Abstract
The study of the characteristics of hand tremors of the patients suffering from Parkinson’s disease (PD) offers an effective way to detect and assess the stage of the disease’s progression. During the semi-quantitative evaluation, neurologists label the PD patients with any of the (0–4) Unified Parkinson’s Diseases Rating Scale (UPDRS) score based on the intensity and prevalence of these tremors. This score can be bolstered by some other modes of assessment as like gait analysis to increase the reliability of PD detection. With the availability of conventional smartphones with a built-in accelerometer sensor, it is possible to acquire the 3-axes tremor and gait data very easily and analyze them by a trained algorithm. Thus we can remotely examine the PD patients from their homes and connect them to trained neurologists if required. The objective of this study was to investigate the usability of smartphones for assessing motor impairments (i.e. tremors and gait) that can be analyzed from accelerometer sensor data. We obtained 98.5% detection accuracy and 91% UPDRS labeling accuracy for 52 PD patients and 20 healthy subjects. The result of this study indicates a great promise for developing a remote system to detect, monitor, and prescribe PD patients over long distances. It will be a tremendous help for the older population in developing countries where access to a trained neurologist is very limited. Also, in a pandemic situation like COVID-19, patients from developed countries can be benefited from such a home-oriented PD detection and monitoring system.
This project was funded by the innovation fund of ICT division, Ministry of Posts, Telecommunications and Information Technology, People Republic of Bangladesh
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Elias, W.J., Shah, B.B.: Tremor. JAMA 311, 948–954 (2014)
Parkinson’s Disease Statistics, Parkinson’s News Today. https://parkinsonsnewstoday.com/parkinsons-disease-statistics/. Accessed 20 Jan 2020
Deep brain stimulation: a chance to thrive, The Daily Star. https://www.thedailystar.net/health/disease/deep-brain-stimulation-chance-thrive-1466578. Accessed 2 Feb 2020
Rascol, O., et al.: A five-year study of the incidence of dyskinesia in patients with early Parkinson’s disease who were treated with ropinirole or levodopa. New Engl. J. Med. 342, 1484–1491 (2000)
Bhat, M., Inamdar, S., Kulkarni, D., Kulkarni, G., Shriram, R.: Parkinson’s disease prediction based on hand tremor analysis. In: 2017 International Conference on Communication and Signal Processing, pp. 0625–0629. IEEE, India (2017)
MDS-Unified Parkinson’s Disease Rating Scale, Movement Disorder Society. https://www.movementdisorders.org/. Accessed 25 Jan 2020
Politis, M., Wu, K., Molloy, S.P.G.B., Chaudhuri, K.E., Piccini, P.: Parkinson’s disease symptoms: the patient’s perspective. Mov. Disord. 25(11), 1646–1651 (2010)
Teravainen, T., Calne, D.B.: Action tremor in Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 43(3), 257–263 (1980)
Koller, W.E., Veter-Overfield, B., Barter, R.: Tremors in early Parkinson’s disease. Clin. Neuropharmacol. 12(4), 293–297 (1989)
Bazgir, O., Habibi, S.A.H., Palma, L., Pierleoni, P., Nafees, S.: Classification system for assessment and home monitoring of tremor in patients with Parkinson’s disease. J. Med. Signals Sens. 8(2), 65–72 (2018)
Rocon, E., et al.: Rehabilitation robotics: a wearable exo-skeleton for tremor assessment and suppression. In: 2005 IEEE International Conference on Robotics and Automation, pp. 2271–2276. IEEE, Barcelona, Spain (2005)
Seki, M., et al.: Development of robotic upper limb orthosis with tremor suppressiblity and elbow joint movability. In: 2011 IEEE International Conference on Systems. Man, and Cybernetics, pp. 729–735. IEEE, Anchorage (2011)
Morrison, S., et al.: Bilateral tremor relations in Parkinson’s disease: effects of mechanical coupling and medication. Parkins. Relat. Disord. 14, 298–308 (2008)
Pistacchi, M., et al.: Gait analysis and clinical correlations in early Parkinson’s disease. Funct. Neurol. 32, 28–34 (2017)
Mirelman, A., et al.: Gait impairments in Parkinson’s disease. Lancet Neurol. 18, 697–708 (2019)
Son, M., et al.: Evaluation of the turning characteristics according to the severity of Parkinson disease during the timed up and go test. Aging Clin. Exp. Res. 29, 1191–1199 (2017)
Leusmann, P., Mollering, C., Klack, L., Kasugai, K., Ziefle, M., Rumpe, B.: Your floor knows where you are: sensing and acquisition of movement data. In: 2011 IEEE 12th International Conference Mobile Data Management, vol. 2, pp. 61–66 (2011)
Vera-Rodriguez, R., Mason, J.S., Fierrez, J., Ortega-Garcia, J.: Comparative analysis and fusion of spatiotemporal information for footstep recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35, 823–834 (2013)
Colyer, S.L., Evans, M., Cosker, D.P., Salo, A.I.T.: A review of the evolution of vision-based motion analysis and the integration of advanced computer vision methods towards developing a markerless system. Sports Med. Open 4, 24 (2018)
Tao, W., Liu, T., Zheng, R., Feng, H.: Gait analysis using wearable sensors. Sensors 12, 2255–2283 (2012)
Dominguez, G., Cardiel, E., Arias, S., Rogeli, P.: A digital goniometer based on encoders for measuring knee-joint position in an orthosis. In: Proceedings of the: World Congress on Nature and Biologically Inspired Computing (NaBIC) 2013, Fargo (2013)
Bae, J., Tomizuka, M.: A tele-monitoring system for gait rehabilitation with an inertial measurement unit and a shoe-type ground reaction force sensor. Mechatronics 23, 646–651 (2013)
Godfrey, A., Del Din, S., Barry, G., Mathers, J.C., Rochester, L.: Instrumenting gait with an accelerometer: a system and algorithm examination. Med. Eng. Phys. 37(4), 400–407 (2015)
Xuan, Y., et al.: Gait cycle recognition based on wireless inertial sensor network. IERI Procedia 4, 44–52 (2013)
Perumala, S.V., Sankarb, R.: Gait and tremor assessment for patients with Parkinson’s disease using wearable sensors. ICT Express 2(4), 168–174 (2016)
Abdulhay, E., Arunkumar, N., Narasimhan, K., Vellaiappan, E., Venkatraman, V.: Gait and tremor investigation using machine learning techniques for the diagnosis of Parkinson disease. Future Gener. Comput. Syst. 83, 366–373 (2018)
Mamun, K.A., Alhussein, M., Sailunaz, K., Islam, M.S.: Cloud based framework for Parkinson’s disease diagnosis and monitoring system for remote healthcare applications. Future Gener. Comput. Syst. 66, 36–47 (2017)
Radovic, M., et al.: Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinform. 18, 1–4 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Sajal, M.S.R., Ehsan, M.T., Vaidyanathan, R., Wang, S., Aziz, T., Mamun, K.A. (2020). UPDRS Label Assignment by Analyzing Accelerometer Sensor Data Collected from Conventional Smartphones. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds) Brain Informatics. BI 2020. Lecture Notes in Computer Science(), vol 12241. Springer, Cham. https://doi.org/10.1007/978-3-030-59277-6_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-59277-6_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59276-9
Online ISBN: 978-3-030-59277-6
eBook Packages: Computer ScienceComputer Science (R0)