Whole body awareness for controlling a robotic transfemoral prosthesis
Open accessAbstract
Restoring locomotion functionality of transfemoral amputees is essential for early rehabilitation treatment and for preserving mobility and independence in daily life. Research in wearable robotics fostered the development of innovative active mechatronic lower-limb prostheses designed with the goal to reduce the cognitive and physical effort of lower-limb amputees in rehabilitation and daily life activities. To ensure benefits to the users, active mechatronic prostheses are expected to be aware of the user intention and properly interact in a closed human-in-the-loop paradigm. In the state of the art various cognitive interfaces have been proposed to online decode the user's intention. Electromyography in combination with mechanical sensing such as inertial or pressure sensors is a widely adopted solution for driving active mechatronic prostheses. In this framework, researchers also explored targeted muscles re-innervation for an objective-oriented surgical amputation promoting wider usability of active prostheses. However, information kept by the neural component of the cognitive interface deteriorates in a prolonged use scenario due to electrodes-related issues, thereby undermining the correct functionality of the active prosthesis. The objective of this work is to present a novel controller for an active transfemoral prosthesis based on whole body awareness relying on a wireless distributed non-invasive sensory apparatus acting as cognitive interface. A finite-state machine controller based on signals monitored from the wearable interface performs subject-independent intention detection of functional tasks such as ground level walking, stair ascent, and sit-to-stand maneuvres and their main sub-phases. Experimental activities carried out with four transfemoral amputees (among them one dysvascular) demonstrated high reliability of the controller capable of providing 100% accuracy rate in treadmill walking even for weak subjects and low walking speeds. The minimum success rate was of 94.8% in performing sit-to-stand tasks. All the participants showed high confidence in using the transfemoral active prosthesis even without training period thanks to intuitiveness of the whole body awareness controller.
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Developments in volitional motor control have outpaced advancements in sensory feedback for better control of prosthetic arms.
“The developments in volitional motor control have outpaced the advancements in sensory feedback for better control of prosthetic arms (Parri et al., 2017).”
From Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review. (2017) · cites this paper as Parri et al., 2017
The Introduction discusses intention detection and sensor-based control for lower-limb prostheses, while the Discussions and conclusions address the performance of a transfemoral prosthesis controller. A search for sensory feedback, prosthetic arms, and a comparison of their progress with volitional motor control found no passage addressing the cited claim. No page numbers are available in our copy. (LLM, conf=0.76)
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Cites
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 · see citing works
Energy cost of walking of amputees: the influence of level of amputation.
Waters, R. L., Perry, J., Antonelli, D. J., & Hislop, H. J. (1976) · PubMed · Cited by 873 · see citing works
Energy cost of walking of amputees
Waters, R. L., Perry, J., Antonelli, D. J., & Hislop, H. J. (1976) · Journal of Bone and Joint Surgery · doi:10.2106/00004623-197658010-00007 · Cited by 840 · see citing works
Huang, H., Zhang, F., Hargrove, L. J., Dou, Z., Rogers, D. R., & Englehart, K. B. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2161671 · Cited by 542 · see citing works
A Strategy for Identifying Locomotion Modes Using Surface Electromyography
Huang, H., Kuiken, T., & Lipschutz, R. D. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.2003293 · Cited by 517 · see citing works
GAILEY, R. S., Roach, K. E., Applegate, B. E., et al. (2002) · Archives of Physical Medicine and Rehabilitation · doi:10.1053/apmr.2002.32309 · Cited by 451 · see citing works
GAILEY, R. S., Roach, K. E., Applegate, B. E., et al. (2002) · Archives of Physical Medicine and Rehabilitation · doi:10.1053/ampr.2002.32309 · Cited by 436 · see citing works
Jiménez-Fabián, R., & Verlinden, O. (2011) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2011.11.018 · Cited by 315 · see citing works
Active lower limb prosthetics: a systematic review of design issues and solutions
Windrich, M., Grimmer, M., Christ, O., Rinderknecht, S., & Beckerle, P. (2016) · BioMedical Engineering OnLine · doi:10.1186/s12938-016-0284-9 · Cited by 275 · see citing works
2011 IEEE International Conference on Rehabilitation Robotics
(2011) · doi:10.1109/icorr16527.2011 · Cited by 260 · see citing works
Robotic Leg Control with EMG Decoding in an Amputee with Nerve Transfers
Hargrove, L. J., Simon, A. M., Young, A. J., Lipschutz, R. D., Finucane, S. B., Smith, D. G., & Kuiken, T. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1300126 · Cited by 253 · see citing works
Epidemiology of limb loss and congenital limb deficiency: A review of the literature
Ephraim, P. L., Dillingham, T. R., Sengoelge, M., Pezzin, L. E., & MacKenzie, E. J. (2003) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/s0003-9993(02)04932-8 · Cited by 219 · see citing works
Nordander, C., Willner, J., Hansson, G.-Å., Larsson, B. K., Unge, J., Granquist, L., & Skerfving, S. (2003) · European Journal of Applied Physiology · doi:10.1007/s00421-003-0819-1 · Cited by 216 · see citing works
A Wireless Flexible Sensorized Insole for Gait Analysis
Crea, S., Donati, M., Rossi, S., et al. (2014) · Sensors · doi:10.3390/s140101073 · Cited by 216 · see citing works
Self-citationYoung, A. J., Kuiken, T., & Hargrove, L. J. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/5/056021 · Cited by 203 · see citing works
Prosthetic use and functional and social outcome following major lower limb amputation
Pohjolainen, T., Alaranta, H., & Kärkäinen, M. (1990) · Prosthetics and Orthotics International · doi:10.3109/03093649009080326 · Cited by 164 · see citing works
Liu, M., Wang, D., & Huang, H. (2015) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2420539 · Cited by 145 · see citing works
A Real-Time Gait Event Detection for Lower Limb Prosthesis Control and Evaluation
Maqbool, H. F., Husman, M. A., Awad, M. I., Abouhossein, A., Iqbal, N., & Dehghani-Sanij, A. A. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2636367 · Cited by 141 · see citing works
Hafner, B. J., Sanders, J. E., Czerniecki, J. M., et al. (2002) · PubMed · Cited by 134 · see citing works
Online Phase Detection Using Wearable Sensors for Walking with a Robotic Prosthesis
Goršič, M., Kamnik, R., Ambrožič, L., Vitiello, N., Lefeber, D., Pasquini, G., & Munih, M. (2014) · Sensors · doi:10.3390/s140202776 · Cited by 121 · see citing works
Self-citationDepth Sensing for Improved Control of Lower Limb Prostheses
Krausz, N. E., Lenzi, T., & Hargrove, L. J. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2448457 · Cited by 115 · see citing works
Significance of skin temperature changes in surface electromyography
Winkel, J., & J�rgensen, K. (1991) · European Journal of Applied Physiology · doi:10.1007/bf00364460 · Cited by 112 · see citing works
Fuzzy-Logic-Based Terrain Identification with Multisensor Fusion for Transtibial Amputees
Yuan, K., Wang, Q., & Wang, L. (2014) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2014.2309708 · Cited by 94 · see citing works
Detection of and Compensation for EMG Disturbances for Powered Lower Limb Prosthesis Control
Spanias, J. A., Perreault, E. J., & Hargrove, L. J. (2015) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2413393 · Cited by 93 · see citing works
A Noncontact Capacitive Sensing System for Recognizing Locomotion Modes of Transtibial Amputees
Zheng, E., Wang, L., Wei, K., & Wang, Q. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2334316 · Cited by 87 · see citing works
The effect of perspiration on the sEMG amplitude and power spectrum
Abdoli-Eramaki, M., Damecour, C., Christenson, J., & Stevenson, J. M. (2012) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2012.04.009 · Cited by 87 · see citing works
Flynn, L., Geeroms, J., Jiménez-Fabián, R., et al. (2014) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2014.12.013 · Cited by 71 · see citing works
Self-citationChen, B., Wang, X., Huang, Y. Q., Wei, K., & Wang, Q. (2015) · Mechatronics · doi:10.1016/j.mechatronics.2015.09.002 · Cited by 69 · see citing works
CYBERLEGs: A User-Oriented Robotic Transfemoral Prosthesis with Whole-Body Awareness Control
Ambrožič, L., Goršič, M., Geeroms, J., et al. (2014) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2014.2360278 · Cited by 65 · see citing works
Self-citationPreliminary design of a terrain recognition system
Zhang, F., Fang, Z., Liu, M., & Huang, H. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6091391 · Cited by 58 · see citing works
Ankle-Knee prosthesis with powered ankle and energy transfer for CYBERLEGs α-prototype
Geeroms, J., Flynn, L., Jiménez-Fabián, R., Vanderborght, B., & Lefeber, D. (2013) · International Conference on Rehabilitation Robotics · doi:10.1109/icorr.2013.6650352 · Cited by 53 · see citing works
Self-citationDesign and energetic evaluation of a prosthetic knee joint actuator with a lockable parallel spring
Geeroms, J., Flynn, L., Jiménez-Fabián, R., et al. (2017) · Bioinspiration & Biomimetics · doi:10.1088/1748-3190/aa575c · Cited by 47 · see citing works
Self-citationTerrain identification for prosthetic knees based on electromyographic signal features
Jin, D., Yang, J., Zhang, R., Wang, R., & Zhang, J. (2006) · Tsinghua Science & Technology · doi:10.1016/s1007-0214(06)70157-2 · Cited by 46 · see citing works
Tkach, D. C., & Hargrove, L. J. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6610190 · Cited by 46 · see citing works
Xuegang, W., Wang, Q., Zheng, E., Wei, K., & Wang, L. (2012) · Advances in intelligent systems and computing · doi:10.1007/978-3-642-33932-5_26 · Cited by 39 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- Beravs, 2011, Development and validation of a wearable inertial measurement system for use with lower limb exoskeletons, 212
- Ceseracciu, 2010, SVM classification of locomotion modes using surface electromyography for applications in rehabilitation robotics, 165
- Flynn, CYBERLEGs Beta-Prosthesis active knee system, 410
- Varol, 2007, Real-time intent recognition for a powered knee and ankle transfemoral prosthesis, 16
Cited by
Sorted by citations
Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review.
Ghafoor, U., Kim, S., & Hong, K.-S. (2017) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2017.00059 · Cited by 88 · see citing works
“The developments in volitional motor control have outpaced the advancements in sensory feedback for better control of prosthetic arms (Parri et al., 2017).”
- Authors
- Parri, A., Martini, E., Geeroms, J., Flynn, L., Pasquini, G., Crea, S., Molino Lova, R., Lefeber, D., Kamnik, R., Munih, M., & Vitiello, N.
- Affiliations
- Scuola Superiore Sant'Anna (IT)
- Vrije Universiteit Brussel (BE)
- Don Carlo Gnocchi Foundation (IT)
- University of Ljubljana (SI)
- Corresponding author
- Parri, Andrea
- ORCID
- Venue
- Frontiers in Neurorobotics
- Publisher
- Frontiers Media SA
- Published
- 2017-05-30
- Volume
- 11
- Pages
- 25-25
- ISSN
- 1662-5218 (ISSN-L)
- PMC
- PMC5448151
- OpenAlex
- W2618961157
- Cited by
- 29
- References
- 35
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 3 · 2022: 3 · 2021: 4 · 2020: 8 · 2019: 2 · 2018: 5 · 2017: 1
- FWCI
- 1.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Neurorobotics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- VUBIR (Vrije Universiteit Brussel) (submittedVersion)
- CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies) (submittedVersion)
- Funders
- Seventh Framework Programme (FP7-ICT-2011-2.1 CYBERLEGs - Grant Agreement #287894)
- Horizon 2020 (H2020-ICT-2016-1 CYBERLEGs PlusPlus - Grant Agreement #731931)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- transfemoral prosthesis, cognitive interface, wearable sensors, finite state machine controller, transfemoral amputees, sit-to-stand, stair climbing, treadmill walking
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