Review of Automated Systems for Upper Limbs Functional Assessment in Neurorehabilitation

Traditionally, the assessment of upper limb (UL) motor function in neurorehabilitation is carried out by clinicians using standard clinical tests for objective evaluation, but which could be influenced by the clinician’s subjectivity or expertise. The automation of such traditional outcome measures (tests) is an interesting and emerging field in neurorehabilitation. In this paper, a systematic review of systems focused on automation of traditional tests for assessment of UL motor function used in neurological rehabilitation is presented. A systematic search and review of related articles in the literature were conducted. The chosen works were analyzed according to the automation level, the data acquisition systems, the outcome generation method, and the focus of assessment. Finally, a series of technical requirements, guidelines, and challenges that must be considered when designing and implementing fully-automated systems for upper extremity functional assessment are summarized. This paper advocates the use of automated assessment systems (AAS) to build a rehabilitation framework that is more autonomous and objective.

[1]  Qiang Fang,et al.  Automated Fugl-Meyer Assessment using SVR model , 2014, 2014 IEEE International Symposium on Bioelectronics and Bioinformatics (IEEE ISBB 2014).

[2]  Ridi Ferdiana,et al.  High-resolution automated Fugl-Meyer Assessment using sensor data and regression model , 2017, 2017 3rd International Conference on Science and Technology - Computer (ICST).

[3]  J. Thonnard,et al.  The use of outcome measures in physical medicine and rehabilitation within Europe. , 2001, Journal of rehabilitation medicine.

[4]  Ilaria Carpinella,et al.  Quantitative assessment of upper limb motor function in Multiple Sclerosis using an instrumented Action Research Arm Test , 2014, Journal of NeuroEngineering and Rehabilitation.

[5]  Benjamin F. Miller,et al.  Encyclopedia & dictionary of medicine, nursing, & allied health , 1992 .

[6]  Erienne V. Olesh,et al.  Automated Assessment of Upper Extremity Movement Impairment due to Stroke , 2014, PloS one.

[7]  Vítor Tedim Cruz,et al.  A novel system for automatic classification of upper limb motor function after stroke: an exploratory study. , 2014, Medical engineering & physics.

[8]  A. Fugl-Meyer,et al.  The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance. , 1975, Scandinavian journal of rehabilitation medicine.

[9]  Jonghyun Kim,et al.  Automated Evaluation of Upper-Limb Motor Function Impairment Using Fugl-Meyer Assessment , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  M. Ketelaar,et al.  Comparing contents of functional outcome measures in stroke rehabilitation using the International Classification of Functioning, Disability and Health , 2007, Disability and rehabilitation.

[11]  M. A. Villán-Villán,et al.  Objective motor assessment for personalized rehabilitation of upper extremity in brain injury patients. , 2018, NeuroRehabilitation.

[12]  Arthur Prochazka,et al.  Fully-automated test of upper-extremity function , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  M. Maier,et al.  Upper Limb Outcome Measures Used in Stroke Rehabilitation Studies: A Systematic Literature Review , 2016, PloS one.

[14]  Carlos Balaguer,et al.  The Automated Box and Blocks Test an Autonomous Assessment Method of Gross Manual Dexterity in Stroke Rehabilitation , 2017, TAROS.

[15]  Daniel Simonsen,et al.  Design and test of an automated version of the modified Jebsen test of hand function using Microsoft Kinect , 2017, Journal of NeuroEngineering and Rehabilitation.

[16]  Carlos Balaguer,et al.  Towards a framework for rehabilitation and assessment of upper limb motor function based on Serious Games , 2018, 2018 IEEE 6th International Conference on Serious Games and Applications for Health (SeGAH).

[17]  M. M. Reijne,et al.  Accuracy of human motion capture systems for sport applications; state-of-the-art review , 2018, European journal of sport science.

[18]  R. Gassert,et al.  Upper limb assessment using a Virtual Peg Insertion Test , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[19]  Enrique J. Gómez,et al.  A First Step for the Automation of Fugl-Meyer Assessment Scale for Stroke Subjects in Upper Limb Physical Neurorehabilitation , 2015, ICIMTH.

[20]  E D Oña,et al.  A Review of Robotics in Neurorehabilitation: Towards an Automated Process for Upper Limb , 2018, Journal of healthcare engineering.

[21]  N. Paik,et al.  Upper Extremity Functional Evaluation by Fugl-Meyer Assessment Scoring Using Depth-Sensing Camera in Hemiplegic Stroke Patients , 2016, PloS one.

[22]  Nicholas Stergiou,et al.  Movement Variability and the Use of Nonlinear Tools: Principles to Guide Physical Therapist Practice , 2009, Physical Therapy.

[23]  Shyamal Patel,et al.  Tracking motor recovery in stroke survivors undergoing rehabilitation using wearable technology , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[24]  Jan Kassubek,et al.  Analysis and Visualization of 3D Motion Data for UPDRS Rating of Patients with Parkinson’s Disease , 2016, Sensors.

[25]  Maja J. Mataric,et al.  Automated administration of the Wolf Motor Function Test for post-stroke assessment , 2010, 2010 4th International Conference on Pervasive Computing Technologies for Healthcare.

[26]  Alessandro Scano,et al.  Kinect V2 implementation and testing of the reaching performance scale for motor evaluation of patients with neurological impairment. , 2018, Medical engineering & physics.

[27]  Sang Hyuk Son,et al.  A Framework to Automate Assessment of Upper-Limb Motor Function Impairment: A Feasibility Study , 2015, Sensors.

[28]  A J Thompson,et al.  Measuring change in disability after inpatient rehabilitation: comparison of the responsiveness of the Barthel Index and the Functional Independence Measure , 1999, Journal of neurology, neurosurgery, and psychiatry.

[29]  Saeid Sanei,et al.  Advances on Singular Spectrum Analysis of Rehabilitative Assessment Data , 2015 .

[30]  Daniel Simonsen,et al.  Microsoft Kinect-Based System for Automatic Evaluation of the Modified Jebsen Test of Hand Function , 2017 .

[31]  B. Volpe,et al.  Kinematic Robot-Based Evaluation Scales and Clinical Counterparts to Measure Upper Limb Motor Performance in Patients With Chronic Stroke , 2010, Neurorehabilitation and neural repair.

[32]  Alberto Jardón,et al.  Towards Automated Assessment of Upper Limbs Motor Function Based on Fugl-Meyer Test and Virtual Environment , 2018 .

[33]  Eric Wade,et al.  Motor function assessment using wearable inertial sensors , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[34]  Saeid Sanei,et al.  Towards rehabilitative e-Health by introducing a new automatic scoring system , 2016, 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom).

[35]  P. Chappell,et al.  A review of clinical upper limb assessments within the framework of the WHO ICF. , 2007, Musculoskeletal care.

[36]  João P. S. Cunha,et al.  Towards a movement quantification system capable of automatic evaluation of upper limb motor function after neurological injury , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[37]  Carlos Balaguer,et al.  Automatic Outcome in Manual Dexterity Assessment Using Colour Segmentation and Nearest Neighbour Classifier , 2018, Sensors.

[38]  Sungmin Cho,et al.  Upper-Limb Function Assessment Using VBBTs for Stroke Patients , 2016, IEEE Computer Graphics and Applications.

[39]  Ellen Yi-Luen Do,et al.  The Digital Box and Block Test Automating traditional post-stroke rehabilitation assessment , 2013, 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[40]  Shyamal Patel,et al.  Estimating fugl-meyer clinical scores in stroke survivors using wearable sensors , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[41]  Greg Welch,et al.  Motion Tracking: No Silver Bullet, but a Respectable Arsenal , 2002, IEEE Computer Graphics and Applications.

[42]  Jeremy C Hobart,et al.  Rating scales as outcome measures for clinical trials in neurology: problems, solutions, and recommendations , 2007, The Lancet Neurology.

[43]  M. Donaghy,et al.  Principles of neurological rehabilitation , 2003, Journal of neurology, neurosurgery, and psychiatry.

[44]  C. Lang,et al.  Assessment of upper extremity impairment, function, and activity after stroke: foundations for clinical decision making. , 2013, Journal of hand therapy : official journal of the American Society of Hand Therapists.

[45]  A. Prochazka,et al.  A Fully Automated, Quantitative Test of Upper Limb Function , 2015, Journal of motor behavior.

[46]  Daxi Xiong,et al.  A remote quantitative Fugl-Meyer assessment framework for stroke patients based on wearable sensor networks , 2016, Comput. Methods Programs Biomed..

[47]  Jonghyun Kim,et al.  Towards clinically relevant automatic assessment of upper-limb motor function impairment , 2016, 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI).

[48]  Alessandro Scano,et al.  Kinect V2 Performance Assessment in Daily-Life Gestures: Cohort Study on Healthy Subjects for a Reference Database for Automated Instrumental Evaluations on Neurological Patients , 2017, Applied bionics and biomechanics.

[49]  Olivier Lambercy,et al.  The Virtual Peg Insertion Test as an assessment of upper limb coordination in ARSACS patients: A pilot study , 2014, Journal of the Neurological Sciences.

[50]  Shyamal Patel,et al.  A Novel Approach to Monitor Rehabilitation Outcomes in Stroke Survivors Using Wearable Technology , 2010, Proceedings of the IEEE.

[51]  S. Black,et al.  The Fugl-Meyer Assessment of Motor Recovery after Stroke: A Critical Review of Its Measurement Properties , 2002, Neurorehabilitation and neural repair.

[52]  Bijan Najafi,et al.  Motor Performance Assessment in Parkinson’s Disease: Association between Objective In-Clinic, Objective In-Home, and Subjective/Semi-Objective Measures , 2015, PloS one.

[53]  R. J. van Beers,et al.  The role of execution noise in movement variability. , 2004, Journal of neurophysiology.