HEALTH MONITORING BY FUNCTIONAL INDICATORS WITH SENSORS ASSISTANCE IN REHABILITATION MEDICINE: SYSTEMATIC REVIEW

Authors

  • V. P. Martsenyuk University of Bielsko-Biala, Republic of Poland
  • I. V. Kachur Institute of Artificial Intelligence Problems MES and NAS of Ukraine
  • A. S. Sverstyuk I. Horbachevsky Ternopil State Medical University
  • V. I. Bondarchuk I. Horbachevsky Ternopil State Medical University
  • Yu. V. Zavidnyuk I. Horbachevsky Ternopil State Medical University
  • V. B. Koval I. Horbachevsky Ternopil State Medical University
  • O. M. Mochulska I. Horbachevsky Ternopil State Medical University

DOI:

https://doi.org/10.11603/2415-8798.2019.2.9971

Keywords:

physical rehabilitation, medical rehabilitation, sensors, touch sensitive tool

Abstract

At present, people’s need for rapid and effective rehabilitation processes is growing significantly. People with limited functional capabilities need sensor devices that are used for rehabilitation in order to improve human health and to return to a decent standard of living. Sensory devices are used for the health monitoring system of people that are divided into portable and movable. After all, rehabilitation treatment requires patients of different age groups with cardio-pulmonary pathology, neurological disorders, orthopedic disorders, etc. The article covers electromechanical, electric, optical and thermal sensors, acoustic signal transducers or sensitive sensors, sensors and their application at different stages of rehabilitation.

The aim of the study – to conduct an analysis of modern domestic and foreign literature on types of sensors in rehabilitation medicine.

Materials and Methods. The study used biblio-semantic and analytical methods in the following electronic databases: Science Direct, PubMed, Scopus and Google Scholar. When looking for an article, annotations are analyzed. Inclusion criteria were: (1) physical and medical rehabilitation and / or auxiliary system supported by sensors and computer, (2) systems developed for the human body, and (3) documents written in English. If the expected criterion was found, the full text was reviewed.

Results and Discussion. During the study, a systematic review and analysis of recent publications, mainly foreign scientific medical, biological and technical literature on the types, principles of work, development and the possibilities of using sensors in rehabilitation medicine was conducted. Sensory technologies continue to be fully developed and offer convenient opportunities to use to improve the functional state of health. A wide range of studies included and reflected in this review included various types of sensors. To date, devices used to monitor physical activity are divided into sensors that measure biological parameters such as pressure, heart rate, respiratory rate - pulse meter, tonometer, spirometer and motion sensors – pedometers, accelerometers, trackers of activity. Some of the most commonly used sensors used in rehabilitation are electromyography, galvanic skin reaction, electrocardiography, electroencephalography and sensory sensors and systems that control motor and physiological activity of a person. The article for examples considered: 1 – a typical algorithm for the operation of devices for monitoring the functional state of human health, 2 – diagnostic tool ALLADIN with sensors, which includes nine components. In the electronic databases: Science Direct, PubMed, Scopus and Google Scholar, no previously published work was found whereby the authors synthesized a combination of sensors with hardware, robotic, computer, and rehabilitation systems for patients of different ages.

Conclusions. In the analysis of modern domestic and foreign literature on types of sensors in rehabilitation medicine, the development and application of sensor devices in physical and medical rehabilitation has been studied and described. All publications indicate that sensory sensors are attached to devices that allow measuring functional performance of a person's health. Therefore, sensory technologies in rehabilitation medicine continue to develop in a comprehensive manner and are frequently used to diagnose, assess and monitor the health of a person.

Author Biographies

V. P. Martsenyuk, University of Bielsko-Biala, Republic of Poland

доктор техн. наук, проф., Університет Бельсько-Бяли, Республіка Польща

I. V. Kachur, Institute of Artificial Intelligence Problems MES and NAS of Ukraine

канд. біол. наук, доцент, заступник директора Інституту проблем штучного інтелекту МОН і НАН України, ikachur3903@gmail.com, моб. т.  +380505653903

A. S. Sverstyuk, I. Horbachevsky Ternopil State Medical University

канд. техн. наук, доц. кафедри медичної інформатики ДВНЗ “Тернопільський державний медичний університет імені І. Я. Горбачевського МОЗ України”, sverstyuk@tdmu.edu.ua, моб. тел. +380677695968

V. I. Bondarchuk, I. Horbachevsky Ternopil State Medical University

канд. біол. наук, асистент кафедри фізичної реабілітації, здоров’я людини та фізичного виховання ДВНЗ “Тернопільський державний медичний університет імені І. Я. Горбачевського МОЗ України”, bondarchykvi@tdmu.edu.ua, моб. тел. +380988692343

Yu. V. Zavidnyuk, I. Horbachevsky Ternopil State Medical University

асистент кафедри медичної реабілітації ДВНЗ “Тернопільський державний медичний університет імені І. Я. Горбачевського МОЗ України”,

zavidniuk@tdmu.edu.ua, моб. тел. +380684754115

V. B. Koval, I. Horbachevsky Ternopil State Medical University

канд. мед. наук, доцен кафедри фізичної реабілітації, здоров’я людини та фізичного виховання ДВНЗ “Тернопільський державний медичний університет імені І. Я. Горбачевського МОЗ України”, Koval@tdmu.edu.ua

O. M. Mochulska, I. Horbachevsky Ternopil State Medical University

канд. мед. наук, асистент кафедри дитячих хвороб з дитячою хірургією ДВНЗ “Тернопільський державний медичний університет імені І. Я. Горбачевського МОЗ України”, mochulska_om@tdmu.edu.ua, моб. тел. +380677941595

References

Mohammaddan, S., & Komeda, T. (2010). Wire-driven mechanism for finger rehabilitation devices. Proceedings of the IEEE int. conf. on mechatronics and automation in China, 1015-1018. DOI: https://doi.org/10.1109/ICMA.2010.5588077

Young, H.L., & Mutharasan, R. (2005). What Is a Biosensor? Sensor technology handbook. Science Direct, 6, 161-180.

Al-Jumaily, A., & Olivares, RA. (2009). Electromyogram (EMG) driven system based virtual reality for prosthetic and rehabilitation devices. 11 th Int Conf on Information integration and web-based applications and services. Malaysia. ACM, 582-586. DOI: https://doi.org/10.1145/1806338.1806448

Goto, S., Nakamura, M., & Sugi, T. (2008). Development of meal assistance orthosis for disabled persons using EOG signal and dish image. International Journal of Advanced Mechatronic Systems, 1 (2), 107-115. DOI: https://doi.org/10.1504/IJAMECHS.2008.022009

Gupta, R., Bera, JN., & Mitra, M. (2010). Development of an embedded system and MATLAB-based GUI for online acquisition and analysis of ECG signal. Measurement, 43, 1119-1126. DOI: https://doi.org/10.1016/j.measurement.2010.05.003

Silvia, P., Stefano, M., Stefania, B., Sara, C., Ilaria, P., & Barbara, L. (2010). Early assessment of neuro-rehabilitation technology: a case study. J. Biomed. Eng. Technol. IndraSci, 4 (3), 232-244.

Prange, G.B., Jannink, M.J.A., Groothuis-Oudshoorn, C.G.M., Hermens, H.J. & Ijzerman, M.J. (2006). Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke. Journal of rehabilitation research and development, 43 (2), 171-184. DOI: https://doi.org/10.1682/JRRD.2005.04.0076

Karatas, M., Cetin, N., Bayramoglu, M. & Dilek, A. (2004). Trunk muscle strength in relation to balance and functional disability in unihemispheric stroke patients. American journal of physical medicine and rehabilitation, 83 (2), 81-87. DOI: https://doi.org/10.1097/01.PHM.0000107486.99756.C7

Pantelopoulos, A., & Bourbakis, N. (2008). A survey on wearable biosensor systems for health monitoring. Proceedings of the 30th Annual IEEE int. conf. on engineering in medicine and biology society in BC, USA, 4887-4890. DOI: https://doi.org/10.1109/IEMBS.2008.4650309

Katherine, M.T., Holly, A.Y., David, J., & Feil-Seifer Maja, J.M. (2008). Survey of domain-specific performance measures in assistive robotic technology. Proceedings of the 8th workshop on performance metrics for intelligent systems in USA, 116-123.

Janis, J.D., & Jonathan, R.W. (2008). Brain-computer interfaces in neurological rehabilitation. J. Lancet. Neurol, 11 (17), 1032-1043.

Steinisch, M., & Guarnieri, BM. (2009). Virtual reality and robotics for neuro-motor rehabilitation of ischemic stroke patients. World congress on medical physics and biomedical engineering, 61-63. DOI: https://doi.org/10.1007/978-3-642-03889-1_17

Martsenyuk, V.P. (2018). Study of classification of immunosensors from viewpoint of medical tasks. Medical informatics and engineering, 1 (41), 13-19. doi: https://dx.doi.org/10.11603/mie.1996-1960.2018.1.8887. DOI: https://doi.org/10.11603/mie.1996-1960.2018.1.8887

Särelä, A., Salminen, J., Koskinen, E., Kirkeby, O., Korhonen, I., & Walters D. (2009). A home-based care model for outpatient cardiac rehabilitation based on mobile technologies. 3rd Int Conf on Pervasive Computing Technologies for Healthcare, 1-18.

Nagaoka, T., Sakatani, K., Awano T., Yokose, N., Hoshino, T., Murata, Y., … & Eda, H. (2010). Development of a new rehabilitation system based on a brain-computer interface using near-infrared spectroscopy. Adv. Exp. Med. Biol, 662, 497-503. DOI: https://doi.org/10.1007/978-1-4419-1241-1_72

Majdalawieh, O., Gu, J., Bai, T., & Cheng, G. (2003). Biomedical signal processing and rehabilitation engineering: a review. Proceedings of IEEE Pacific Rim conference on communications, computers and signal processing in Canada, 2, 1004-1007. DOI: https://doi.org/10.1109/PACRIM.2003.1235954

Kozyavkina, O.V., Kozyavkina, N.V., Hordiyevych, М.S., Voloshyn, Т.B.,. Lysovych, V.I, Babelyuk, V.Y., … Popovych, I.L. (2018). Forecasting caused by Kozyavkin© metod changes in hand function parameters in children with spastic form of cerebral palsy at their baseline levels as well as EEG, HRV AND GDV. Zdobutky klinichnoi i eksperymentalnoi medytsyny - Achievements of clinical and experimental medicine, 4, 17-35.

Enzo Pasquale, S., Gemignani, A., Paradiso, R., & Taccini, N. (2005). Performance evaluation of sensing fabrics for monitoring physiological and biomechanical variables. IEEE T Inf Technol B., 9 (3), 345-352.

Ahamed, NU., Sundaraj, K., & Poo, TS. (2013). Design and development of an automated, portable and handheld tablet personal computer-based data acquisition system for monitoring electromyography signals during rehabilitation. Proc Inst Mech Eng Part H-J Eng Med., 262-274. DOI: https://doi.org/10.1177/0954411912471493

Burns, A., Greene, B.R., McGrath, M.J. & O'Shea (2010). SHIMMERTM: A Wireless Sensor Platform for Noninvasive Biomedical Research. IEEE Sens J., 10 (9), 1527-1534. DOI: https://doi.org/10.1109/JSEN.2010.2045498

Schabowsky, C.N., & Godfrey, S.B. (2010). Development and pilot testing of HEXORR: hand Exoskeleton rehabilitation robot. J Neuroeng Rehabil., 7, 36. DOI: https://doi.org/10.1186/1743-0003-7-36

Sasidhar, S., Panda, S.K., & Xu, J. (2010). A real time control algorithm for a myoelectric glove for the rehabilitation of wrist and elbow of stroke patients. 8th IEEE Int Conf on control and automation, 745-749. DOI: https://doi.org/10.1109/ICCA.2010.5524324

Virtual reality training may be as effective as regular therapy after stroke (2017). Online issue of Neurology®, the medical journal of the American Academy of Neurology. Retrieved from: https://www.sciencedaily.com/releases/2017/11/171115175655.htm.

Warren, J.M., Ekelund, U., Besson, H., Mezzani, A., Geladas, N., & Vanhees L. (2010). Assessment of physical activity – a review of methodologies with reference to epidemiological research: a report of the exercise physiology section of the European Association of Cardiovascular Prevention and Rehabilitation. European Journal of Cardiovascular Prevention and Rehabilitation, 17 (2), 127-139. DOI: https://doi.org/10.1097/HJR.0b013e32832ed875

Popovych, D.V., Sopel, O.O., Bondarchuk, V.I., & Diachenko, M.M. (2019). Analiz fizychnoi aktyvnosti studentok pershoho roku navchannia v Ternopilskomu derzhavnomu medychnomu universyteti imeni I.Ya.Horbachevskoho [Analysis of physical activity of students in the first year of study at Ternopil State Medical University named after I.Ya.Gorbachevsky]. Zdobutky klinichnoi i eksperymentalnoi medytsyny – Achievements of clinical and experimental medicine, 4, 123-127 [in Ukrainian].

Tsvyakh, A., & Hospodarskyy, A. (2017). Telerehabilitation of patients with injuries of the lower extremities. Telemed J E Health, 23, 1011-1015. doi: 10.1089/tmj.2016.0267. DOI: https://doi.org/10.1089/tmj.2016.0267

Colcombe, S., & Kramer, A.F. (2003). Fitness effects on the cognitive function of older adults: a meta-analytic study. Psychological Science March, 14 (2), 125-130. DOI: https://doi.org/10.1111/1467-9280.t01-1-01430

Loginov, S.I. (2007) Vozmozhnosti otcenki fizicheskoi aktivnosti cheloveka s pomoshchiu datchikov dvizheniia- akselerometrov [Possibilities of estimation of physical activity of a person with the help of motion sensors-accelerometers]. Vestnik novykh meditcinskikh tekhnologii – Herald of new medical technologies, 14 (1), 149-150 [in Russian].

Judith E Deutsch, Megan Borbely, Jenny Filler, Karen Huhn, & Phyllis Guarrera-Bowlby (2008). Use of a low-cost, commercially available gaming console (Wii) for rehabilitation of an adolescent with cerebral palsy. Physical Therapy, 88, 1196-1207. DOI: https://doi.org/10.2522/ptj.20080062

Prati, Andrea, Shan Caifeng, Wang, & Kevin I-Kaic (2019). Sensors, vision and networks: From video surveillance to activity recognition and health monitoring. Journal of ambient intelligence and smart environments, 11, 5-22.

Tran, B. & Saratoga, CA (US) (2015). Health monitoring system. Patent application publication US 2015/0125832 A1, G09B 19/0092 (2013.01); G09B5/00 (2013.01).

Kachur, I.V. (2016). Razrabotka intellektualnoi sistemy s bioadaptivnym upravleniem dlya psikhofiziologicheskoi reabilitatcii [Development of an intellectual system with bioadaptive control for psychophysiological rehabilitation.]. Materialy Mezhdunarodnoi nauchno-tekhnicheskoi konferentcii «Iskusstvennyi intellekt. Intellektualnye transportnye sistemy» – Materials of the International scientific and technical conference "Artificial Intelligence. Intelligent Transport Systems "(Be-Safe 2016), Belarus, 23-26 [in Russian].

Mazzoleni, S., Van Vaerenbergh, J., Toth, A., Munih, M., Guglielmelli, E. & Dario, P. (2005). ALLADIN: a novel mechatronic platform for assessing post-stroke functional recovery. Proceedings of the international conference on rehabilitation robotics. Chicago, IL, USA, 156-159. DOI: https://doi.org/10.1109/ICORR.2005.1501074

Ino, S., Sato, M., Hosono, M., & Izumi, T. (2009). Development of a soft metal hydride actuator using a laminate bellows for rehabilitation systems. Sens actuator B-Chem., 136 (1), 86-91. DOI: https://doi.org/10.1016/j.snb.2008.10.054

Published

2019-04-16

How to Cite

Martsenyuk, V. P., Kachur, I. V., Sverstyuk, A. S., Bondarchuk, V. I., Zavidnyuk, Y. V., Koval, V. B., & Mochulska, O. M. (2019). HEALTH MONITORING BY FUNCTIONAL INDICATORS WITH SENSORS ASSISTANCE IN REHABILITATION MEDICINE: SYSTEMATIC REVIEW. Bulletin of Scientific Research, (2), 5–12. https://doi.org/10.11603/2415-8798.2019.2.9971

Issue

Section

REVIEWS AND ORIGINAL RESEARCH