The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Stronger, Smarter, Softer: Next-Generation Wearable Robots
Abstract
Exosuits show much promise as a method for augmenting the body with lightweight, portable, and compliant wearable systems. We envision that such systems can be further refined so that they can be sufficiently low profile to fit under a wearer's existing clothing. Our focus is on creating an assistive device that provides a fraction of the nominal biological torques and does not provide external load transfer. In early work, we showed that the system can substantially maintain normal biomechanics and positively affect a wearer's metabolic rate. Many basic fundamental research and development challenges remain in actuator development, textile innovation, soft sensor development, human-machine interface (control), biomechanics, and physiology, which provides fertile ground for academic research in many disciplines. While we have focused on gait assistance thus far, numerous other applications are possible, including rehabilitation, upper body support, and assistance for other motions. We look forward to a future where wearable robots provide benefits for people across many areas of our society.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Lower Extremity Exoskeletons and Active Orthoses: Challenges and State-of-the-Art
Dollar, A. M., & Herr, H. (2008) · IEEE Transactions on Robotics · doi:10.1109/tro.2008.915453 · Cited by 1,327
Design and Evaluation of the LOPES Exoskeleton Robot for Interactive Gait Rehabilitation
Veneman, J. F., Kruidhof, R., Hekman, E. E. G., Ekkelenkamp, R., Van Asseldonk, E. H. F., & van der Kooij, H. (2007) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2007.903919 · Cited by 1,280
Esquenazi, A., Talaty, M., Packel, A., & Saulino, M. (2012) · American Journal of Physical Medicine & Rehabilitation · doi:10.1097/phm.0b013e318269d9a3 · Cited by 958
Design and Fabrication of Soft Artificial Skin Using Embedded Microchannels and Liquid Conductors
Park, Y.-L., Chen, B.-R., & Wood, R. J. (2012) · IEEE Sensors Journal · doi:10.1109/jsen.2012.2200790 · Cited by 776
The Effects of Adding Mass to the Legs on the Energetics and Biomechanics of Walking
BROWNING, R. C., MODICA, J. R., KRAM, R., & GOSWAMI, A. (2007) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e31802b3562 · Cited by 603
Yun, X., & Bachmann, E. R. (2006) · IEEE Transactions on Robotics · doi:10.1109/tro.2006.886270 · Cited by 569
Hyperelastic pressure sensing with a liquid-embedded elastomer
Park, Y.-L., Majidi, C., Kramer, R., Bérard, P., & Wood, R. J. (2010) · Journal of Micromechanics and Microengineering · doi:10.1088/0960-1317/20/12/125029 · Cited by 513
Robotic Orthosis Lokomat: a rehabilitation and research tool
Jezernik, S., Colombo, G., Keller, T., Frueh, H., & Morari, M. (2003) · Neuromodulation Technology at the Neural Interface · doi:10.1046/j.1525-1403.2003.03017.x · Cited by 503 · cited 1×
“Other approaches have been to assist with load carriage by providing a parallel path to the ground, thereby offloading the wearer’s musculature [10]– [12], and some systems also provide gait rehabilitation in conjunction with a treadmill [6], [13], [14].”
BackgroundEMG frequency spectrum, muscle structure, and fatigue during dynamic contractions in man
Komi, P. V., & Tesch, P. (1979) · European Journal of Applied Physiology and Occupational Physiology · doi:10.1007/bf00421103 · Cited by 461
Autonomous exoskeleton reduces metabolic cost of human walking during load carriage
Mooney, L. M., Rouse, E. J., & Herr, H. M. (2014) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-11-80 · Cited by 461
Cited by
A soft robotic exosuit improves walking in patients after stroke
Awad, L. N., Bae, J., O’Donnell, K., De Rossi, S. M. M., Hendron, K., Sloot, L. H., Kudzia, P., Allen, S., Holt, K. G., Ellis, T. D., et al. (2017) · Science Translational Medicine · doi:10.1126/scitranslmed.aai9084 · Cited by 672
Self-citationWearable Soft Technologies for Haptic Sensing and Feedback
Yin, J., Hinchet, R., Shea, H., & Majidi, C. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202007428 · Cited by 325
A deep-learned skin sensor decoding the epicentral human motions
Kim, K. K., Ha, I., Kim, M., Choi, J., Won, P., Jo, S., & Ko, S. H. (2020) · Nature Communications · doi:10.1038/s41467-020-16040-y · Cited by 299
A biologically-inspired multi-joint soft exosuit that can reduce the energy cost of loaded walking
Panizzolo, F. A., Galiana, I., Asbeck, A. T., Siviy, C., Schmidt, K., Holt, K. G., & Walsh, C. J. (2016) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-016-0150-9 · Cited by 291
Self-citationBio-inspired upper limb soft exoskeleton to reduce stroke-induced complications
Li, N., Yang, T., Yu, P., Chang, J., Zhao, L., Zhao, X., Elhajj, I. H., Xi, N., & Liu, L. (2018) · Bioinspiration & Biomimetics · doi:10.1088/1748-3190/aad8d4 · Cited by 92
Metadata
- Authors
- Asbeck, A. T., De Rossi, S. M. M., Galiana, I., Ding, Y., & Walsh, C. J.
- Venue
- IEEE Robotics & Automation Magazine
- Published
- 2014-12
- Volume
- 21
- Issue
- 4
- Pages
- 22-33
- ISSN
- 1070-9932 (ISSN-L)
- 1558-223X
- OpenAlex
- W2002161214
- Cited by
- 368
- References
- 50
- FWCI
- 8.94
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Stronger, Smarter, Softer: Next-Generation Wearable Robots. https://app.cochat.ai/papers/10.1109/mra.2014.2360283
- The paper
- Asbeck, A. T., De Rossi, S. M. M., Galiana, I., Ding, Y., & Walsh, C. J. (2014). Stronger, Smarter, Softer: Next-Generation Wearable Robots. IEEE Robotics & Automation Magazine. https://doi.org/10.1109/mra.2014.2360283
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
