• Users Online: 157
  • Print this page
  • Email this page


 
 Table of Contents  
CHAPTER 6: SCIENTIFIC BACKGROUND OF PHYSICAL AND REHABILITATION MEDICINE
Year : 2019  |  Volume : 2  |  Issue : 2  |  Page : 107-110

6.2 Scientific background of physical and rehabilitation medicine: Biomedical sciences and engineering in rehabilitation


1 Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Aichi, Japan
2 Department of Clinical Sciences, Karolinska Institutet, Danderyd Hospital, Stockholm, Sweden

Date of Web Publication11-Jun-2019

Correspondence Address:
Prof. Masahiko Mukaino
Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Aichi
Japan
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jisprm.jisprm_25_19

Rights and Permissions

How to cite this article:
Mukaino M, Borg K, Saitoh E. 6.2 Scientific background of physical and rehabilitation medicine: Biomedical sciences and engineering in rehabilitation. J Int Soc Phys Rehabil Med 2019;2, Suppl S1:107-10

How to cite this URL:
Mukaino M, Borg K, Saitoh E. 6.2 Scientific background of physical and rehabilitation medicine: Biomedical sciences and engineering in rehabilitation. J Int Soc Phys Rehabil Med [serial online] 2019 [cited 2019 Aug 20];2, Suppl S1:107-10. Available from: http://www.jisprm.org/text.asp?2019/2/2/107/259351




  Introduction Top


Biomedical sciences and engineering in rehabilitation are one of the scientific fields in physical and rehabilitation medicine (PRM).[1] This field study diagnostic measures and interventions including physical modalities suitable to minimize impairment, to control symptoms, and to optimize people's capacity.

As described by Stucki et al., biomedical rehabilitation sciences and engineering are applied sciences which from the biomedical perspective of human functioning develop and evaluate

  • Diagnostic tools for the assessment of impairment in body functions and structures as well as the evaluation of capacity of activity, i.e., physical diagnostics for example muscle strength, motor and cognitive function, as well as neurophysiological testing
  • Interventions to stabilize, improve, restore, and compensate for impaired body functions and structures, for example, pain, fatigue, and memory problems
  • Interventions to prevent secondary impairment, medical complications, and risks, for example, joint contractures, depression, osteoporosis, and fractures due to falling.


Biomedical rehabilitation sciences and engineering include rehabilitation research in nanotechnology and robotic.[1] Recently, there have been marked developments in technology for use in the field of rehabilitation. These will affect research in this area, and the approach in clinical settings will also change with the use of these new technologies. This manuscript gives a brief overview of the use of technology in the field of rehabilitation for the purposes of intervention, as well as for the evaluation of patients' abilities. Below are some examples of the development of PRM in this field.


  Robotics Top


The use of robotics is an emerging topic in the field of rehabilitation medicine, and many kinds of robotics have been developed and commercialized for use in rehabilitation clinics. The main uses of robotics are related to assistance with independence, practical rehabilitation, and care.

Robotics for assistance with independence, which helps patients in their daily lives, has mainly been developed in the field of spinal cord injury (SCI) rehabilitation. For example, there are many kinds of powered exoskeleton-type robotics to help SCI patients walk. Most of these exoskeletons consist of trunk support and powered limbs, which enable walking with the support of walkers or crutches.[2],[3],[4] There is also a medial-type exoskeleton without trunk support[5] that has been developed for use by people who use wheelchairs, which are still the essential means of mobility for SCI patients.

Robotics are primarily used in rehabilitation for practical assistance with activities such as walking,[6],[7],[8] finger movement,[9],[10] and upper limb movement.[11],[12],[13]

A number of trials document the effectiveness of the use of robotics in rehabilitation. Several randomized controlled trials show that the additional use of robotics to standard practice for upper limb rehabilitation presents positive results.[14] There is also number of studies regarding the use of robotics for gait practice, and several show possible superiority of robotic training to usual care although this area needs further investigation.[15]

The use of robotics enables the monitoring of movement during repetitive practice and provides feedback for patients and clinicians.[6],[7],[12],[13] Robotics can also allow the setting of gradual difficulty levels for patients.[8] These uses could enhance patients' functional restoration. On the other hand, at present, the use of robotics does not cover task-specific training, which is important in achieving rehabilitation goals and should be optimized for each patient. Appropriate combinations of robotic training and therapist-mediated rehabilitation would seem to be necessary.

Robotics has also been developed for assistance with care; however, this kind of use of robotics is still not common. This may due to some technical issues regarding safety. Care assistance robotics should be used by caregivers, including families, who may not familiar with those devices, and the required safety level for these robots could be much higher than the robots used by health professionals. There is still much room left for the development of care assistance robotics. Instead, there have been recent developments in companion robots, which aim to provide verbal assistance, supervision, or communication for patients. There are several projects in which patients with mild cognitive impairment live with the help of these kinds of robots.[16],[17]

The use of robotics has the potential to improve rehabilitation practice. The further development of technology in this field would be expected.


  Neurofeedback Top


Electromyograms have been used in rehabilitation to provide the biofeedback of muscle activity, during practice, for the improvement of muscle tone control. Recent developments in technology have enabled more direct feedback on the brain's activity. The research of brain–computer interfaces, using activity from electroencephalograms or near-infrared spectroscopy, has enabled the feedback of the neural activity of the brain. Using neural activity data, various kinds of feedback, such as visual feedback, active joint movement by electrical stimulation, or somatosensory feedback with passive joint movement by exoskeleton, are shown to be possibly effective for functional restoration[18],[19],[20] although the actual effectiveness and clinical feasibility should be further investigated.


  Use of Virtual Reality Top


A growing number of papers describe the use of virtual reality (VR) in rehabilitation, such as its use either to augment the feedback from a certain movement to emphasize a change in that movement, or to motivate patients by providing attractive environments for daily exercise, which can otherwise be tiring or repetitive.[21],[22] Current rapid developments in VR technology could further contribute to improving the likelihood of patients' commitment to rehabilitation programs. Based on the studies' findings, the results of rehabilitation could be facilitated by additional training with VR;[23] however, the overall superiority of the VR training to the usual training is not shown. The merits of the use of VR in clinical settings should be further investigated.


  Neuromodulation Techniques Top


An increasing number of reports have been published regarding brain stimulation using such means as transcranial direct current stimulation and repetitive transcranial magnetic stimulation. These techniques are considered to have the potential to change neural activity and to facilitate improvement in the movement of individuals with neural deficits.[24],[25] Several stimulation strategies that could be effective for the restoration of motor function have been reported in previous studies, such as enhancing the brain's activity in the motor area,[26] suppressing the activity in the contralesional motor area,[27] and facilitating the motor learning process through cerebellar stimulation.[28] Further investigation in the clinical effects and their mechanisms would facilitate the use of these techniques in clinical settings.


  Technology in the Evaluation of Patients' Abilities Top


Marked advances in objective measurement procedures in the field of rehabilitation have been made. The use of technology for evaluation can be seen in various areas, such as gait analysis, activity monitoring, upper limb motion analysis, and the evaluation of swallowing.


  Gait Analysis and Upper Limb Motion Analysis Top


Gait analysis has been a major research topic in the field of rehabilitation; however, it has not been widely used in clinics due to problems relating to the high cost and the time-consuming measurement process. The latest developments in analysis techniques have enabled clinician-friendly systems that could be used in clinical settings. For example, low-cost three-dimensional (3D) motion analysis using body markers or depth sensors, as well as wearable motion analysis systems using the data from accelerometers, have been commercialized.[29],[30],[31],[32] There are also attempts to develop the motion analysis-based gait evaluation paradigm to quantify the impairment and to use when making clinical decisions in the field of rehabilitation.[33],[34]

Several studies using motion capture systems for upper limbs have been made, although there is still some technical limitation to evaluate upper limb movement with a wide range of motion of joints and complexity of combined joint movement.[35] Robotic systems for evaluation have also been developed, and also, many of the recently developed upper limb exercise robotics include evaluation systems, while some of them validated with clinical scales.[36],[37],[38]


  Activity Monitoring Top


These days, there are many commercialized devices for activity monitoring. Popular systems include devices that use accelerometery or wristband-type photoplethysmography. Some reports suggest the use of these kinds of systems to monitor physical activity and for safety, although there is still some concern regarding the accuracy of monitoring.[39],[40],[41] There are also reports showing the feasibility of “smart clothing” systems, using the clothes combined with heart rate- or accelerometry-based movement monitoring systems.[42]


  Medical Imaging for Rehabilitation Top


Developments in technology have enabled the evaluation of movement through medical imaging. For example, a 320-detector-row multislice computed tomography (CT) scan, developed for high-speed imaging, has an area detector covering a 16.0-cm area in a single rotation without performing a helical scan. This has enabled the development of the swallowing CT [Figure 1], used to observe the dynamic movements of swallowing as a series of 3D image data. This may facilitate a further understanding of dysphagia, which is one of the major therapeutic targets in the field of rehabilitation medicine.[43]
Figure 1: Swallowing computed tomography

Click here to view


Of course, other more traditional research topics are related to biomedical sciences and engineering in rehabilitation. Examples for this are methods of exercise and training, joint mobilization and manipulation techniques, massage and myofascial techniques, mechanical stimulation techniques, and other physical modalities including transcranial magnetic stimulation.

These are only examples of the increasing knowledge of the background of different conditions seen in the PRM specialty revealed by basic medical and engineering research during recent decades. An intensified activity of the research in coming years will most certainly yield more knowledge that will lead to better outcome after rehabilitation in different disorders concerning diagnostic assessment, improving function, and avoiding secondary complications.

The gain of biomedical and engineering research requires implementation of the results in clinical practice. Thus, the studies performed in PRM should, as an addition to parameters of organ structure, also include outcome measures for functioning, activity, and participation. Results from biomedical research and engineering are almost always published in specialized journals. It is of great importance that results of basic medical research are disseminated in scientific journals and conferences devoted to rehabilitation and also via different websites including Cochrane Rehabilitation.[44]


  Summary Top


Scientific field of PRM in biomedical sciences and engineering includes the study of diagnostic measures and interventions to minimize impairment and symptoms and to control and optimize people's capacity. The recent development of new modalities in analysis and intervention technologies in the field of rehabilitation medicine is reviewed. These technologies may contribute to improvements in the quality of rehabilitation practice. Further developments in this field would be expected.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Stucki G, Grimby G. Organizing human functioning and rehabilitation research into distinct scientific fields. Part I: Developing a comprehensive structure from the cell to society. J Rehabil Med 2007;39:293-8.  Back to cited text no. 1
    
2.
Esquenazi A, Talaty M, Packel A, Saulino M. The reWalk powered exoskeleton to restore ambulatory function to individuals with thoracic-level motor-complete spinal cord injury. Am J Phys Med Rehabil 2012;91:911-21.  Back to cited text no. 2
    
3.
Quintero HA, Farris RJ, Hartigan C, Clesson I, Goldfarb M. A powered lower limb orthosis for providing legged mobility in paraplegic individuals. Top Spinal Cord Inj Rehabil 2011;17:25-33.  Back to cited text no. 3
    
4.
Kolakowsky-Hayner SA. Safety and feasibility of using the EksoTM bionic exoskeleton to aid ambulation after spinal cord injury. J Spine 2013;S4:3. [doi: 10.4172/2165-7939.S4-003].   Back to cited text no. 4
    
5.
Tanabe S, Hirano S, Saitoh E. Wearable power-assist locomotor (WPAL) for supporting upright walking in persons with paraplegia. NeuroRehabilitation 2013;33:99-106.  Back to cited text no. 5
    
6.
Jezernik S, Colombo G, Keller T, Frueh H, Morari M. Robotic orthosis Lokomat: A rehabilitation and research tool. Neuromodulation 2003;6:108-15.  Back to cited text no. 6
    
7.
Hesse S, Uhlenbrock D, Werner C, Bardeleben A. A mechanized gait trainer for restoring gait in nonambulatory subjects. Arch Phys Med Rehabil 2000;81:1158-61.  Back to cited text no. 7
    
8.
Hirano S, Saitoh E, Tanabe S, Tanikawa H, Sasaki S, Kato D, et al. The features of gait exercise assist robot: Precise assist control and enriched feedback. NeuroRehabilitation 2017;41:77-84.  Back to cited text no. 8
    
9.
Stein J, Bishop L, Gillen G, Helbok R. Robot-assisted exercise for hand weakness after stroke: A pilot study. Am J Phys Med Rehabil 2011;90:887-94.  Back to cited text no. 9
    
10.
Adamovich SV, Fluet GG, Mathai A, Qiu Q, Lewis J, Merians AS, et al. Design of a complex virtual reality simulation to train finger motion for persons with hemiparesis: A proof of concept study. J Neuroeng Rehabil 2009;6:28.  Back to cited text no. 10
    
11.
Gijbels D, Lamers I, Kerkhofs L, Alders G, Knippenberg E, Feys P, et al. The Armeo spring as training tool to improve upper limb functionality in multiple sclerosis: A pilot study. J Neuroeng Rehabil 2011;8:5.  Back to cited text no. 11
    
12.
Lum PS, Burgar CG, Shor PC, Majmundar M, Van der Loos M. Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke. Arch Phys Med Rehabil 2002;83:952-9.  Back to cited text no. 12
    
13.
Hogan N, Krebs HI, Charnnarong J. MIT-MANUS: A workstation for manual therapy and training. I. Proceedings IEEE International Workshop on Robot and Human Communication. Tokyo, Japan: IEEE; 1992. p. 161-5.  Back to cited text no. 13
    
14.
Veerbeek JM, Langbroek-Amersfoort AC, van Wegen EE, Meskers CG, Kwakkel G. Effects of robot-assisted therapy for the upper limb after stroke. Neurorehabil Neural Repair 2017;31:107-21.  Back to cited text no. 14
    
15.
Louie DR, Eng JJ. Powered robotic exoskeletons in post-stroke rehabilitation of gait: A scoping review. J Neuroeng Rehabil 2016;13:53.  Back to cited text no. 15
    
16.
Wu YH, Wrobel J, Cristancho-Lacroix V, Kamali L. Designing an assistive robot for older adults: The ROBADOM project. IRBM 2013;34:119-23.  Back to cited text no. 16
    
17.
Schroeter C, Mueller S, Volkhardt M. Realization and user evaluation of a companion robot for people with mild cognitive impairments. IEEE International Conference on Robotics and Automation. Karlsruhe, Germany: IEEE; 2013. p.1145-51.   Back to cited text no. 17
    
18.
Silvoni S, Ramos-Murguialday A, Cavinato M, Volpato C, Cisotto G, Turolla A, et al. Brain-computer interface in stroke: A review of progress. Clin EEG Neurosci 2011;42:245-52.  Back to cited text no. 18
    
19.
Ono T, Shindo K, Kawashima K, Ota N, Ito M, Ota T, et al. Brain-computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke. Front Neuroeng 2014;7:19.  Back to cited text no. 19
    
20.
Cervera MA, Soekadar SR, Ushiba J, Millán JD, Liu M, Birbaumer N, et al. Brain-computer interfaces for post-stroke motor rehabilitation: A meta-analysis. Ann Clin Transl Neurol 2018;5:651-63.  Back to cited text no. 20
    
21.
Sveistrup H. Motor rehabilitation using virtual reality. J Neuroeng Rehabil 2004;1:10.  Back to cited text no. 21
    
22.
Levin MF, Weiss PL, Keshner EA. Emergence of virtual reality as a tool for upper limb rehabilitation: Incorporation of motor control and motor learning principles. Phys Ther 2015;95:415-25.  Back to cited text no. 22
    
23.
Laver KE, Lange B, George S, Deutsch JE, Saposnik G, Crotty M, et al. Virtual reality for stroke rehabilitation. Cochrane Database Syst Rev 2017;11:CD008349.  Back to cited text no. 23
    
24.
Elsner B, Kugler J, Pohl M, Mehrholz J. Transcranial direct current stimulation (tDCS) for improving activities of daily living, and physical and cognitive functioning, in people after stroke. Cochrane Database Syst Rev 2016;3:CD009645.  Back to cited text no. 24
    
25.
Dionísio A, Duarte IC, Patrício M, Castelo-Branco M. The use of repetitive transcranial magnetic stimulation for stroke rehabilitation: A systematic review. J Stroke Cerebrovasc Dis 2018;27:1-31.  Back to cited text no. 25
    
26.
Kim YH, You SH, Ko MH, Park JW, Lee KH, Jang SH, et al. Repetitive transcranial magnetic stimulation-induced corticomotor excitability and associated motor skill acquisition in chronic stroke. Stroke 2006;37:1471-6.  Back to cited text no. 26
    
27.
Nowak DA, Grefkes C, Dafotakis M, Eickhoff S, Küst J, Karbe H, et al. Effects of low-frequency repetitive transcranial magnetic stimulation of the contralesional primary motor cortex on movement kinematics and neural activity in subcortical stroke. Arch Neurol 2008;65:741-7.  Back to cited text no. 27
    
28.
Celnik P. Understanding and modulating motor learning with cerebellar stimulation. Cerebellum 2015;14:171-4.  Back to cited text no. 28
    
29.
Carse B, Meadows B, Bowers R, Rowe P. Affordable clinical gait analysis: An assessment of the marker tracking accuracy of a new low-cost optical 3D motion analysis system. Physiotherapy 2013;99:347-51.  Back to cited text no. 29
    
30.
Mukaino M, Ohtsuka K, Tanikawa H, Matsuda F, Yamada J, Itoh N, et al. Clinical-oriented three-dimensional gait analysis method for evaluating gait disorder. J Vis Exp. 2018;(133). doi: 10.3791/57063.  Back to cited text no. 30
    
31.
Fern'ndez-Baena A, Susín A. Biomechanical validation of upper-body and lower-body joint movements of Kinect motion capture data for rehabilitation treatments. Intelligent networking and collaborative systems (INCoS), 2012. Bucharest, Romania: IEEE; 2012.  Back to cited text no. 31
    
32.
Cloete T, Scheffer C. Benchmarking of a full-body inertial motion capture system for clinical gait analysis. Conf Proc IEEE Eng Med Biol Soc 2008;2008:4579-82.  Back to cited text no. 32
    
33.
Li HT, Huang JJ, Pan CW, Chi HI, Pan MC. Inertial sensing based assessment methods to quantify the effectiveness of post-stroke rehabilitation. Sensors (Basel) 2015;15:16196-209.  Back to cited text no. 33
    
34.
Pongpipatpaiboon K, Mukaino M, Matsuda F, Ohtsuka K, Tanikawa H, Yamada J, et al. The impact of ankle-foot orthoses on toe clearance strategy in hemiparetic gait: A cross-sectional study. J Neuroeng Rehabil 2018;15:41.  Back to cited text no. 34
    
35.
Valevicius AM, Jun PY, Hebert JS, Vette AH. Use of optical motion capture for the analysis of normative upper body kinematics during functional upper limb tasks: A systematic review. J Electromyogr Kinesiol 2018;40:1-5.  Back to cited text no. 35
    
36.
Germanotta M, Vasco G, Petrarca M, Rossi S, Carniel S, Bertini E, et al. Robotic and clinical evaluation of upper limb motor performance in patients with Friedreich's Ataxia: An observational study. J Neuroeng Rehabil 2015;12:41.  Back to cited text no. 36
    
37.
Coderre AM, Zeid AA, Dukelow SP, Demmer MJ, Moore KD, Demers MJ, et al. Assessment of upper-limb sensorimotor function of subacute stroke patients using visually guided reaching. Neurorehabil Neural Repair 2010;24:528-41.  Back to cited text no. 37
    
38.
Otaka E, Otaka Y, Kasuga S, Nishimoto A, Yamazaki K, Kawakami M, et al. Clinical usefulness and validity of robotic measures of reaching movement in hemiparetic stroke patients. J Neuroeng Rehabil 2015;12:66.  Back to cited text no. 38
    
39.
Bourke AK, O'Brien JV, Lyons GM. Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait Posture 2007;26:194-9.  Back to cited text no. 39
    
40.
Wallen MP, Gomersall SR, Keating SE, Wisløff U, Coombes JS. Accuracy of heart rate watches: Implications for weight management. PLoS One 2016;11:e0154420.  Back to cited text no. 40
    
41.
Chen MD, Kuo CC, Pellegrini CA, Hsu MJ. Accuracy of wristband activity monitors during ambulation and activities. Med Sci Sports Exerc 2016;48:1942-9.  Back to cited text no. 41
    
42.
Majumder S, Mondal T, Deen MJ. Wearable sensors for remote health monitoring. Sensors (Basel) 2017;17. pii: E130.  Back to cited text no. 42
    
43.
Inamoto Y, Fujii N, Saitoh E, Baba M, Okada S, Katada K, et al. Evaluation of swallowing using 320-detector-row multislice CT. Part II: Kinematic analysis of laryngeal closure during normal swallowing. Dysphagia 2011;26:209-17.  Back to cited text no. 43
    
44.
Cochrane rehabilitation [Internet]. Available from: http://rehabilitation.cochrane.org/. [Last cited on 2019 Feb 27].  Back to cited text no. 44
    


    Figures

  [Figure 1]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Introduction
Robotics
Neurofeedback
Use of Virtual R...
Neuromodulation ...
Technology in th...
Gait Analysis an...
Activity Monitoring
Medical Imaging ...
Summary
References
Article Figures

 Article Access Statistics
    Viewed107    
    Printed8    
    Emailed0    
    PDF Downloaded25    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]