Abstract
For several stroke cases, rehabilitation focuses on the pincer movements and grasps with the index and thumb fingers. The improvements in the coordination between these fingers guides the recovery of the subject. Obtaining a good measurement of these opening and closing movements is still unsolved, with robotic based high cost solutions. This research includes a preliminary study that analyses the use of tri-axial accelerometers to measure these movements and to evaluate the performance of the subjects. Under certain constraints, the solution has been found valid to detect the finger opening-closing pincer movements.
This research has been funded by the Spanish Ministry of Science and Innovation under project MINECO-TIN2017-84804-R and by the Grant FCGRUPIN-IDI/2018/000226 project from the Asturias Regional Government.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Miura, S., et al.: Quality management program of stroke rehabilitation using adherence to guidelines: a nationwide initiative in japan. J. Stroke Cerebrovasc. Dis. 28(9), 2434–2441 (2009)
Kim, H., Lee, S.H., Cho, N.B., You, H., Choi, T., Kim, J.: User-dependent usability and feasibility of a swallowing training mhealth app for older adults: mixed methods pilot study. JMIR mHealth and uHealth 8(7), e19585 (2020)
Chi, N.F., Huang, Y.C., Chiu, H.Y., Chang, H.J., Huang, H.C.: Systematic review and meta-analysis of home-based rehabilitation on improving physical function among home-dwelling patients with a stroke. Arch. Phys. Med. Rehab. 101(2), 359–373 (2020)
Veisi-Pirkoohi, S., Hassani-Abharian, P., Kazemi, R., Vaseghi, S., Zarrindast, M.R., Nasehi, M.: Efficacy of RehaCom cognitive rehabilitation software in activities of daily living, attention and response control in chronic stroke patients. J. Clinical Neurosci. 71, 101–107 (2019)
Wolf, S.L., et al.: The HAAPI (Home Arm Assistance Progression Initiative) trial: A novel robotics delivery approach in stroke rehabilitation. Neurorehabil. Neural Repair 29(10), 958–968 (2015). PMID: 25782693
Zhang, H., Austin, H., Buchanan, S., Herman, R., Koeneman, J., He, J.: Feasibility studies of robot-assisted stroke rehabilitation at clinic and home settings using rupert. In: Proceedings of the 2011 IEEE International Conference on Rehabilitation Robotics, IEEE press (2011)
Bartnicka, J., et al.: The role of virtual reality and biomechanical technologies in stroke rehabilitation. In: Nazir, Salman, Teperi, Anna-Maria, Polak-Sopińska, Aleksandra (eds.) AHFE 2018. AISC, vol. 785, pp. 351–361. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-93882-0_34
Huang, X., Naghdy, F., Naghdy, G., Du, H., Todd, C.: The combined effects of adaptive control and virtual reality on robot-assisted fine hand motion rehabilitation in chronic stroke patients: a case study. J. Stroke Cerebrovasc. Dis. 27(1), 221–228 (2018)
Chen, M.-H., Huang, L.-L.: Design suggestions of the clinical upper extremity rehabilitation equipment for stroke patients. In: Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T., Fujita, Y. (eds.) IEA 2018. AISC, vol. 824, pp. 682–687. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-96071-5_72
McPherson, L.M., Dewald, J.P.: Differences between flexion and extension synergy-driven coupling at the elbow, wrist, and fingers of individuals with chronic hemiparetic stroke. Clinical Neurophysiol. 130(4), 454–468 (2019)
Wolbrecht, E.T., Rowe, J.B., Chan, V., Ingemanson, M.L., Cramer, S.C., Reinkensmeyer, D.J.: Finger strength, individuation, and their interaction: Relationship to hand function and corticospinal tract injury after stroke. Clinical Neurophysiol. 129(4), 797–808 (2018)
Kwon, D.Y., Kwon, Y., Kim, J.W.: Quantitative analysis of finger and forearm movements in patients with off state early stage Parkinson’s disease and scans without evidence of dopaminergic deficit (SWEDD). Parkinsonism Relat. Disord. 57, 33–38 (2018)
Stegemöller, E., Zaman, A., MacKinnon, C.D., Tillman, M.D., Hass, C.J., Okun, M.S.: Laterality of repetitive finger movement performance and clinical features of Parkinson’s disease. Hum. Movement Sci. 49, 116–123 (2016)
Patar, M.N.A.A., Komeda, T., Low, C.Y., Mahmud, J.: System integration and control of finger orthosis for post stroke rehabilitation. Procedia Technol. 15, 755–764 (2014)
Oliver-Salazar, M., Szwedowicz-Wasik, D., Blanco-Ortega, A., Aguilar-Acevedo, F., Ruiz-González, R.: Characterization of pneumatic muscles and their use for the position control of a mechatronic finger. Mechatronics 42, 25–40 (2017)
Bataller, A., Cabrera, J., Clavijo, M., Castillo, J.: Evolutionary synthesis of mechanisms applied to the design of an exoskeleton for finger rehabilitation. Mech. Mach. Theory 105, 31–43 (2016)
Lu, S., Chen, D., Liu, C., Jiang, Y., Wang, M.: A 3-D finger motion measurement system via soft strain sensors for hand rehabilitation. Sens. Actuator A Phys. 285, 700–711 (2019)
Murphy, M.A., Andersson, S., Danielsson, A., Wipenmyr, J., Ohlsson, F.: Comparison of accelerometer-based arm, leg and trunk activity at weekdays and weekends during subacute inpatient rehabilitation after stroke. J. Rehab. Med. 18, 426–433 (2019)
Carús, J.L., Peláez, V., López, G., Lobato, V.: Jim: a novel and efficient accelerometric magnitude to measure physical activity. Stud. Health Technol. Inform. 177, 283–288 (2012)
Lee, J.Y., Kwon, S., Kim, W.S., Hahn, S.J., Park, J., Paik, N.J.: Feasibility, reliability, and validity of using accelerometers to measure physical activities of patients with stroke during inpatient rehabilitation. PLoS ONE 13(12), e0209607 (2018)
Villar, J.R., González, S., Sedano, J., Chira, C., Trejo-Gabriel-Galan, J.M.: Improving human activity recognition and its application in early stroke diagnosis. Int. J. Neural Syst. 25(04), 1450036 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Aranda-Orna, D., Villar, J.R., Sedano, J. (2020). Stroke Rehabilitation: Detection of Finger Movements. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_61
Download citation
DOI: https://doi.org/10.1007/978-3-030-61705-9_61
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61704-2
Online ISBN: 978-3-030-61705-9
eBook Packages: Computer ScienceComputer Science (R0)