The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
State of the Art and Future Directions for Lower Limb Robotic Exoskeletons
Abstract
Research on robotic exoskeletons has rapidly expanded over the previous decade. Advances in robotic hardware and energy supplies have enabled viable prototypes for human testing. This review paper describes current lower limb robotic exoskeletons, with specific regard to common trends in the field. The preponderance of published literature lacks rigorous quantitative evaluations of exoskeleton performance, making it difficult to determine the disadvantages and drawbacks of many of the devices. We analyzed common approaches in exoskeleton design and the convergence, or lack thereof, with certain technologies. We focused on actuators, sensors, energy sources, materials, and control strategies. One of the largest hurdles to be overcome in exoskeleton research is the user interface and control. More intuitive and flexible user interfaces are needed to increase the success of robotic exoskeletons. In the last section, we discuss promising future solutions to the major hurdles in exoskeleton control. A number of emerging technologies could deliver substantial advantages to existing and future exoskeleton designs. We conclude with a listing of the advantages and disadvantages of the emerging technologies and discuss possible futures for the field.
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Cites
Kim, D.-H., Lu, N., Ma, R., Kim, Y.-S., Kim, R.-H., Wang, S., Wu, J., Won, S. M., Tao, H., Islam, A., et al. (2011) · Science · doi:10.1126/science.1206157 · Cited by 4,836
Control strategies for active lower extremity prosthetics and orthotics: a review
Tucker, M. R., Olivier, J., Pagel, A., Bleuler, H., Bouri, M., Lambercy, O., Millán, J. D. R., Riener, R., Vallery, H., & Gassert, R. (2015) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-12-1 · Cited by 1,047
Esquenazi, A., Talaty, M., Packel, A., & Saulino, M. (2012) · American Journal of Physical Medicine & Rehabilitation · doi:10.1097/phm.0b013e318269d9a3 · Cited by 958
Double nerve intraneural interface implant on a human amputee for robotic hand control
Rossini, P. M., Micera, S., Benvenuto, A., Carpaneto, J., Cavallo, G., Citi, L., Cipriani, C., Denaro, L., Denaro, V., Di Pino, G., et al. (2010) · Clinical Neurophysiology · doi:10.1016/j.clinph.2010.01.001 · Cited by 396
On the Biomimetic Design of the Berkeley Lower Extremity Exoskeleton (BLEEX)
Chu, A., Kazerooni, H., & Zoss, A. (2006) · Proceedings of the 2005 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2005.1570789 · Cited by 258 · cited 1×
“The Berkeley Lower Extremity Exoskeleton's (BLEEX) goal was to develop a portable robotic exoskeleton that allowed soldiers to carry heavy loads over long distances [7]–[10].”
BackgroundHuman–Robot Synchrony: Flexible Assistance Using Adaptive Oscillators
Ronsse, R., Vitiello, N., Lenzi, T., van den Kieboom, J., Carrozza, M. C., & Ijspeert, A. J. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2089629 · Cited by 158
A Method for the Autonomous Control of Lower Limb Exoskeletons for Persons With Paraplegia
Quintero, H. A., Farris, R. J., & Goldfarb, M. (2012) · Journal of Medical Devices · doi:10.1115/1.4007181 · Cited by 97
Cited by
Wearable health devices in health care: narrative systematic review
Lu, L., Zhang, J., Xie, Y., Gao, F., Xu, S., Wu, X., & Ye, Z. (2020) · JMIR mHealth and uHealth · doi:10.2196/18907 · Cited by 701
Reducing the metabolic rate of walking and running with a versatile, portable exosuit
Kim, J., Lee, G., Heimgartner, R., Arumukhom Revi, D., Karavas, N., Nathanson, D., Galiana, I., Eckert-Erdheim, A., Murphy, P., Perry, D., et al. (2019) · Science · doi:10.1126/science.aav7536 · Cited by 482
Task-agnostic exoskeleton control via biological joint moment estimation
Molinaro, D. D., Scherpereel, K. L., Schonhaut, E. B., Evangelopoulos, G., Shepherd, M. K., & Young, A. J. (2024) · Nature · doi:10.1038/s41586-024-08157-7 · Cited by 107
Self-citationRecognition of walking environments and gait period by surface electromyography
Kyeong, S., Shin, W., Yang, M., Heo, U., Feng, J.-R., & Kim, J. (2019) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1800601 · Cited by 68
Human Gait phases recognition based on multi-source data fusion and BILSTM attention neural network
Zhan, H., Kou, J., Cao, Y., Guo, Q., Zhang, J., & Shi, Y. (2024) · Measurement · doi:10.1016/j.measurement.2024.115396 · Cited by 28
Metadata
- Authors
- Young, A. J., & Ferris, D. P.
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2017-02
- Volume
- 25
- Issue
- 2
- Pages
- 171-182
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W2343715419
- Cited by
- 901
- References
- 7
- FWCI
- 30.09
- Type
- journal article
- Language
- en
- Open access
- closed
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