Wearable sensors for the monitoring of movement disorders

Nahed Jalloul, Nahed Jalloul

Abstract

This paper offers a review of the implementation of current wearable sensing technologies in monitoring the movement and activity of patients suffering from movement disorders. Recent literature has focused on incorporating simple and reliable wearable technologies for the continuous and objective monitoring of patient movement during normal daily activities. However, the use of such wearable sensing technologies has yet to find its way to clinical practice. In the following, the basic elements of such monitoring systems and their applications are introduced, and a discussion regarding current clinical applications is presented.

Keywords: Activity monitoring; Movement disorders; Wearable sensors.

Copyright © 2018 Chang Gung University. Published by Elsevier B.V. All rights reserved.

Figures

Fig. 1
Fig. 1
Diagram showing the different categories of wearable sensors, common body fixed positions based on sensor type, and a general summary of health related applications .
Fig. 2
Fig. 2
General scheme of development of a monitoring system for the quantification of movement.

References

    1. Johansson D., Malmgren K., Alt Murphy M. Wearable sensors for clinical applications in epilepsy, Parkinson's disease, and stroke: a mixed-methods systematic review. J Neurol. 2018:1432–1459.
    1. Pulliam C.L., Heldman D.A., Brokaw E.B., Mera T.O., Mari Z.K., Burack M.A. Continuous assessment of levodopa response in Parkinson's disease using wearable motion sensors. IEEE Trans Biomed Eng. 2018;65:159–164.
    1. Ameli S., Naghdy F., Stirling D., Naghdy G., Aghmesheh M. Objective clinical gait analysis using inertial sensors and six minute walking test. Pattern Recogn. 2017;63:246–257.
    1. Gilmore G., Jog M. Movement disorders rehabilitation. Springer; Cham: 2017. Future perspectives: assessment tools and rehabilitation in the new age; pp. 155–182.
    1. Muennig P.A., Glied S.A. What changes in survival rates tell us about US health care. Health Aff. 2010;29:2105–2113.
    1. Gulley S.P., Rasch E.K., Chan L. If we build it, who will come?: Working-age adults with chronic health care needs and the medical home. Med Care. 2011;49:149–155.
    1. Gulley S.P., Rasch E.K., Chan L. Ongoing coverage for ongoing care: access, utilization, and out-of-pocket spending among uninsured working-aged adults with chronic health care needs. Am J Public Health. 2011;101:368–375.
    1. Liu Y., Jiang X., Cao T., Wan F., Mak P.U., Mak P.I. Virtual environments human-computer interfaces and measurement systems (VECIMS) IEEE International Conference; 2012. Implementation of SSVEP based BCI with Emotiv EPOC; pp. 34–37.
    1. Vokorokos L., Mados B., Ádám N., Baláz A. Data acquisition in non-invasive brain-computer interface using emotiv epoc neuroheadset. Acta Electrotechnica et Informatica. 2012;12:5–8.
    1. Mateu-Mateus M., Guede-Fernández F., García-González M.A. 6th European conference of the International Federation for Medical and Biological Engineering. Springer; Cham: 2015. RR time series comparison obtained by H7 polar sensors or by photoplethysmography using smartphones: breathing and devices influences; pp. 264–267.
    1. O'Brien B., Gisby T., Anderson I.A. Electroactive polymer actuators and devices (EAPAD) International Society for Optics and Photonics; 2014. Stretch sensors for human body motion; p. 9056. 905618.
    1. OQuigley C., Sabourin M., Coyle S., Connolly J., Condall J., Curran K. Wearable and Implantable body sensor networks workshops (BSN workshops) IEEE; 2014. Characteristics of a piezo-resistive fabric stretch sensor glove for home-monitoring of rheumatoid arthritis; pp. 23–26.
    1. Daneault J.F., Kanzler C., Lee S., Golabchi F., Vergara-Diaz G., Carvalho G.F. Exploring the use of wearable sensors to monitor drug response of patients with Parkinson’s disease in the home setting. Neurology. 2017;88:P4.002.
    1. Eskofier B.M., Lee S.I., Baron M., Simon A., Martindale C.F., Garner H. An overview of smart shoes in the internet of health things: gait and mobility assessment in health promotion and disease monitoring. Appl Sci. 2017;7:986.
    1. Vogel J., Auinger A., Riedl R., Kindermann H., Helfert M., Ocenasek H. Digitally enhanced recovery: Investigating the use of digital self-tracking for monitoring leisure time physical activity of cardiovascular disease (CVD) patients undergoing cardiac rehabilitation. PloS one. 2017;12:e0186261.
    1. Patel S., Park H., Bonato P., Chan L., Rodgers M. A review of wearable sensors and systems with application in rehabilitation. J Neuroeng Rehabil. 2012;9:21.
    1. Luinge H.J. Twente University Press; 2002. Inertial sensing of human movement.
    1. Ghodssi R., Lin P., editors. MEMS materials and processes handbook. Springer Science & Business Media; 2011.
    1. Woodman O.J. University of Cambridge, Computer Laboratory; 2007. An introduction to inertial navigation.
    1. Luinge H.J., Veltink P.H. Measuring orientation of human body segments using miniature gyroscopes and accelerometers. Med Biol Eng Comput. 2005;43:273–282.
    1. Bulling A., Blanke U., Schiele B. A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput Surv (CSUR) 2014;46:33.
    1. Lambrecht S., del-Ama A.J. Springer. Emerging Therapies in Neurorehabilitation; 2014. Human movement analysis with inertial sensors; pp. 305–328.
    1. Aggarwal J.K., Ryoo M.S. Human activity analysis: A review. ACM Comput Surv (CSUR) 2011;43:16.
    1. Rigas G., Tzallas A.T., Tsipouras M.G., Bougia P., Tripoliti E.E., Baga D. Assessment of tremor activity in the Parkinson’s disease using a set of wearable sensors. IEEE Trans Inf Technol Biomed. 2012;16:478–487.
    1. Mariani B., Jiménez M.C., Vingerhoets F.J., Aminian K. On-shoe wearable sensors for gait and turning assessment of patients with Parkinson's disease. IEEE Trans Biomed Eng. 2013;60:155–158.
    1. Cole B.T., Roy S.H., De Luca C.J., Nawab S.H. Dynamical learning and tracking of tremor and dyskinesia from wearable sensors. IEEE Trans Neural Syst Rehabil Eng. 2014;22:982–991.
    1. Bernad-Elazari H., Weiss A., Oren S., Cohen Y., Mirelman A., Giladi N. Using a wearable sensor to evaluate activity and motor response fluctuations in patients with Parkinson's disease (pd): Preliminary findings: 675. Mov Disord. 2015;30:S265.
    1. Lennon T., Bernier T., Tamayo D., Goldberg C., Mankodiya K. IEEE; 2015. Multi-sensory system for monitoring dyskinesia in movement disorders. Biomedical engineering conference (NEBEC) pp. 1–2.
    1. Jalloul N., Porée F., Viardot G., L'Hostis P., Carrault G. Engineering in Medicine and Biology Society (EMBC), IEEE; 2015. Detection of Levodopa induced Dyskinesia in Parkinson's DIsease patients based on activity classification; pp. 5134–5137.
    1. Jalloul N., Porée F., Viardot G., L'Hostis P., Carrault G. Advances in Biomedical Engineering (ICABME), IEEE; 2015. Feature selection for activity classification and Dyskinesia detection in Parkinson's disease patients; pp. 146–149.
    1. Howcroft J., Kofman J., Lemaire E.D., McIlroy W.E. Analysis of dual-task elderly gait in fallers and non-fallers using wearable sensors. J Biomech. 2016;49:992–1001.
    1. Bourke A.K., Klenk J., Schwickert L., Aminian K., Ihlen E.A., Mellone S. Engineering in Medicine and Biology Society (EMBC), IEEE; 2016. Fall detection algorithms for real-world falls harvested from lumbar sensors in the elderly population: a machine learning approach; pp. 3712–3715.
    1. Kobsar D., Osis S.T., Boyd J.E., Hettinga B.A., Ferber R. Wearable sensors to predict improvement following an exercise intervention in patients with knee osteoarthritis. J Neuroeng Rehabil. 2017;14:94.
    1. Daneault J.F., Lee S.I., Golabchi F.N., Patel S., Shih L.C., Paganoni S. Proceedings of the second IEEE/ACM international conference on connected health: applications, systems and engineering technologies. 2017. Estimating bradykinesia in Parkinson's disease with a minimum number of wearable sensors; pp. 264–265.
    1. Thilarajah S., Clark R.A., Williams G. Wearable sensors and Mobile Health (mHealth) technologies to assess and promote physical activity in stroke: a narrative review. Brain Impair. 2016;17:34–42.
    1. Chen S., Lach J., Lo B., Yang G.Z. Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review. IEEE J Biomed Health Inf. 2016;20:1521–1537.
    1. Sánchez-Ferro Á., Elshehabi M., Godinho C., Salkovic D., Hobert M.A., Domingos J. New methods for the assessment of Parkinson's disease (2005 to 2015): A systematic review. Mov Disord. 2016;31:1283–1292.
    1. de Lima A.L., Evers L.J., Hahn T., Bataille L., Hamilton J.L., Little M.A. Freezing of gait and fall detection in Parkinson’s disease using wearable sensors: a systematic review. J Neurol. 2017;264:1642–1654.

Source: PubMed

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