Singapore. https://doi.org/10.1007/978-981-16-3945-6_9

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  • DOI: https://doi.org/10.1007/978-981-16-3945-6_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-3944-9

  • Online ISBN: 978-981-16-3945-6

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