The increasing number of patients with acquired brain injury and the current subjectivity of the conventional upper extremity (UE) assessment tests require new objective assessment techniques.
OBJECTIVE:
This research proposes a novel objective motor assessment (OMA) methodology based on the Fugl-Meyer assessment (FMA). The goals are to automatically calculate the objective scores (OSs) of FMA-UE movements (as well as a global OS) and to interpret the estimated OSs.
METHODS:
Fifteen patients participated in the study. The OMA algorithm was designed to detect small-scale variations in UE movements. The OSs for 14 FMA-UE movements and the global OSs were automatically calculated using the algorithm and evaluated by 2 therapists. The interpretation of the global OSs was performed at 3 levels: by item, movement and globally.
RESULTS:
The global OSs calculated by our algorithm had a significant correlation with the therapists’ scores (0.783 and 0.938, p < 0.01). All OSs for each movement were correlated with the scores given by the therapists. The correlation coefficient can reach values as high as 0.981 (p < 0.01).
CONCLUSIONS:
We provide a new objective assessment tool for therapists to help them improve the diagnostic accuracy and to achieve a more personalized and potentially effective physical rehabilitation of brain injury patients.
BartfaiA., MarkovicG., LandahlK. S., & SchultM. L. (2014). The protocol and design of a randomised controlled study on training of attention within the first year after acquired brain injury. BMC Neurology, 14(1), 102.
de los Reyes-GuzmánA., Dimbwadyo-TerrerI., Trincado-AlonsoF., Monasterio-HuelinF., TorricelliD., & Gil-AgudoA. (2014). Quantitative assessment based on kinematic measures of functional impairments during upper extremity movements: A review. Clinical Biomechanics, 29(7), 719–727.
4.
de los Reyes-GuzmánA., Dimbwadyo-TerrerI., Pérez-NombelaS., Monasterio-HuelinF., TorricelliD., PonsJ. L., & Gil-AgudoA. (2016). Novel kinematic indices for quantifying movement agility and smoothness after cervical Spinal Cord Injury. NeuroRehabilitation, 38(2), 199–209.
5.
Fugl-MeyerA. R., JääsköL., LeymanI., OlssonS., & SteglindS. (1975). The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance. Scandinavian Journal of Rehabilitation Medicine, 7(1), 13–31.
6.
GiustiniA., PistariniC., & PisoniC. (2012). Handbook of Clinical Neurology. InBarnes MichaelG. D. (Ed.) (110th ed., pp. 401–409). Elsevier.
7.
HouW. H., ShihC. L., ChouY. T., SheuC. F., LinJ. H., WuH. C., & HsiehC. L. (2012). Development of a computerized adaptive testing system of the Fugl-Meyer motor scale in stroke patients. Archives of Physical Medicine and Rehabilitation, 93(6), 1014–1020.
8.
HsuehI. P., HsuM. J., SheuC. F., LeeS., HsiehC. L., & LinJ. H. (2008). Psychometric comparisons of 2 versions of the Fugl-Meyer Motor Scale and 2 versions of the Stroke Rehabilitation Assessment of Movement. Neurorehabilitation and Neural Repair, 22(6), 737–744.
9.
JohnstonS. C., MendisS., & MathersC. D. (2009). Global variation in stroke burden and mortality: Estimates from monitoring, surveillance, and modelling. The Lancet Neurology, 8(4), 345–354.
LevinM. F., KleimJ. A., & WolfS. L. (2009). What do motor “recovery” and “compensation” mean in patients following stroke?Neurorehabilitation and Neural Repair, 23(4), 313–319.
12.
MiddletonA., FritzS. L., LiuzzoD. M., Newman-NorlundR., & HerterT. M. (2014). Using clinical and robotic assessment tools to examine the feasibility of pairing tDCS with upper extremity physical therapy in patients with stroke and TBI: A consideration-of-concept pilot study. NeuroRehabilitation, 35(4), 741–754.
13.
MurphyM. A., ResteghiniC., FeysP., & LamersI. (2015). An overview of systematic reviews on upper extremity outcome measures after stroke. BMC Neurology, 15(1), 29.
14.
OleshE. V., YakovenkoS., & GritsenkoV. (2014). Automated assessment of upper extremity movement impairment due to stroke. PLoS One, 9(8), e104487.
15.
OttenP., KimJ., & SonS. H. (2015). A framework to automate assessment of upper-limb motor function impairment: A feasibility study. Sensors, 15(8), 20097–20114.
16.
Pérez-RodríguezR., RodríguezC., CostaU., CáceresC., TormosJ. M., MedinaJ., & GómezE. J. (2014). Anticipatory assistance-as-needed control algorithm for a multijoint upper limb robotic orthosis in physical neurorehabilitation. Expert Systems with Applications, 41(8), 3922–3934.
17.
ProchazkaA., & KowalczewskiJ. (2015). A fully automated, quantitative test of upper limb function. Journal of Motor Behavior, 47(1), 19–28.
18.
RabG., PetuskeyK., & BagleyA. (2002). A method for determination of upper extremity kinematics. Gait & Posture, 15(2), 113–119.
19.
SommerfeldD. K., EekE. U. B., SvenssonA. K., HolmqvistL. W., & Von ArbinM. H. (2004). Spasticity after stroke. Stroke, 35(1), 134–139.
20.
SusserM., & SusserE. (1996). Choosing a future for epidemiology: I. Eras and paradigms. American Journal of Public Health, 86(5), 668–673.
21.
TagliaferriF., CompagnoneC., KorsicM., ServadeiF., & KrausJ. (2006). A systematic review of brain injury epidemiology in Europe. Acta Neurochirurgica, 148(3), 255–268.
VelozoC. A., & WoodburyM. L. (2011). Translating measurement findings into rehabilitation practice: An example using Fugl-Meyer Assessment-Upper Extremity with patients following stroke. J Rehabil Res Dev, 48(10), 1211–1222.
24.
Villán-VillánM. A., Pérez-RodríguezR., GómezC., OpissoE., TormosJ. M., MedinaJ., & GómezE. J. (2014). Dysfunctional profile for patients in physical Neurorehabilitation of Upper Limb. In XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013 (pp. 1775–1778). Springer, Cham.
25.
Villán-VillánM. A., Pérez-RodríguezR., GómezC., OpissoE., TormosJ., MedinaJ., & GómezE. J. (2015). A first step for the automation of fugl-meyer assessment scale for stroke subjects in upper limb physical neurorehabilitation. Studies in Health Technology and Informatics, 213, 45–48.
26.
Villán-VillánM. A., Pérez-RodríguezR., GómezC., OpissoE., TormosJ. M., MedinaJ., & GómezE. J. (2015). Automated Fugl-Meyer assessment for ABI subjects in upper limb physical neurorehabilitation. In International conference on recent advances in neurorehabilitation (ICRAN) (pp. 26–28).
27.
WangJ., YuL., WangJ., GuoL., GuX., & FangQ. (2014). Automated Fugl-Meyer assessment using SVR model. In 2014 IEEE International Symposium on Bioelectronics and Bioinformatics (ISBB) pp. 1–4.
28.
World Health Organization. (2006). Neurological disorders: Public health challenges. World Health Organization.
29.
WuG., Van der HelmF. C., VeegerH. D., MakhsousM., Van RoyP., AnglinC., & WernerF. W. (2015). ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion—Part II: Shoulder, elbow, wrist and hand. Journal of Biomechanics, 38(5), 981–992.
30.
YuL., XiongD., GuoL., & WangJ. (2016). A remote quantitative Fugl-Meyer assessment framework for stroke patients based on wearable sensor networks. Computer Methods and Programs in Biomedicine, 128, 100–110.
