The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
A Novel Approach to Monitor Rehabilitation Outcomes in Stroke Survivors Using Wearable Technology
Open accessAbstract
Quantitative assessment of motor abilities in stroke survivors can provide valuable feedback to guide clinical interventions. Numerous clinical scales were developed in the past to assess levels of impairment and functional limitation in individuals after stroke. The Functional Ability Scale is one of these clinical scales. It is a 75-point scale used to evaluate the functional ability of subjects by grading movement quality during performance of 15 motor tasks. Performance of these motor tasks requires subjects to reach for objects (e.g., a pencil on a table) and manipulate them (e.g., lift the pencil). In this paper, we show that accelerometer data recorded during performance of a subset of the motor tasks pertaining to the Functional Ability Scale can be relied upon to derive accurate estimates of the scores provided by a clinician using this scale. Accelerometer-based estimates of clinical scores were obtained by segmenting the recordings into movement components (reaching, manipulation, release/return), extracting data features, selecting features that maximized the separation among classes associated with different clinical scores, feeding these features to Random Forests to estimate scores for individual motor tasks, and using a linear equation to estimate the total Functional Ability Scale score based on the sum of the clinical scores for individual motor tasks derived from the accelerometer data. Results showed that it is possible to achieve estimates of the total Functional Ability Scale score marked by a bias of only 0.04 points of the scale and a standard deviation of only 2.43 points when using as few as three sensors to collect data during performance of only six motor tasks.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Finally, Patel et al. [109] showed that, using accelerometers placed on the arm, it is possible to derive accurate estimates of upper extremity functional ability.”
From A review of wearable sensors and systems with application in rehabilitation (2012) · cites this paper as [109]
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
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 25,543
Witten, I. H. (2008) · Cited by 9,143
The random subspace method for constructing decision forests
Ho, T. K. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.709601 · Cited by 7,005 · cited 1×
“To estimate the FAS scores associated with each of the motor tasks listed in Table 1, we chose to train Random Forests [31], [ 32 ] (RFs) with accelerometer data features derived and selected as summarized above.”
MethodHeart Disease and Stroke Statistics—2009 Update
WRITING GROUP MEMBERS, Lloyd‐Jones, D. M., Adams, R. J., Carnethon, M. R., SIMONE, G. D., Ferguson, T. B., Flegal, K. M., Ford, E. S., Furie, K. L., Go, A. S., et al. (2009) · Circulation · doi:10.1161/circulationaha.108.191259 · Cited by 3,915
A Practical Approach to Feature Selection
Kira, K., & Rendell, L. A. (1992) · Elsevier eBooks · doi:10.1016/b978-1-55860-247-2.50037-1 · Cited by 3,090 · cited 1×
“For the Manipulation Tasks, the results were derived using features estimated from all three data segments (S1V reaching, S2Vmanipulation, and S3Vrelease/return).”
MethodTheoretical and Empirical Analysis of ReliefF and RReliefF
Robnik-Šikonja, M., & Kononenko, I. (2003) · Machine Learning · doi:10.1023/a:1025667309714 · Cited by 3,025 · cited 2×
“First, we used the ReliefF [ 27 ] algorithm which ranks the features in decreasing order of importance.”
MethodReview of control strategies for robotic movement training after neurologic injury
Marchal-Crespo, L., & Reinkensmeyer, D. J. (2009) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-6-20 · Cited by 1,182
Some new indexes of cluster validity
Bezdek, J. C., & Pal, N. R. (1998) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/3477.678624 · Cited by 1,143 · cited 1×
“This was achieved by calculating the Davies-Bouldin (DB) cluster validity index [ 30 ].”
MethodWOLF, S., LeCraw, D. E., BARTON, L., & Jann, B. B. (1989) · Experimental Neurology · doi:10.1016/s0014-4886(89)80005-6 · Cited by 892
Cited by
Sorted by citations
Items 101–169 of 169
Activity Routine Discovery in Stroke Rehabilitation Patients without Data Annotation
Seiter, J., Derungs, A., Schuster‐Amft, C., Amft, O., & Tröster, G. (2014) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/icst.pervasivehealth.2014.255275 · Cited by 6
“Uswatte et al. used accelerometers at the wrists to infer the activity of the impaired arm [11].”
Zhi, T., Meng, C., & Fu, L. (2022) · Journal of Sensors · doi:10.1155/2022/9323152 · Cited by 6
“Patel team [15, 19] has done a lot of research on the use of motion sensors in clinical evaluation, but they mainly Preprocessing”
BackgroundMethodTran, B., Zhang, X., Modan, A., & Hughes, C. M. L. (2022) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob52689.2022.9925549 · Cited by 6
“Inertial measurement units (IMUs) are low-cost portable devices that can precisely and quantitatively measure movement kinematics, which enable clinicians to identify pathological movement, characterize disease state, and track recovery progress [7-11].”
BackgroundKnowledge discovery, rehabilitation robotics, and serious games: Examining training data
Moretti, C. B., Joaquim, R. C., Caurin, G. A. P., Krebs, H. I., & Martins, J. A. (2014) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob.2014.6913838 · Cited by 5
“In order to interpret this new big collection of data, there are several machine learning techniques for mining and analyzing data that have been used to discover new information and knowledge with this rehabilitation robotics method [7][11][13][14].”
BackgroundUsing trajectory features for upper limb action recognition
Wang, X., Suvorova, S., Vaithianathan, T., & Leckie, C. A. (2014) · International Conference on Intelligent Sensors, Sensor Networks and Information Processing · doi:10.1109/issnip.2014.6827613 · Cited by 5
“I. INTRODUCTION Using wearable sensors in medical applications such as stroke rehabilitation and physiotherapy to model limb movement has attracted growing attention [1].”
BackgroundHand acceleration measurement by Kinect for rehabilitation applications
Mobini, A., Behzadipour, S., & Foumani, M. S. (2017) · Scientia Iranica · doi:10.24200/sci.2017.4025 · Cited by 4
“Position derivatives such as velocity, acceleration, and jerk, also known as kinematic parameters, are of critical importance for a comprehensive analysis of human movements [18-21].”
BackgroundMethodOn the sensor choice and data analysis for classification of elementary upper limb movements
Biswas, D., Cranny, A., Rahim, A., Gupta, N., Harris, N. R., Maharatna, K., & Ortmann, S. (2014) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2014.6864471 · Cited by 4
“We consider 10 time-domain features, chosen from amongst those most commonly used in human activity recognition: 1) standard deviation, 2) root mean square (rms), 3) information entropy [10], 4) jerk metric [11], 5) peak number, 6) maximum peak amplitude, 7) absolute difference - absolute difference between the maximu…”
MethodBiswas, D., Cranny, A., Gupta, N., Maharatna, K., & Ortmann, S. (2014) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2014.40 · Cited by 4
“The RELIEF algorithm [27], Clamping technique [16] and Principal Component Analysis (PCA) [11] are the most commonly used ranking/selection algorithms in the field of human activity recognition but are computationally intensive.”
MethodComplex human action recognition on daily living environments using wearable inertial sensors
López-Nava, I. H., & Muñoz-Meléndez, A. (2016) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/eai.16-5-2016.2263332 · Cited by 4
Peker, M., Ballı, S., & Sağbaş, E. A. (2020) · IGI Global eBooks · doi:10.4018/978-1-7998-2460-2.ch017 · Cited by 4
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
“Similar digital markers or rule-based techniques for automatic segmentation are commonly found in the analysis of upper limb movements [15, 28].”
BackgroundNeurorehabilitation mit einem Roboterball – ein geeignetes Therapiekonzept?
Neuendorf, T., Zschäbitz, D., Nitzsche, N., & Schulz, H. (2017) · neuroreha · doi:10.1055/s-0043-101367 · Cited by 3
López-Nava, I. H., & Muñoz-Meléndez, A. (2018) · International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI) · doi:10.3390/proceedings2191238 · Cited by 3
“Action recognition is important for various applications, such as, ambient intelligence, smart devices, and healthcare [1,2].”
BackgroundKhoury, G. E., Penta, M., Barbier, O., Libouton, X., Thonnard, J., & Lefèvre, P. (2021) · Sensors · doi:10.3390/s21093245 · Cited by 3
“Indeed, the correlations observed between the kinematic analysis of the upper limb, questionnaire scores and observational methods [30,31] indicate that an approach combining objective activity monitoring and question‐ naire scores could help clinicians in the selection of the optimal treatments for their pa‐ tients.”
BackgroundCount What You Want: Exemplar Identification and Few-Shot Counting of Human Actions in the Wild
Huang, Y., Nguyen, D. D., Nguyen, L., Pham, C., & Hoai, M. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i9.28869 · Cited by 3
Boiarskaia, E. A. (2016) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 3
Peker, M., Ballı, S., & Sağbaş, E. A. (2018) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-5225-4766-2.ch017 · Cited by 3
Cranny, A., Beriain, A., Solar, H., Tartarisco, G., & Pioggia, G. (2013) · Springer eBooks · doi:10.1007/978-1-4614-8842-2_3 · Cited by 3
tuPOY: Epitomizing a New Epoch in Communications With Polymer Textiles
Karamchandani, S., Mustafa, H. D., Merchant, S. N., & Desai, U. B. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2011.2178009 · Cited by 3
Experience of network neuro-rehabilitation project implementation in Russia
Jafarova, O. A., Tarasov, E. A., Guk, R. Y., & Shtark, M. B. (2014) · International Scientific-Technical Conference on Actual Problems of Electronic Instrument Engineering (APEIE) · doi:10.1109/apeie.2014.7040730 · Cited by 3
“High-tech technologies applied for medical monitoring of disabled at home are aimed to assess fall risk, control gait, and after all, evaluate the success of rehab efforts [2].”
BackgroundImportant 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
“Previous studies showed that the kinematic variables extracted from the wearable sensors can be used to predict the conventional assessment scale score in the stroke populations [24-26].”
BackgroundUsing Hidden Markov Models and Rule-based Sensor Mediation on Wearable eHealth Devices
Neyens, G., & Zampuniéris, D. (2017) · Open Repository and Bibliography (University of Luxembourg) · Cited by 3
Unsupervised phase learning and extraction from quasiperiodic multidimensional time-series data
Jatesiktat, P., Lim, G. M., Kuah, C. W. K., & Ang, W. T. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106386 · Cited by 3
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
“Various time and frequency domain features from accelerometer data have been previously used to predict clinical scores in stroke patients [8], [9].”
MethodA Synchronous Acquisition System of Ultrasound, sEMG and IMU for Human Motion Prediction
Liu, Y., Yin, Z., Yang, H., Yang, X., & Liu, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-13835-5_8 · Cited by 3
Multi-Dimensional Assessment of Rehabilitation Exercises with Multi-gate Mixture-of-Experts
He, T. C., Wang, L., & Cheng, H. (2025) · doi:10.1145/3730436.3730498 · Cited by 3
“These methods include hidden Markov models [6, 13, 39], Support Vector Machines [30, 42], and Gaussian mixture models [23], among others [10, 41].”
Sheng, B., Lei, X., Jian, C., Xie, Q., Tao, J., & Chen, Y. (2024) · Journal of Shanghai Jiaotong University (Science) · doi:10.1007/s12204-024-2754-8 · Cited by 2
“In the third study [43] , data was collected based on accelerometers, and features were extracted from the data.”
Kumar, S. P., Saranya, S. S., Kavitha, A., & SRI, R. D. (2025) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s0219519425400834 · Cited by 2
A Body-Sensed Motor Assessment System for Stroke Upper-Limb Rehabilitation: A Preliminary Study
Lee, C.-H., Chiu, Y.-H., Kao, H.-Y., Chen, I.-T., Lee, I.-N., Ho, W., Chen, L., Huang, P.-H., Chen, S.-H., Liu, C.-Y., et al. (2013) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2013.652 · Cited by 2
A dedicated wearable device integrated in textiles
Leba, M., Ionica, A. C., & Risteiu, M. N. (2020) · Industria Textila · doi:10.35530/it.071.01.1541 · Cited by 2
Body Area Sensing Networks for Remote Health Monitoring
Biswas, D., Cranny, A., & Maharatna, K. (2015) · Springer eBooks · doi:10.1007/978-3-319-21293-7_4 · Cited by 2
“4 Jerk metric rms value of the second derivative of the data normalized with respect to the maximum value of the first derivative [3]”
BackgroundMethodInfluentialActivity Patterns in Stroke Patients - Is There a Trend in Behaviour During Rehabilitation?
Derungs, A., Seiter, J., Schuster‐Amft, C., & Amft, O. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24195-1_11 · Cited by 2
“[10,12,13].”
Eskofier, B. M., Paulus, J., Paulsen, U., Burkart, M., Wullich, B., & Huppert, V. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6943654 · Cited by 2
“Automatic, sensor-based assessment systems with similar constraints have been developed that allow automatic assessment of sleep [9], daily life activities [10], and several medical conditions like Parkinson’s disease [11], stroke [12], and arrhythmia [13].”
BackgroundAutomated Brunnstrom Assessment for Home Rehabilitation Based on GRNN Model
Wang, J., Guo, L., Sheng, T.-Y., & Xiong, D. (2017) · ITM Web of Conferences · doi:10.1051/itmconf/20171201021 · Cited by 2
“[11] have proposed a method based on Random.”
MethodYu, L., Wang, J., Guo, L., Zhang, Q., Li, P., Li, Y., Yu, X., Huang, Y., & Wu, Z. (2017) · doi:10.1109/incit.2017.8257874 · Cited by 2
“[7] proposed an accelerometer based method to monitor the”
MethodA 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
Smart Objects Ecosystem for Post-Stroke Upper Limbs' Motor Functions Monitoring
Bobin, M., Anastassova, M., Boukallel, M., Bimbard, F., & Ammi, M. (2018) · International Conference on Digital Tools & Uses Congress (DTUC) · doi:10.1145/3240117.3240133 · Cited by 2
“Many works are based on accelerometers [4, 9] but wearables also includes smart textiles were the sensors are knitted directly onto the garment [14].”
BackgroundEstimating Quality of Reaching Movement Using a Wrist-Worn Inertial Sensor
Oubre, B., Daneault, J., Jung, H.-T., Park, J., Ryu, T., Kim, Y., & Lee, S. I. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175708 · Cited by 2
“Though previous works have shown that it is possible to estimate overall movement quality [5], [6], they are unable to estimate the quality of individual movements, which would provide greater insight when measuring movement quantity and quality in tandem.”
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
“Wearable accelerometry has been previously used to predict clinical scores using time and frequency domain features in stroke patients during rehabilitation (after patient is discharged from hospital) [8], [9].”
MethodEstimating the Quality of Reaching Movements in Stroke Survivors
Lee, J., Jung, H.-T., & Lee, S. I. (2021) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi50953.2021.9508566 · Cited by 3
[Changes in overjet with cuspless complete dentures].
Jakstat, H. A., & Wegmann, N. (1990) · PubMed · Cited by 2
Tian, M., Yang, X., Chen, Y., Chen, W., & Wang, L. (2019) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2019.2922247 · Cited by 2
“It is considered to have broad application potential in the Internet of Things, human interaction, biomedicine, and other fields [1]–[4].”
BackgroundWearable sensor technologies applied for post-stroke rehabilitation
Zhang, Z. (2015) · RMIT Research Repository (RMIT University Library) · doi:10.25439/rmt.27581808 · Cited by 2
Game-Assisted Rehabilitation for Post-Stroke Survivors
Jung, H.-T. (2021) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · doi:10.7275/15213362 · Cited by 2
Wearable Computing Systems: State‐of‐the‐Art and Research Challenges
Fortino, G., & Gravina, R. (2023) · doi:10.1002/9781119863663.ch29 · Cited by 2
Monitoring Patient Recovery Using Wireless Physiotherapy Devices
Roy, N., & Kindle, B. R. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-14424-5_8 · Cited by 1
“Our goal is to create a mobile app that tracks the recovery progress of physiotherapy patients with motor deficits - such as stroke patients - by collecting real time data from wireless sensors about various prescribed exercises and then providing both qualitative and quantitative feedback to the patient without the n…”
Classification of the Shoulder Movements for Intelligent Frozen Shoulder Rehabilitation
., S., Khandnor, P., Kumar, N., & Das, R. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-8971-8_1 · Cited by 1
Han, S. W., & Kim, Y.-N. (2015) · Arrhythmia · doi:10.18501/arrhythmia.2015.027 · Cited by 1
Moretti, C. B., Joaquim, R. C., Terranova, T. T., Battistella, L. R., Mazzoleni, S., & Caurin, G. A. P. (2016) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob.2016.7523711 · Cited by 1
“In order to extract and interpret valuable information, there are several machine learning techniques for mining and analyzing data which have been used to discover new information and knowledge within this rehabilitation robotics method [5] [13] [14] [15].”
BackgroundAlisa, A. (2016) · IEEE Global Humanitarian Technology Conference (GHTC) · doi:10.1109/ghtc.2016.7857346 · Cited by 1
Capela, N. A. (2015) · uO Research (University of Ottawa) · doi:10.20381/ruor-4167 · Cited by 1
“Wearable sensors have been used to assess movement quality after stroke, such as upper extremity motion [75] or gait characteristics [76].”
MethodData-driven phase extraction for anomaly detection of repetitive human movements
Jatesiktat, P. (2019) · doi:10.32657/10220/47998 · Cited by 1
Exploiting wearable technology to estimate Fugl-Meyer clinical scores in stroke patients
Din, S. D., Patel, S., Cobelli, C., & Bonato, P. (2012) · Gait & Posture · doi:10.1016/j.gaitpost.2011.09.062
Self-citationDesign of Virtual Reality Contents for Upper-limbs Rehabilitation Using Kinect Sensor
Park, M.-C., Jung, H.-C., & Kang, H.-S. (2015) · Journal of the Korea Society of Computer and Information · doi:10.9708/jksci.2015.20.10.085
“0 / n) * i * GL_PI / 180; x = m_fTrainRadius * cos(angle); y = m_fTrainRadius * sin(angle); glVertex3f( x, y, z); } glVertex3f( m_fTrainRadius, 0, z); glEnd(); GLdouble gcx, gcy, gcz; gcx = m_faPosCursor[0]; gcy = m_faPosCursor[1]; gcz = m_faPosCursor[2]; glTranslatef( gcx, gcy, gcz); glColor3f(1.”
Polymeric Pressure Cushions for Potential Applications on Forearm Robotic Orthoses
Alavi, N. (2016) · Summit (Simon Fraser University)
Haladjian, J., Taheritanjani, S., & Bruegge, B. (2018) · Biosystems & biorobotics · doi:10.1007/978-3-030-01845-0_129
Tian, M., Yang, X., Chen, Y., & Wang, L. (2019) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2019.2891731
“It is considered to have broad application prospects in the Internet of things, human interaction, and other fields [1]–[3].”
BackgroundSmart Monitoring System for Stroke Rehabilitation
Atallah, M. A., Jamil, Z., Khafagy, H., Ummar, M., Aloul, F., & Sagahyroon, A. A. (2021) · IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) · doi:10.1109/iotais53735.2021.9628644
Trauth, E., & Browning, E. R. (2021) · IGI Global eBooks · doi:10.4018/978-1-6684-2414-8.ch031
Sheng, B., Wang, X., Hou, M., Huang, J., Xiong, S., & Zhang, Y. (2019) · doi:10.2196/preprints.16731
Shoulder and Wrist Rehabilitation: A Biofeedback Simulation using IoT
Sridevi, R., B, S. S., Logeshsanjay, R., Manikandan, N., & Venugopal, B. (2023) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei56765.2023.10125649
“[4] used an accelerometer to grab information from persons with stroke who performed tasks based on the functional ability scale while contrasting the computation findings to the total scale provided by medical experts.”
MethodMuscle Activation Patterns Differentiate Post-stroke and Healthy Population
Eusebio-Martín, E., Salido-Ruiz, R. A., Torres-Ramos, S., & Román-Godínez, I. (2023) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-46933-6_18
Skvortsov, D. V., Александрович, Л. Д., & Ivanova, G. Е. (2024) · Journal of clinical practice · doi:10.17816/clinpract636242
Wang, E., & Nie, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-13187-4_26
GRAFF-MM : Graph-Frequency Filtering for Multi-Modal Memorability PredictionPreprint
Banna, T. T., Deb, S., Rahman, S., & Rahman, S. (2026) · Research Square · doi:10.21203/rs.3.rs-10283943/v1
Metadata
- Authors
- Patel, S., Hughes, R., Hester, T., Stein, J., Akay, M., Dy, J. G., & Bonato, P.
- Affiliations
- Harvard University (US)
- The University of Texas at Austin (US)
- Cornell University (US)
- University of Houston (US)
- Northeastern University (US)
- Venue
- Proceedings of the IEEE
- Published
- 2010-03
- Volume
- 98
- Issue
- 3
- Pages
- 450-461
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W2163901584
- Cited by
- 170
- References
- 36
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 7 · 2023: 8 · 2022: 7 · 2021: 11 · 2020: 23 · 2019: 14 · 2018: 21 · 2017: 15 · 2016: 15 · 2015: 10 · 2014: 17 · 2013: 8 · 2012: 3
- FWCI
- 4.98
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Proceedings of the IEEE (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- National Institutes of Health
- Topics
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- stroke rehabilitation, accelerometer data, wearable sensors, random forest, movement segmentation, feature selection, stroke survivors, motor impairment
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
