Development of an assessment method of forearm pronation/supination motor function based on mobile phone accelerometer data for an early diagnosis of Parkinson’s disease

International Journal of Bio-Science and Bio-Technology

Volumn 8, Issue 2, 2016, Pages 1-10

  Choi, Ji Hun ^a   Ma, Hyeo Il ^b   Kim, Yun Joong ^b   Lee, Unjoo ^a  

^a Hallym University   (South Korea)

^b Hallym University College of Medicine   (South Korea)

Author keywords

Accelerometer; Motor symptoms; Parkinson s disease; Sensor

Indexed keywords

EID: 84964800798     PISSN: 22337849     EISSN: None     Source Type: Journal    
DOI: 10.14257/ijbsbt.2016.8.2.01     Document Type: Article

References (32)

1. G. F. Dunton, E. Dzubur, K. Kawabata, B. Yanez, B. Bo and S. Intille, “Development of a smartphone application to measure physical activity using sensor-assisted self-report”, Frontiers in Public Health, vol. 28, article 12, (2014).
2. K. H. Kim, M. Y. Jeon, J. Y. Lee, J. H. Jeong and G. M Jeong, “A study on the app development using sensor signals from smartphone and smart watch”, Advanced Science and Technology Letters, vol. 62, (2014), pp. 66-69.
3. W. Maetzker, J. Domingos, K. Srulijes, J. J. Ferreira and B. R. Bloem, “Quantitative wearable sensors for objective assessment of Parkinson”s disease”, Movement Disorders, vol. 28, no. 12, (2013), pp. 1628-1637.
4. M. Susi, V. Renaudin and G. Lachapelle, “Motion Mode Recognition and Step Detection Algorithms for Mobile Phone Users”, Sensors, vol. 13, (2013), pp. 1539-1562.
5. A. Svensson and C. Larsson, “A Mobile Phone App for Dietary Intake Assessment in Adolescents: An Evaluation Study”, JMIR mHealth uHealth (Mobile and Ubiquitous Health), vol. 3, no. 4, e93, (2015).
6. F. Taffoni, V. Focaroli, D. Formica, E. Gugliemelli, F. Keller and J.M. Iverson, “Sensor-based technology in the study of motor skills in infants at risk for ASD”, Proceedings of IEEE RAS EMBS International Conference Biomed Robot Biomechatron, (2012), pp. 1879-1883.
7. J. P. Tregarthen, J. Lock and A. M. Darcy, “Development of a Smartphone Application for Eating Disorder Self-Monitoring”, International Journal of Eating Disorders, vol. 48, (2015), pp. 972-982.
8. Z. Ye, Y. Li, Q. Zhao and X. Liu, “A Falling Detection System with wireless sensor for the Elderly People Based on Ergonomics”, International Journal of Smart Home, vol. 8, no. 1, (2014), pp. 187-196.
9. S. Abbate, M. Avvenuti and J. Light, “Usability Study of a Wireless Monitoring System among Alzheimer”s Disease Elderly Population”, International Journal of Telemedicine and Applications, (2014). http://dx.doi.org/10.1155/2014/617495.
10. J. Cancela, M. Pastorino, A. T. Tzallas, M. G. Tsipouras, G. Rigas, M. T. Arredondo and D.I. Fotiadis, “Wearability Assessment of a Wearable System for Parkinson’s Disease Remote Monitoring Based on a Body Area Network of Sensors”, Sensors, vol. 14, (2014), pp. 1723-1725.
11. H. Dai, H. Lin and T. C. Lueth, “Quantitative assessment of parkinsonian bradykinesia based on an inertial measurement unit”, BioMedical Enineering, vol. 14-68, (2015).
12. M. Memedi, D. Nyholm, A. Johansson, S. Pålhagen, T. Willows, H. Widner, J. Linder and J. Westin, “Validity and responsiveness of at-home touchscreen assessments in advanced Parkinson’s disease”, IEEE Journal of Biomedical and Health Informatics, vol. 19, no. 6, (2015), pp. 1829-1834.
13. S. H. Roy, B. T. Cole, L. D. Gilmore, C. J. De Luca, C. A. Thomas, M. M. Saint-Hilaire and S. H. Nawab, “High-Resolution Tracking of Motor Disorders in Parkinson”s Disease During Unconstrained Activity”, Movement Disorders, vol. 28, no. 8, (2013), pp. 1080-1087.
14. N. A. Capela, E. D. Lemaire and N. Baddour, “Novel algorithm for a smartphone-based 6-minute walk test application: algorithm, application development, and evaluation”, Journal of NeuroEngineering and Rehabilitation, vol.12:19, DOI 10.1186/s12984-015-0013-9, (2015).
15. R. LeMoyne, T. Mastroianni, M. Cozza, C. Coroian and W. Grundfest, “Implementation of an iPhone for characterizing Parkinson”s disease tremor through a wireless accelerometer application”, 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS) Argentina, (2010) September, pp. 4954-4958.
16. R. LeMoyne, “Wearable and wireless accelerometer systems for monitoring Parkinson”s disease patients-A perspective review”, Advances in Parkinson’s disease, vol. 2, no. 4, (2013), pp. 113-115.
17. R. LeMoyne, T. Mastroianni and W. Grundfest, “Identification of common molecular subsequences wireless accelerometer configuration for monitoring Parkinson’s disease hand tremor”, Advances in Parkinson’s Disease vol. 2, no. 2, (2013), pp. 62-67.
18. N. Kostikis, D. Hristu-Varsakelis, M. Arnaoutoglou and C. Kotsavasiloglou, “Smartphone-based evaluation of parkinsonian hand tremor: Quantitative measurements vs. clinical assessment scores”, 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE Press, Chicago, IL, (2014), pp. 906-909.
19. V. Parra, G. Figueras, M. Huerta, A. Marzinotto and R. Gonzalez, “A Smartphone Application for Parkinson Tremor Detection”, Workshop IEEE Latincom, At Santiago de Chile, (2013).
20. Wesley J. E. Teskey, M. Elhabiby and N. El-Sheimy, “Inertial Sensing to Determine Movement Disorder Motion Present before and after Treatment”, Sensors, vol. 12, (2012), pp. 3512-3527.
21. S. Arora, V. Venkataraman, S. Donohue, K. M. Biglan, E. R. Dorsey and M. A. Little, “HIGH ACCURACY DISCRIMINATION OF PARKINSON’S DISEASE PARTICIPANTS FROM HEALTHY CONTROLS USING SMARTPHONES”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE Press, Florence, (2014), pp. 3641-3644.
22. M. Sekine, T. Tamura, M. Yoshida, Y. Suda, Y. Kimura, H. Miyoshi, Y. Kijima, Y. Higashi and T. Fujimoto, “A gait abnormality measure based on root mean square of trunk acceleration”, JOURNAL OF NEUROENGINEERING AND REHABILITATION, vol. 10, (2013), pp. 118.
23. K. C. Lan and W. Y. Shih, “Early Diagnosis of Parkinson’s Disease using a Smartphone”, Procedia Computer Science, vol. 34, (2014), pp. 305-312.
24. M. V. Albert, S. Toledo, M. Shapiro and K. Kording, “Using mobile phones for activity recognition in Parkinson’s patients”, Frontiers in Neurology, vol. 3, (2012).
25. S. Das, L. Trutoiu, A. Murai, D. Alcindor, M. Oh, F. D. Torre and J. Hodgins, “Quantitative measurement of motor symptoms in Parkinson’s disease: a study with full-body motion capture data”, Engineering in Medicine and Biology Society (EMBC), Annual International Conference of the IEEE, (2011), pp. 6789-6792.
26. S. Patel, K. Lorincz, R. Hughes, N. Huggins, J. Growdon, D. Standaert, M. Akay, J. Dy, M. Welsh and P. Bonato, “Monitoring Motor Fluctuations in Patients With Parkinson’s Disease Using Wearable Sensors”, IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, vol. 13, no. 6, (2009), pp. 864-873.
27. A. T. Tzallas, M. G. Tsipouras, G. Rigas, D. G. Tsalikakis, E. C. Karvounis, M. Chondrogiorgi, F. Psomadellis, J. Cancela, M. Pastorino, M. A. T. Waldmeyer, S. Konitsiotis and D. I. Fotiadis, “PERFORM: A System for Monitoring, Assessment and Management of Patients with Parkinson’s Disease”, Sensors, vol. 14, (2014), pp. 21329-21357.
28. M. Pastorino, M. T. Arredondo, J. Cancela and S. Guillen, “Wearable sensor network for health monitoring: the case of Parkinson disease”, Journal of Physics: Conference Series 450, (2013).
29. S. Mazilu, M. Hardegger, Z. Zhu, D. Roggen, G. Troster, M. Plotnik and J. M. Hausdorff, “Online detection of freezing of gait with smartphones and machine learning techniques”, 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (Pervasive Health), (2012), pp. 123-130.
30. J. W. Kim, J. Lee, J. Y. Shin, J. H. Lee, Y. R. Kwon, D. Y. Kwon, K. W. Park and G. M. Eom, “Measurement of Angular Velocity of Forearm Pronation/Supination Movement for the Quantification of the Bradykinesia in Idiopathic Parkinson’s Disease Patients”, Journal of Biomedical Engineering Research, vol. 30, no. 2, (2009), pp. 142-146.
31. S. J. Kang, J. H. Choi, Y. J. Kim, H.-I. Ma and U. Lee, “Development of an acquisition and visualization of forearm tremors and pronation/supination motor activities in a smartphone based environment for an early diagnosis of Parkinson’s disease”, Advanced Science and Technology Letters, vol. 116, (2015), pp. 209-212.
32. U. Lee, J. H. Choi, Y. J. Kim and H.-I. Ma, “Quantitative assessment of forearm pronation/supination motor functions using a smartphone accelerometer for an early diagnosis of Parkinson’s disease”, Advanced Science and Technology Letters, vol. 116, (2015), pp. 240-243.