Lower Limb Exoskeleton During Gait and Posture: Objective and Subjective Assessment Procedures With Minimal Instrumentation

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Tipo de produção
article
Data de publicação
2023
Título da Revista
ISSN da Revista
Título do Volume
Editora
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Autores
PARIK-AMERICANO, Pedro
PINHO, Joao Pedro
SANTOS, Fabia Camile dos
UMEMURA, Guilherme Silva
FORNER-CORDERO, Arturo
Citação
IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS, v.5, n.4, p.1025-1036, 2023
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
In this work, we propose and implement an enhanced methodology for assessing the impact of wearing a lower limb passive exoskeleton on the gait and posture of the wearer, integrating both objective and subjective assessment techniques. Using only an inertial measurement unit (IMU) and a heart rate monitor, we examined the gait of eighteen healthy volunteers during treadmill and overground walking, both with and without the exoskeleton. Postural control tests were conducted on a force platform and with an IMU during standing and crouching. Our findings indicate that exoskeleton use resulted in higher physical effort, perceived effort, and user frustration. Moreover, wearing the exoskeleton showed an augmented periodic stability, which is related to more constant strides, gait movements showed reduced smoothness, which is related to difficulties to adapt the strides in response to gait perturbations. Nevertheless, the exoskeleton exhibited better stability during stance, notably reducing medio-lateral displacement by over 30%. Finally, we found an increase in cognitive effort linked to exoskeleton use. These outcomes offer valuable insights into the comprehensive influence of exoskeletons on gait performance and user experience.
Palavras-chave
Exoskeletons, Legged locomotion, Task analysis, Heart rate, Force, Biomimetics, Stability analysis, Biomechanics, User experience, Statistical analysis, Exoskeleton, biomechanics, user experience, inertial sensor, statistical analysis
Referências
  1. Babanov N. D., 2021, Human Physiology, V47, P410, DOI [10.31857/S0131164621030024, 10.1134/S0362119721030026]
  2. BEQUETTE B., 2018, P HUM FACT ERG SOC A
  3. Bequette B, 2020, HUM FACTORS, V62, P411, DOI 10.1177/0018720820907450
  4. Bruijn SM, 2013, J R SOC INTERFACE, V10, DOI 10.1098/rsif.2012.0999
  5. Cordero AF, 2005, GAIT POSTURE, V21, P243, DOI 10.1016/j.gaitpost.2004.01.012
  6. Cordero AF, 2004, BIOL CYBERN, V91, P212, DOI 10.1007/s00422-004-0508-0
  7. Dal U, 2010, GAIT POSTURE, V31, P366, DOI 10.1016/j.gaitpost.2010.01.006
  8. De Witt JK, 2008, J EXP BIOL, V211, P1087, DOI 10.1242/jeb.012443
  9. dos Anjos FV, 2022, J BIOMECH, V130, DOI 10.1016/j.jbiomech.2021.110846
  10. Duddy D, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21093207
  11. Fasola J, 2019, INT C REHAB ROBOT, P593, DOI [10.1109/icorr.2019.8779500, 10.1109/ICORR.2019.8779500]
  12. Font-Llagunes JM, 2009, MECH BASED DES STRUC, V37, P259, DOI 10.1080/15397730902810234
  13. Forner-Cordero A, 2007, J MOTOR BEHAV, V39, P215, DOI 10.3200/JMBR.39.3.215-226
  14. Hansson EE, 2019, BMC RES NOTES, V12, DOI 10.1186/s13104-019-4238-8
  15. HART S G, 1988, P139
  16. Haufe FL, 2021, J NEUROENG REHABIL, V18, DOI 10.1186/s12984-021-00946-9
  17. HORAK FB, 1986, J NEUROPHYSIOL, V55, P1369, DOI 10.1152/jn.1986.55.6.1369
  18. Kamen G, 1998, GERONTOLOGY, V44, P40, DOI 10.1159/000021981
  19. Kuo AD, 2010, PHYS THER, V90, P157, DOI 10.2522/ptj.20090125
  20. Lee CH, 2018, J PHYSIOL ANTHROPOL, V37, DOI 10.1186/s40101-018-0187-5
  21. Luger T, 2019, APPL ERGON, V80, P152, DOI 10.1016/j.apergo.2019.05.018
  22. Melendez-Calderon A, 2021, FRONT BIOENG BIOTECH, V8, DOI 10.3389/fbioe.2020.558771
  23. Moraes R, 2018, FRONT NEUROSCI-SWITZ, V12, DOI 10.3389/fnins.2018.00346
  24. Moreno Juan C., 2009, Applied Bionics and Biomechanics, V6, P245, DOI 10.1080/11762320902823324
  25. Noamani A, 2020, MED ENG PHYS, V77, P53, DOI 10.1016/j.medengphy.2019.10.018
  26. Olivier J, 2015, INT C REHAB ROBOT, P618, DOI 10.1109/ICORR.2015.7281269
  27. Parik-Americano P, 2022, P IEEE RAS-EMBS INT, DOI [10.1109/BIOROB52689.2022.9925521, 10.1109/BioRob52689.2022.9925521]
  28. Park JH, 2022, GAIT POSTURE, V92, P181, DOI 10.1016/j.gaitpost.2021.11.028
  29. Pillai MV, 2020, HUM FACTORS, V62, P489, DOI 10.1177/0018720820907752
  30. Pinho JP, 2020, IEEE ENG MED BIO, P4917, DOI 10.1109/EMBC44109.2020.9175895
  31. Pinto-Fernandez D, 2020, IEEE T NEUR SYS REH, V28, P1573, DOI 10.1109/TNSRE.2020.2989481
  32. Poggensee KL, 2021, SCI ROBOT, V6, DOI 10.1126/scirobotics.abf1078
  33. RALSTON H J, 1958, Int Z Angew Physiol, V17, P277
  34. Riva F, 2013, GAIT POSTURE, V38, P170, DOI 10.1016/j.gaitpost.2013.05.002
  35. Rojek A, 2020, FRONT NEUROL, V10, DOI 10.3389/fneur.2019.01344
  36. Ros J, 2015, MULTIBODY SYST DYN, V35, P215, DOI 10.1007/s11044-015-9460-0
  37. Tetteh E, 2022, APPL ERGON, V100, DOI 10.1016/j.apergo.2021.103646
  38. Theurel J, 2018, APPL ERGON, V67, P211, DOI 10.1016/j.apergo.2017.10.008
  39. van Schooten KS, 2013, J BIOMECH, V46, P137, DOI 10.1016/j.jbiomech.2012.10.032
  40. Watanabe Miyoko, 2020, J Phys Ther Sci, V32, P55, DOI 10.1589/jpts.32.55
  41. Weaver TB, 2017, J BIOMECH, V53, P90, DOI 10.1016/j.jbiomech.2017.01.003
  42. Xin Jin, 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA), P1772, DOI 10.1109/ICRA.2017.7989210
  43. Zanotto D, 2015, IEEE T ROBOT, V31, P978, DOI 10.1109/TRO.2015.2450414