Abstract
The present study aimed to develop a realistic model for the generation of human activities of daily living (ADL) movements. The angular profiles of the elbow joint during functional ADL tasks such as eating and drinking were generated by a submovement-based closed-loop model. First, the ADL movements recorded from three human participants were broken down into logical phases, and each phase was decomposed into submovement components. Three separate artificial neural networks were trained to learn the submovement parameters and were then incorporated into a closed-loop model with error correction ability. The model was able to predict angular trajectories of human ADL movements with target access rate = 100%, VAF = 98.9%, and NRMSE = 4.7% relative to the actual trajectories. In addition, the model can be used to provide the desired target for practical trajectory planning in rehabilitation systems such as functional electrical stimulation, robot therapy, brain-computer interface, and prosthetic devices.
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Abbreviations
- ADL:
-
Activities of daily living
- ANN:
-
Artificial neural network
- BCI:
-
Brain–computer interface
- CC:
-
Correlation coefficient
- FES:
-
Functional electrical stimulation
- NRMSE:
-
Normalized RMSE
- RMSE:
-
Root mean square error
- SD:
-
Standard deviation
- tansig:
-
Tangent-sigmoid
- VAF:
-
Variance accounted for
References
Ackery A, Tator C, Krassioukov A (2004) A global perspective on spinal cord injury epidemiology. J Neurotrauma 21(10):1355–1370. https://doi.org/10.1089/neu.2004.21.1355
Akhtar A, Hargrove LJ, Bretl T (2012) Prediction of distal arm joint angles from EMG and shoulder orientation for prosthesis control. In: 2012 Annual international conference of the IEEE engineering in medicine and biology society. IEEE, pp 4160–4163 http://doi.org/10.1109/EMBC.2012.6346883
Akoglu H (2018) User’s guide to correlation coefficients. Turk J Emerg Med 18(3):91–93. https://doi.org/10.1016/j.tjem.2018.08.001
Ashegh Toosi M (2011) Synergy based anticipation of elbow joint angle using kinematic and EMG data of upper limb. Amirkabir University of Technology, Tehran
Chen LL, Lee D, Fukushima K, Fukushima J (2012) Submovement composition of head movement. PLoS ONE 7(11):4–7. https://doi.org/10.1371/journal.pone.0047565
Crago PE, Memberg WD, Usey MK, Keith MW, Kirsch RF, Chapman GJ et al (1998) An elbow extension neuroprosthesis for individuals with tetraplegia. IEEE Trans Rehabil Eng. https://doi.org/10.1109/86.662614
Dipietro L, Krebs HI, Fasoli SE, Volpe BT, Hogan N (2009) Submovement changes characterize generalization of motor recovery after stroke. Cortex 45(3):318–324. https://doi.org/10.1016/j.cortex.2008.02.008
Eraifej J, Clark W, France B, Desando S, Moore D (2017) Effectiveness of upper limb functional electrical stimulation after stroke for the improvement of activities of daily living and motor function: a systematic review and meta-analysis. Syst Rev 6(1):1–21. https://doi.org/10.1186/s13643-017-0435-5
Farokhzadi M, Maleki A, Fallah A, Rashidi S (2017) Online estimation of elbow joint angle using upper arm acceleration: a movement partitioning approach. J Biomed Phys Eng 7(3):305–314
Fishbach A, Roy SA, Bastianen C, Miller LE, Houk JC (2005) Kinematic properties of on-line error corrections in the monkey. Exp Brain Res 164(4):442–457. https://doi.org/10.1007/s00221-005-2264-3
Fishbach A, Roy S, Bastianen C, Miller LE, Houk JC (2006) Deciding when and how to correct a movement: discrete submovements as a decision making process. Exp Brain Res 177(1):45–63. https://doi.org/10.1007/s00221-006-0652-y
Flash T, Hogan N (1985) The coordination of arm movements: an experimentally confirmed mathematical model. J Neurosci 5(7):1688–1703. https://doi.org/10.1523/JNEUROSCI.05-07-01688.1985
Friedman J, Finkbeiner M (2010) Temporal dynamics of masked congruence priming: evidence from reaching trajectories. In: ASCS09: Proceedings of the 9th conference of the Australasian society for cognitive science. pp 98–105. http://doi.org/10.5096/ascs200916
Friedman J, Brown S, Finkbeiner M (2013) Linking cognitive and reaching trajectories via intermittent movement control. J Math Psychol 57(3–4):140–151. https://doi.org/10.1016/j.jmp.2013.06.005
Frisoli A, Loconsole C, Bartalucci R, Bergamasco M (2013) A new bounded jerk on-line trajectory planning for mimicking human movements in robot-aided neurorehabilitation. Robot Auton Syst 61(4):404–415. https://doi.org/10.1016/J.ROBOT.2012.09.003
Grafton ST, Tunik E (2011) Human basal ganglia and the dynamic control of force during on-line corrections. J Neurosci 31(5):1600–1605. https://doi.org/10.1523/JNEUROSCI.3301-10.2011
Guidali M, Büchel M, Klamroth V, Nef T, Riener R (2009) Trajectory planning in ADL tasks for an exoskeletal arm rehabilitation robot. In: European conference on technically assisted rehabilitation (TAR). Deutsche Gesellschaft für Biomedizinische Technik, Berlin, pp 20–24. https://www.research-collection.ethz.ch/handle/20.500.11850/17939
Henderson A, Korner-Bitensky N, Levin M (2007) Virtual reality in stroke rehabilitation: a systematic review of its effectiveness for upper limb motor recovery. Top Stroke Rehabil 14(2):52–61. https://doi.org/10.1310/tsr1402-52
Hogan N, Sternad D (2013) Dynamic primitives in the control of locomotion. Front Comput Neurosci. https://doi.org/10.3389/FNCOM.2013.00071
Iuppariello L (2015) Modelling and performance assessment of human reaching movements for disease classification. Università degli Studi di Napoli Federico II, Napoli
Krebs HI, Aisen ML, Volpe BT, Hogan N (1999) Quantization of continuous arm movements in humans with brain injury. Proc Natl Acad Sci USA 96(8):4645–4649. https://doi.org/10.1073/pnas.96.8.4645
Ku HH, Ryu JH, Bae HS, Jeong C, Lee SE (2019) Modeling a long-term effect of rice straw incorporation on SOC content and grain yield in rice field. Arch Agron Soil Sci 65(14):1941–1954. https://doi.org/10.1080/03650340.2019.1583330
Liao JY, Kirsch RF (2014) Characterizing and predicting submovements during human three-dimensional arm reaches. PLoS ONE. https://doi.org/10.1371/journal.pone.0103387
Liu MM, Herzog W, Savelberg HHCM (1999) Dynamic muscle force predictions from EMG: an artificial neural network approach. J Electromyogr Kinesiol 9(6):391–400. https://doi.org/10.1016/S1050-6411(99)00014-0
Marchal-Crespo L, Reinkensmeyer DJ (2009) Review of control strategies for robotic movement training after neurologic injury. J Neuroeng Rehabil 6:20. https://doi.org/10.1186/1743-0003-6-20
Meng Q, Shao H, Wang L, Yu H (2019) Task-based trajectory planning for an exoskeleton upper limb rehabilitation robot. In: International conference on man-machine-environment system engineering. Springer, Singapore, pp. 141–149. http://doi.org/10.1007/978-981-13-2481-9_18
Murphy MA, Sunnerhagen KS, Johnels B, Willén C (2006) Three-dimensional kinematic motion analysis of a daily activity drinking from a glass: a pilot study. J Neuroeng Rehabil 3(1):18. https://doi.org/10.1186/1743-0003-3-18
Naghibi SS, Maleki A, Fallah A, Ghassemi F (2017) A modified method of submovement decomposition based on velocity profile and endpoint position. In: 24th Iranian conference of biomedical engineering (ICBME). IEEE, Tehran, pp 1–4. https://doi.org/10.1109/ICBME.2017.8430253
Plamondon R, Alimi AM, Yergeau P, Leclerc F (1993) Modelling velocity profiles of rapid movements: a comparative study. Biol Cyber 69(2):119–128. https://doi.org/10.1007/BF00226195
Raghavan VS, Albrecht J, Irwin D (2011) Finding a “Kneedle” in a haystack: detecting knee points in system behavior. In: 31st International conference on distributed computing systems workshops. pp 166–171. https://doi.org/10.1109/ICDCSW.2011.20
Ramírez-García A, Leija L, Muñoz R (2010) Active upper limb prosthesis based on natural movement trajectories. Prosthet Orthot Int 34(1):58–72. https://doi.org/10.3109/03093640903463792
Rohrer BR (2002) Evolution of movement smoothness and submovement patterns in persons with stroke. Massachusetts Institute of Technology, Cambridge
Rohrer B, Hogan N (2006) Avoiding spurious submovement decompositions II: a scattershot algorithm. Biol Cybern 94(5):409–414. https://doi.org/10.1007/s00422-006-0055-y
Ruparel R, Johnson MJ (2008) A task-oriented, trajectory planner: could it train stroke survivors to move normally on ADLs? In: 2008 2nd IEEE RAS & EMBS International conference on biomedical robotics and biomechatronics. IEEE, pp 813–818. http://doi.org/10.1109/BIOROB.2008.4762930
Sinclair J, John Taylor P, Jane Hobbs S (2013) Digital filtering of three-dimensional lower extremity kinematics: an assessment. J Hum Kinet 39(1):25–36. https://doi.org/10.2478/hukin-2013-0065
Triwiyanto T, Wahyunggoro O, Nugroho HA, Herianto H (2018) Estimation of elbow joint angle based on electromyography using the sign-slope change feature and Kalman filtering. IOP Conf Ser Mater Sci Eng 384(1):012014. https://doi.org/10.1088/1757-899X/384/1/012014
WHO (2018) Stroke, cerebrovascular accident. Retrieved May 19, 2019 from http://www.who.int/topics/cerebrovascular_accident/en/
Wisneski KJ, Johnson MJ (2007) Trajectory planning for functional wrist movements in an ADL-oriented, robot-assisted therapy environment. In: Proceedings 2007 IEEE international conference on robotics and automation. IEEE, pp 3365–3370. http://doi.org/10.1109/ROBOT.2007.363992
Woodworth RS (1899) The accuracy of voluntary movements. Psychol Rev 3:1–114
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Naghibi, S.S., Fallah, A., Maleki, A. et al. Elbow angle generation during activities of daily living using a submovement prediction model. Biol Cybern 114, 389–402 (2020). https://doi.org/10.1007/s00422-020-00834-w
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DOI: https://doi.org/10.1007/s00422-020-00834-w