Using Wearable Sensors to Measure Motor Abilities following Stroke
Abstract
Motor abilities of stroke survivors are often severely affected. Post-stroke rehabilitation is guided by the use of clinical assessments of motor abilities. Clinical assessment scores can be predicted by models based on features extracted from the wearable sensor data. Wearable sensors would allow monitoring of subjects in the home and provide accurate assessments to guide the rehabilitation process. We propose the use of a wearable sensor system to assess the motor abilities of stroke victims. Preliminary results from twelve subjects show the ability of this system to predict clinical scores of motor abilities
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879 · see citing works
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,493 · see citing works
Agreement between physiotherapists on quality of movement rated via videotape
Pomeroy, V. M., Pramanik, A., Sykes, L., Richards, J., & Hill, E. (2003) · Clinical Rehabilitation · doi:10.1191/0269215503cr607oa · Cited by 46 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Direct measurement of human movement by accelerometry
Godfrey, A., Conway, R., Meagher, D., & ÓLaighin, G. (2008) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2008.09.005 · Cited by 591 · see citing works
Sensitivity of Smoothness Measures to Movement Duration, Amplitude, and Arrests
Hogan, N., & Sternad, D. (2009) · Journal of Motor Behavior · doi:10.3200/35-09-004-rc · Cited by 440 · see citing works
Maceira-Elvira, P., Popa, T., Schmid, A., & Hummel, F. C. (2019) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-019-0612-y · Cited by 275 · see citing works
Lorincz, K., Chen, B.-R., Challen, G. W., Chowdhury, A. R., Patel, S., Bonato, P., & Welsh, M. (2009) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/1644038.1644057 · Cited by 265 · see citing works
Self-citationWearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review
Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., & McDonald-Maier, K. D. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103197 · Cited by 180 · see citing works
“To tackle these issues, researchers have developed applications to assess rehabilitation outcomes using novel technologies namely ”wearable sensors” [28], which provide a high level of portability and low price giving researchers and therapists a plethora of possibilities and solutions [29].”
BackgroundA Novel Approach to Monitor Rehabilitation Outcomes in Stroke Survivors Using Wearable Technology
Patel, S., Hughes, R., Hester, T., Stein, J., Akay, M., Dy, J. G., & Bonato, P. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2009.2038727 · Cited by 170 · see citing works
Self-citationNoorkõiv, M., Rodgers, H., & Price, C. I. (2014) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-11-144 · Cited by 141 · see citing works
Wearable Medical Sensor-Based System Design: A Survey
Mosenia, A., Sur‐Kolay, S., Raghunathan, A., & Jha, N. K. (2017) · IEEE Transactions on Multi-Scale Computing Systems · doi:10.1109/tmscs.2017.2675888 · Cited by 143 · see citing works
Support vector regression snow-depth retrieval algorithm using passive microwave remote sensing data
Xiao, X., Zhang, T., Zhong, X., Shao, W., & Li, X. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.03.008 · Cited by 140 · see citing works
Yu, L., Xiong, D., Guo, L., & Wang, J. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.02.012 · Cited by 134 · see citing works
Wearable systems for shoulder kinematics assessment: a systematic review
Carnevale, A., Longo, U. G., Schena, E., Massaroni, C., Presti, D. L., Berton, A., Candela, V., & Denaro, V. (2019) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-019-2930-4 · Cited by 129 · see citing works
Wearable Sensor-Based Rehabilitation Exercise Assessment for Knee Osteoarthritis
Chen, K.-H., Chen, P.-C., Liu, K.-C., & Chan, C.-T. (2015) · Sensors · doi:10.3390/s150204193 · Cited by 115 · see citing works
SPINE: a domain‐specific framework for rapid prototyping of WBSN applications
Bellifemine, F. L., Fortino, G., Giannantonio, R., Gravina, R., Guerrieri, A., & Sgroi, M. (2010) · Software Practice and Experience · doi:10.1002/spe.998 · Cited by 103 · see citing works
Augmenting the Senses: A Review on Sensor-Based Learning Support
Schneider, J., Börner, D., van Rosmalen, P., & Specht, M. M. (2015) · Sensors · doi:10.3390/s150204097 · Cited by 101 · see citing works
Estimating fugl-meyer clinical scores in stroke survivors using wearable sensors
Del Din, S., Patel, S., Cobelli, C., & Bonato, P. (2011) · 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.2011.6091444 · Cited by 90 · see citing works
Self-citationTracking motor recovery in stroke survivors undergoing rehabilitation using wearable technology
Patel, S., Hughes, R., Hester, T., Stein, J. M., Akay, M., Dy, J., & Bonato, P. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5626446 · Cited by 87 · see citing works
Self-citationA method to qualitatively assess arm use in stroke survivors in the home environment
Leuenberger, K., Gonzenbach, R. R., Wachter, S., Luft, A. R., & Gassert, R. (2016) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-016-1496-7 · Cited by 86 · see citing works
Stroke Therapy through Motion-Based Games
Alankuş, G., Proffitt, R. M., Kelleher, C. L., & Engsberg, J. R. (2011) · ACM Transactions on Accessible Computing · doi:10.1145/2039339.2039342 · Cited by 82 · see citing works
Application of Wireless Sensor Networks to Healthcare Promotion
Neves, P. S., Stachyra, M., & Rodrigues, J. J. P. C. (2008) · Journal of Communications Software and Systems · doi:10.24138/jcomss.v4i3.218 · Cited by 75 · see citing works
Repnik, E., Puh, U., Goljar, N., Munih, M., & Mihelj, M. (2018) · Sensors · doi:10.3390/s18092767 · Cited by 67 · see citing works
Motor function assessment using wearable inertial sensors
Parnandi, A., Wade, E., & Matarić, M. J. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5626156 · Cited by 59 · see citing works
Assessment of movement disorders using wearable sensors during upper limb tasks: A scoping review
Vanmechelen, I., Haberfehlner, H., Vleeschhauwer, J. D., Wonterghem, E. V., Feys, H., Desloovere, K., Aerts, J., & Monbaliu, E. (2023) · Frontiers in Robotics and AI · doi:10.3389/frobt.2022.1068413 · Cited by 51 · see citing works
Leder, R. S., Azcárate, G., Savage, R., Savage, S., Sucar, L. E., Reinkensmeyer, D. J., Toxtli, C., Roth, E., & Molina, A. (2008) · International Conference on Virtual Rehabilitation · doi:10.1109/icvr.2008.4625137 · Cited by 47 · see citing works
Event-Driven Data Integration for Personal Health Monitoring
Mouttham, A., Peyton, L., Eze, B., & Saddik, A. E. (2009) · Journal of Emerging Technologies in Web Intelligence · doi:10.4304/jetwi.1.2.110-118 · Cited by 43 · see citing works
Monitoring motor capacity changes of children during rehabilitation using body-worn sensors
Strohrmann, C., Labruyère, R., Gerber, C. N., van Hedel, H. J. A., Bert Arnrich, A., & Tröster, G. (2013) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-10-83 · Cited by 40 · see citing works
Automated administration of the Wolf Motor Function Test for post-stroke assessment
Wade, E., Parnandi, A., & Matarić, M. J. (2010) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/icst.pervasivehealth2010.8903 · Cited by 40 · see citing works
Zhang, M., Lange, B. S., Chang, C., Sawchuk, A. A., & Rizzo, A. A. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6347388 · Cited by 38 · see citing works
Marker-Based Movement Analysis of Human Body Parts in Therapeutic Procedure
Khan, M. H., Zöller, M., Farid, M. S., & Grzegorzek, M. (2020) · Sensors · doi:10.3390/s20113312 · Cited by 38 · see citing works
Virtual proprioception with real-time step detection and processing
Lemoyne, R. C., Coroian, C., Mastroianni, T., Wu, W. H., Grundfest, W. S., & Kaiser, W. (2008) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2008.4650145 · Cited by 37 · see citing works
Dutta, D., Aruchamy, S., Mandal, S., & Sen, S. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3110432 · Cited by 34 · see citing works
Joint angle tracking with inertial sensors
El‐Gohary, M., Pearson, S., & McNames, J. (2008) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2008.4649344 · Cited by 30 · see citing works
Wearable wireless sensor network to assess clinical status in patients with neurological disorders
Lorincz, K., Kuris, B., Ayer, S. M., Patel, S., Bonato, P., & Welsh, M. (2007) · ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1145/1236360.1236441 · Cited by 30 · see citing works
Self-citationMeasuring upper limb function in children with hemiparesis with 3D inertial sensors
Newman, C. J., Bruchez, R., Roches, S., Gygax, M. J., Duc, C., Dadashi, F., Massé, F., & Aminian, K. (2017) · Child s Nervous System · doi:10.1007/s00381-017-3580-1 · Cited by 27 · see citing works
Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model
Khan, M. H., Schneider, M., Farid, M. S., & Grzegorzek, M. (2018) · Sensors · doi:10.3390/s18103202 · Cited by 27 · see citing works
Identifying Hand Use and Hand Roles After Stroke Using Egocentric Video
Tsai, M.-F., Wang, R. H., & Zariffa, J. (2021) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2021.3072347 · Cited by 23 · see citing works
Datta, S., Karmakar, C. K., Rao, A. S., Yan, B., & Palaniswami, M. S. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.2972285 · Cited by 20 · see citing works
A performance comparison study of ECC and AES in commercial and research sensor nodes
de la Piedra, A., Braeken, A., & Touhafi, A. (2013) · IEEE EUROCON - International Conference on Computer as a Tool (EUROCON) · doi:10.1109/eurocon.2013.6625007 · Cited by 18 · see citing works
Nguyen, G., MacLean, J., & Stirling, L. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3072010 · Cited by 17 · see citing works
Kinect-based virtual rehabilitation and evaluation system for upper limb disorders: A case study
Ding, W., zheng, Y., Su, Y. P., & Li, Y. (2018) · Journal of Back and Musculoskeletal Rehabilitation · doi:10.3233/bmr-140203 · Cited by 17 · see citing works
Comparison of robotic and clinical motor function improvement measures for sub-acute stroke patients
Çelik, Ö., O’Malley, M. K., Boake, C., Levin, H. S., Fischer, S., & Reistetter, T. (2008) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2008.4543585 · Cited by 16 · see citing works
Derungs, A., Schuster‐Amft, C., & Amft, O. (2018) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2018.00057 · Cited by 14 · see citing works
Motion-Based Video Games for Stroke Rehabilitation with Reduced Compensatory Motions
Alankuş, G. (2011) · Open Scholarship Institutional Repository (Washington University in St. Louis) · doi:10.7936/k7zg6q9r · Cited by 12 · see citing works
Towards a Smarter organization for a Self-servicing Society
Florio, V. D., Bakhouya, M., Eloudghiri, D., & Blondia, C. L. (2016) · International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion (DSAI) · doi:10.1145/3019943.3019980 · Cited by 12 · see citing works
Upper limb movement profiles during spontaneous motion in acute stroke
Datta, S., Karmakar, C. K., Rao, A. S., Yan, B., & Palaniswami, M. S. (2021) · Physiological Measurement · doi:10.1088/1361-6579/abf01e · Cited by 12 · see citing works
Resource Discovery in Activity-Based Sensor Networks
Bucur, D., & Bardram, J. E. (2007) · Mobile Networks and Applications · doi:10.1007/s11036-007-0015-3 · Cited by 12 · see citing works
Datta, S., Karmakar, C. K., Yan, B., & Palaniswami, M. S. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3024589 · Cited by 11 · see citing works
Tsai, M.-F., Wang, R. H., & Zariffa, J. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9176154 · Cited by 8 · see citing works
Li, Y., Gao, Y., Shao, M., Tonecha, J. T., Wu, Y., Hu, J., & Lee, I. (2021) · Journal of Low Power Electronics and Applications · doi:10.3390/jlpea11030034 · Cited by 8 · see citing works
Myographic Information Enables Hand Function Classification in Automated Fugl-Meyer Assessment
Formstone, L., Pucek, M., Wilson, S., Bentley, P., McGregor, A. H., & Vaidyanathan, R. (2019) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2019.8717014 · Cited by 7 · see citing works
Behrendt, F., & Schuster‐Amft, C. (2018) · BMJ Open · doi:10.1136/bmjopen-2017-019646 · Cited by 7 · see citing works
Kitagawa, K., Fernandez, I. G., Nagasaki, T., Nakano, S., Hida, M., Okamatsu, S., & Wada, C. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph181910481 · Cited by 7 · see citing works
Tanner, S., Stein, C., & Graves, S. J. (2009) · Springer eBooks · doi:10.1007/978-3-642-02788-8_13 · Cited by 7 · see citing works
Gait spectral index (GSI): a new quantification method for assessing human gait
Héliot, R., Coste, C., Schwirtlich, L. B., & Espiau, B. (2010) · Health · doi:10.4236/health.2010.21007 · Cited by 7 · see citing works
Addressing Security and Privacy Challenges in Internet of ThingsPreprint
Mosenia, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.06724 · Cited by 9 · see citing works
Goizueta, S., Navarro, M. D., Calvo, G., Campos, G., Colomer, C., Noé, E., & Lloréns, R. (2025) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-025-01563-6 · Cited by 6 · see citing works
Tool Use as Gesture: new challenges for maintenanceand rehabilitation
Parekh, M., & Baber, C. (2010) · Electronic workshops in computing · doi:10.14236/ewic/hci2010.30 · Cited by 6 · see citing works
Wearable Wireless Sensor Network to Assess Clinical Status in Patients with Neurological Disorders
Lorincz, K., Kuris, B., Ayer, S. M., Patel, S., Bonato, P., & Welsh, M. (2007) · ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1109/ipsn.2007.4379727 · Cited by 6 · see citing works
Self-citationSensor Abstracted Extremity Representation for Automatic Fugl-Meyer Assessment
Heyer, P., Orihuela‐Espina, F., Castrejón, L. R., Hernández-Franco, J., & Sucar, L. E. (2016) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-49622-1_17 · Cited by 4 · see citing works
Wireless device for people affected by neurodegenerative diseases
Bustamante, P., Solas, G., & Grandez, K. (2008) · Conference on Soft Computing as Transdisciplinary Science and Technology · doi:10.1145/1456223.1456250 · Cited by 4 · see citing works
Autonomous modeling of repetitive movement for rehabilitation exercise monitoring
Jatesiktat, P., Lim, G. M., Kuah, C. W. K., Anopas, D., & Ang, W. T. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01907-5 · Cited by 3 · see citing works
A framework for automated administration of post stroke assessment test
Parnandi, A. (2015) · doi:10.25549/usctheses-m3042 · Cited by 3 · see citing works
Use of Body Sensor Networks in Clinical Settings and Medical Research
Boano, C. A., Oppermann, F. J., & Römer, K. (2014) · CRC Press eBooks · doi:10.1201/b17124-11 · Cited by 3 · see citing works
Important Parameters for Hand Function Assessment of Stroke Patients
Rahman, H. B. A., Yeong, C. F., Khor, K. X., & Su, E. L. M. (2017) · TELKOMNIKA (Telecommunication Computing Electronics and Control) · doi:10.12928/telkomnika.v15i4.7228 · Cited by 3 · see citing works
Wireless Biomedical Sensor Networks: A technology review
Freitas, E., & Azevedo, A. (2018) · Journal of Biomedical Engineering and Biosciences · doi:10.11159/jbeb.2018.002 · Cited by 3 · see citing works
Poincaré Descriptors for Identifying Hemiparesis in Acute Stroke using Wearable Accelerometry
Datta, S., Karmakar, C. K., Yan, B., & Palaniswami, M. S. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175847 · Cited by 3 · see citing works
Real time activity recognition framework for health monitoring support in home environments
Hossain, S. F. (2017) · International Conference on Electrical Information and Communication Technology (EICT) · doi:10.1109/eict.2017.8275132 · Cited by 2 · see citing works
Wearable Technology in the Rehabilitation Hospital of the Future
Bonato, P. (2006) · IEEE/NIH Life Science Systems and Applications Workshop · doi:10.1109/lssa.2006.250378 · Cited by 2 · see citing works
Self-citationA case study on muscle activation analysis in post-stroke rehabilitation patients
Pavic, B., Daponte, P., Vito, L. D., & Silva, H. (2011) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea.2011.5966777 · Cited by 2 · see citing works
A rule-based approach for self-optimisation in autonomic eHealth systems
Neyens, G., & Zampuniéris, D. (2018) · Open Repository and Bibliography (University of Luxembourg) · Cited by 2 · see citing works
Analyzing Distance Measures for Upper Limb Activity Measurement in Hemiparetic Stroke Patients
Datta, S., Karmakar, C. K., Yan, B., & Palaniswami, M. S. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175758 · Cited by 2 · see citing works
[Changes in overjet with cuspless complete dentures].
Jakstat, H. A., & Wegmann, N. (1990) · PubMed · Cited by 2 · see citing works
Sensing Technologies for Biomedical Telemetry
Tamura, T. (2014) · Wiley eBooks · doi:10.1002/9781118893715.ch4 · Cited by 2 · see citing works
Chen, Y., Tian, F., Zeng, X., He, Y. L., Jiang, C., Zhang, X., Zhu, Y., & Wang, H. (2014) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2559206.2581268 · Cited by 1 · see citing works
Wearable Device for Patients Affected by Neurodegenerative Diseases
Solas, G., Bustamante, P., & Grandez, K. (2008) · International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM) · doi:10.1109/ubicomm.2008.70 · Cited by 1 · see citing works
Duiser, I. H. F., Ledebt, A., van der Kamp, J., & Savelsbergh, G. J. P. (2022) · Motor Control · doi:10.1123/mc.2021-0034 · Cited by 1 · see citing works
Quantitative motor assessment in patients with mobility limiting conditions using wearable sensors
Patel, S. (2012) · doi:10.17760/d20002546 · Cited by 2 · see citing works
Resource Discovery in Activity-Based Sensor Networks
Bucur, D., & Bardram, J. E. (2006) · doi:10.1109/pcthealth.2006.361663 · Cited by 1 · see citing works
Patel, S., Hughes, R. L., Hester, T., Stein, J. M., Akay, M., Dy, J., & Bonato, P. (2010)
Self-citationClass-based rate differentiation in wireless sensor networks
Takaffoli, M. (2009) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3404m
Difficulties with handwriting in primary school children
Duiser, I. H. F. (2023) · doi:10.5463/thesis.153 · Cited by 1 · see citing works
Bourbour, S., Boodelalou, F. B., & Taghizadeh, G. (2024) · doi:10.1109/icee63041.2024.10668315
- Authors
- Hester, T., Hughes, R., Sherrill, D. M., Knorr, B., Akay, M., Stein, J., & Bonato, P.
- Affiliations
- Harvard University (US)
- Dartmouth College (US)
- Arizona State University (US)
- Venue
- International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06)
- Publisher
- IEEE
- Published
- 2006-04-28
- Pages
- 5-8
- ISSN
- 2376-8886 (ISSN-L)
- OpenAlex
- W2109945587
- Cited by
- 82
- References
- 3
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 2 · 2022: 3 · 2021: 7 · 2020: 6 · 2019: 3 · 2018: 9 · 2017: 4 · 2016: 4 · 2015: 3 · 2014: 4 · 2013: 2 · 2012: 2
- FWCI
- 11.29
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Wearable and Implantable Body Sensor Networks (publishedVersion)
- Funders
- McMaster University
- Topics
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Keywords
- wearable sensors, stroke, motor ability assessment, stroke rehabilitation, clinical score prediction, home monitoring
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