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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.
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“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]
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Self-citationWearable systems for shoulder kinematics assessment: a systematic review
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The Manumeter: A Wearable Device for Monitoring Daily Use of the Wrist and Fingers
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Deb, S., Islam, M. F., Rahman, S., & Rahman, S. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3150392 · Cited by 88
A method to qualitatively assess arm use in stroke survivors in the home environment
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Human Activity Recognition in AAL Environments Using Random Projections
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BackgroundOubre, B., Daneault, J.-F., Jung, H.-T., Whritenour, K., Miranda, J. G. V., Park, J., Ryu, T., Kim, Y., & Lee, S. I. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.2966950 · Cited by 78
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BackgroundMethodInfluentialAI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI
Senadheera, I., Hettiarachchi, P., Haslam, B. S., Nawaratne, R., Sheehan, J., Lockwood, K. J., Alahakoon, D., & Carey, L. M. (2024) · Sensors · doi:10.3390/s24206585 · Cited by 90
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Review of Automated Systems for Upper Limbs Functional Assessment in Neurorehabilitation
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Self-citationMourchid, Y., & Slama, R. (2023) · Comput. Biol. Medicine · doi:10.1016/j.compbiomed.2023.107420 · Cited by 45
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Low-power sensor module for long-term activity monitoring
Leuenberger, K., & Gassert, R. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6090424 · Cited by 45
Automated Fugl-Meyer Assessment using SVR model
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“Then, some statistical features were computed such as max, mean, standard deviation, root mean square and approximate entropy, which have been proven useful in inertial sensor-based motor assessment [9].”
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“1) Data Segmentation: The sensor time-series recorded during the performance of the Reaching Tasks and the Manipulation Tasks were analyzed by first identifying the segments corresponding to distinct movement components.”
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Liu, X., Rajan, S., Ramasarma, N., Bonato, P., & Lee, S. I. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2821136 · Cited by 39
“tered and segmented accelerometer data to capture the intensity, smoothness, and periodicity of hand use [24].”
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
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Wearable technology for patients with brain and spinal cord injuries
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InfluentialDutta, D., Aruchamy, S., Mandal, S., & Sen, S. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3110432 · Cited by 34
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“Inertial measurement technology has been used to quantify many existing assessments [15] and movement behaviour in natural (non-laboratory) conditions [16–18].”
MethodKim, Y., Jung, H.-T., Park, J., Kim, Y., Ramasarma, N., Bonato, P., Choe, E. K., & Lee, S. I. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3369817 · Cited by 32
“It is noteworthy that, to compute the FAS score, we employed a subset of four (out of originally 15) motor tasks that were previously shown to be sufficient to reflect the total FAS score [50] (i.”
MethodSelf-citationA Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients
Yu, L., Xiong, D., Guo, L., & Wang, J. (2016) · Sensors · doi:10.3390/s16020202 · Cited by 31
“[21] proposed a Random Forests-based algorithm to estimate Functional Ability Scale (FAS) scores by using the signals of six accelerometers placed on the affected arm and the trunk.”
MethodA novel fuzzy approach for automatic Brunnstrom stage classification using surface electromyography
Liparulo, L., Zhang, Z., Panella, M., Gu, X., & Fang, Q. (2016) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-016-1597-3 · Cited by 31
“Nevertheless, the performance of the proposed approach is highly competitive to many current state-of-art inertia or optical measurement-based classification methods [1, 22, 44, 67] and it proves that, alone with kinematic and physiological inputs, single-channel sEMG sampled during dynamic exercise can also be treate…”
BackgroundIntelligent Shoulder Joint Home-Based Self-Rehabilitation Monitoring System
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BackgroundInfluentialDesign and Study of a Smart Cup for Monitoring the Arm and Hand Activity of Stroke Patients
Bobin, M., Anastassova, M., Boukallel, M., & Ammi, M. (2018) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2018.2853553 · Cited by 28
“[25] used accelerometers to accurately estimate the scores assigned by clinicians using the functional ability scale.”
MethodDevelopment of an EMG-Based Muscle Health Model for Elbow Trauma Patients
Farago, E., Chinchalkar, S. J., Lizotte, D. J., & Trejos, A. L. (2019) · Sensors · doi:10.3390/s19153309 · Cited by 30
“The RF algorithm prevents decision tree models from considering most of the available predictors at each split (usually the number of predictors considered is the square root of the total number of predictors) [48].”
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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
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“…located on the arm and chest to assess stroke patients as they performed a subset of the motor tasks pertaining to the functional ability scale (Patel et al 2010), whilst Subramanian et al used kinematic data collected using an optical system with markers placed on the hand, elbow and…”
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“One suggestion was the combination of accelerometers with a random Forest classifier, performing multiple repetitions to gather information about within subject variability [8].”
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“[9], machine learning techniques were applied to accelerometer data in order to successfully estimate the full Wolf functional ability scale (FAS) in stroke survivors.”
MethodDatta, 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
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“Early RPM proposals measured only a single physiological signal, primarily ECG [56] and activity level [17, 42].”
BackgroundQuantitative Upper Limb Impairment Assessment for Stroke Rehabilitation: A Review
Wang, X., Zhang, J., Xie, S. Q., Shi, C., Li, J., & Zhang, Z. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3359811 · Cited by 20
“Generally, the Davies–Bouldin (DB) cluster validity index [118] method was also used at the same time to add one feature at a time according to the feature ranking obtained by the Relief F algorithm and select the most suitable feature amount [50].”
BackgroundMethodInfluentialSensor-Based Rehabilitation in Neurological Diseases: A Bibliometric Analysis of Research Trends
Facciorusso, S., Spina, S., Reebye, R. N., Turolla, A., Calabrò, R. S., Fiore, P., & Santamato, A. (2023) · Brain Sciences · doi:10.3390/brainsci13050724 · Cited by 18
A wearable monitoring system for at-home stroke rehabilitation exercises: A preliminary study
Jung, H.-T., Park, J., Jeong, J., Ryu, T., Kim, Y., & Lee, S. I. (2018) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2018.8333358 · Cited by 16
“A number of studies attempted to build monitoring systems using data collected from a set of wearable inertial sensors to estimate quality of rehabilitation exercise movements [10]–[12].”
BackgroundSpECTRUM: Smart ECosystem for sTRoke patient׳s Upper limbs Monitoring
Bobin, M., Bimbard, F., Boukallel, M., Anastassova, M., & Ammi, M. (2019) · Smart Health · doi:10.1016/j.smhl.2019.01.001 · Cited by 16
Wearable Sensors for Stroke Rehabilitation
Adans-Dester, C. P., Lang, C. E., Reinkensmeyer, D. J., & Bonato, P. (2022) · Springer eBooks · doi:10.1007/978-3-031-08995-4_21 · Cited by 16
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“Similar system was developed in [10] for Functional Ability Scale (FAS) for stroke patients.”
MethodSheng, B., Wang, X., Hou, M., Huang, J., Xiong, S., & Zhang, Y. (2020) · Measurement · doi:10.1016/j.measurement.2020.107896 · Cited by 15
Bernal, V. C. E., Hiremath, S. V., Wolf, B., Riley, B., Mendonca, R. J., & Johnson, M. J. (2021) · Journal of Rehabilitation and Assistive Technologies Engineering · doi:10.1177/20556683211044640 · Cited by 15
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Decision support for stroke rehabilitation therapy via describable attribute-based decision trees
Venkataraman, V., Turaga, P. K., Lehrer, N., Baran, M., Rikakis, T., & Wolf, S. L. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944292 · Cited by 15
“Several automated approaches exist in literature to quantify movement quality based on complex models including nonlinear dynamical system theory [18], [20], [22], random forests [16], and SVMs [17].”
MethodGold Nanoparticle Treated Textile-Based Materials for Potential use as Wearable Sensors
Poinern, G. E. J. (2016) · International Journal of Sciences · doi:10.18483/ijsci.1018 · Cited by 15
Bandini, A., Kalsi‐Ryan, S., Craven, B. C., Zariffa, J., & Hitzig, S. L. (2021) · Journal of Spinal Cord Medicine · doi:10.1080/10790268.2021.1920787 · Cited by 15
“12.”
Trauth, E., & Browning, E. R. (2018) · International Journal of Sociotechnology and Knowledge Development · doi:10.4018/ijskd.2018070101 · Cited by 13
A single task assessment system of upper-limb motor function after stroke
Li, J., Pan, B., Jin, T., Huang, Z., Ye, S., Wu, J., Huang, Z., Xie, B., Luo, C., & Wang, C. (2016) · Technology and Health Care · doi:10.3233/thc-161199 · Cited by 13
“Through these data, they extracted some features and quantitatively evaluated the performance of the motion [12,13].”
MethodA comparative study of motion recognition methods for efficacy assessment of upper limb function
He, J., Chen, S., Guo, Z., Pirbhulal, S., Wu, W., Feng, J., & Guo, D. (2018) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.2941 · Cited by 13
Sheng, B., Chen, X., Jian, C., Zhang, Y., Xie, S. Q., Tao, J., & Duan, C. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107887 · Cited by 12
Hughes, C. M. L., Baye, M., Gordon-Murer, C., Louie, A. V., Sun, S., Belay, G. J., & Zhang, X. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.01323 · Cited by 12
The application of EMD in activity recognition based on a single triaxial accelerometer
Liao, M., Guo, Y., Qin, Y., & Wang, Y. (2015) · Bio-Medical Materials and Engineering · doi:10.3233/bme-151452 · Cited by 12
“chose six accelerometers to classify the rehabilitation stage of different stroke patients through operating certain motions [4].”
Adans-Dester, C. P., Fasoli, S. E., Fabara, E. E., Menard, N., Fox, A. B., Severini, G., & Bonato, P. (2020) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-020-00730-1 · Cited by 12
Self-citationIntroduction of a sEMG Sensor System for Autonomous Use by Inexperienced Users
Avila, E. R., Junker, E., & Dißelhorst-Klug, C. (2020) · Sensors · doi:10.3390/s20247348 · Cited by 12
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 13
“Various time and frequency domain features from accelerometer data correlate well with clinical scores in stroke patients (Hester et al 2006, Patel et al 2010, 2010,Del Din et al 2011, Li et al 2015).”
ResultRazfar, N., Kashef, R. F., & Mohammadi, F. A. (2023) · Sensors · doi:10.3390/s23125513 · Cited by 15
“[28,29] estimated the functional ability scale using accelerometer sensors attached to the arm, upper arm, and hand.”
MethodSassi, M., Carnevale, A., Mancuso, M., Schena, E., Pecchia, L., & Longo, U. G. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3406138 · Cited by 11
“1) [14], [15].”
Nelson, A., Waller, S. M., Robucci, R., Patel, C., & Banerjee, N. (2018) · Journal of Rehabilitation and Assistive Technologies Engineering · doi:10.1177/2055668318762063 · Cited by 11
A Game Changer: ‘The Use of Digital Technologies in the Management of Upper Limb Rehabilitation’
Ballantyne, R., & Rea, P. (2019) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-31904-5_9 · Cited by 12
Bandini, A., Dousty, M., & Zariffa, J. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9176274 · Cited by 11
“functional ability scale) were estimated in stroke survivors by using accelerometer data from upper limb movements [5], [6], whereas IMUs were used in patients with acute SCI for monitoring their upper limb and wheeling activities [7], [8].”
MethodDatta, 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
“The performance of the proposed bPP descriptors is compared with standard time and frequency domain hand activity measures [9], [10] in classifying hemiparesis.”
MethodLee, S.-H., Hwang, Y., Lee, H.-J., Kim, Y., Ogrinc, M., Burdet, E., & Kim, J. (2021) · Italian National Conference on Sensors · doi:10.3390/s21175926 · Cited by 11
“Another reason would be that the machine learning methods used in the existing works for FMA are not appropriate to handle this issue because (1) some of them (support vector machine [17,19] and Naive– Bayes classification [20]) cannot be used for regression and (2) the others (extreme machine learning [14], artificia…”
MethodLi, Y., Li, C., Shu, X., Sheng, X., Jia, J., & Zhu, X. (2022) · Brain Sciences · doi:10.3390/brainsci12101380 · Cited by 10
“The Shoulder/Elbow part was scored using random forest (RF) classification, an effective machine learning algorithm for estimating scale scores from kinematic features [28,29].”
MethodTang, C., Zhang, R., Gao, S., Zhao, Z., Zhang, Z., Wang, J., Li, C., Chen, J., Dai, Y., Wang, S., et al. (2025) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2025.3645093 · Cited by 18
“Various wearable sensors—including force sensors [13], [14], [15], [16], accelerometers [17], [18], [19], [20], and eye trackers [21], [22], [23]—have shown promise in tracking motor recovery and capturing aspects of daily function such as gait stability, balance, and fine motor control for…”
On the data analysis for classification of elementary upper limb movements
Biswas, D., Cranny, A., Rahim, A., Gupta, N., Maharatna, K., Harris, N. R., & Ortmann, S. (2014) · Biomedical Engineering Letters · doi:10.1007/s13534-014-0160-0 · Cited by 9
“In this investigation, we consider 10 time-domain features as follows: 1) standard deviation, 2) root mean square (rms), 3) information entropy - measure of the randomness in a signal [26], 4) jerk metric - rms value of the second derivative of the data normalized with respect to the maximum value of the first derivat…”
MethodLow-Complexity Framework for Movement Classification Using Body-Worn Sensors
Biswas, D., Maharatna, K., Panić, G., Mazomenos, E. B., Achner, J., Klemke, J., Jöbges, M., & Ortmann, S. (2017) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2016.2641046 · Cited by 9
“It is calculated as the rms value of the derivative of the acceleration (jerk) normalized by the maximum value of the integral (velocity) [19] as shown in”
MethodUsing Commercial Physical Activity Trackers for Health Promotion Research: Four Case Studies
Turner‐McGrievy, G. M., Jake‐Schoffman, D. E., Singletary, C. R., Wright, M., Crimarco, A. E., Wirth, M. D., Shivappa, N., Mandes, T., West, D. S., Wilcox, S., et al. (2018) · Health Promotion Practice · doi:10.1177/1524839918769559 · Cited by 9
“Wearable trackers can also be used to detect falls, assist with proper posture and balance, and help with physical rehabilitation at home (Patel et al., 2010; Yang & Hsu, 2010).”
Alavi, N., Zampierin, S., Komeili, M., Cocuzza, S., Debei, S., & Menon, C. (2017) · BioMedical Engineering OnLine · doi:10.1186/s12938-017-0345-8 · Cited by 9
“Sensors such as force sensing resistors (FSRs) [6], capacitive force sensors [7], load cells [8], and torque sensors [9] are integrated into these systems to measure the applied forces that are exerted on the mechanical system.”
BackgroundLee, S. I., Jung, H.-T., Park, J., Jeong, J., Ryu, T., Kim, Y., dos Santos, V. S., Miranda, J. G. V., & Daneault, J. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512845 · Cited by 9
“There has been some work that estimated movement quality based on inertial data collected from different parts of the body [5], [6].”
TAMURA, T. (2017) · Springer eBooks · doi:10.1007/978-3-319-69362-0_8 · Cited by 9
Corniani, G., Sapienza, S., Vergara-Diaz, G. P., Valerio, A., Vaziri, A., Bonato, P., & Wayne, P. M. (2025) · Scientific Reports · doi:10.1038/s41598-025-93979-2 · Cited by 8
“This approach is particularly suitable for small datasets, as demonstrated in previous studies 56,57 .”
Self-citationEstimating physical ability of stroke patients without specific tests
Derungs, A., Seiter, J., Schuster‐Amft, C., & Amft, O. (2015) · Proceedings · doi:10.1145/2802083.2808412 · Cited by 8
“[4] used classification in combination with regression to estimate the FAS from accelerometers placed on several body locations (index finger, thumb, hand, forearm, upper arm and trunk).”
BackgroundMethodTsai, 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
“Meng-Fen Tsai and José Zariffa laboratories rather in home or community environments [8, 9].”
BackgroundUsing a Minimum Set of Wearable Sensors to Assess Quality of Movement in Stroke Survivors
Sapienza, S., Adans-Dester, C., OBrien, A., Vergara-Diaz, G., Lee, S., Patel, S., Black-Schaffer, R., Zafonte, R., Bonato, P., Meagher, C., et al. (2017) · 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) · doi:10.1109/chase.2017.104 · Cited by 9
“Based on this consideration, we previously explored the use of wearable sensing technology as a means to gather data suitable to assess changes in the quality of the movements performed by stroke survivors undergoing rehabilitation [2].”
BackgroundSelf-citationDetecting delays in motor skill development of children through data analysis of a smart play device
Sander, J., de Schipper, A. W., Brons, A., Mironcika, S., Toussaint, H. M., Schouten, B. A. M., & Kröse, B. (2017) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.1145/3154862.3154867 · Cited by 7
“stroke survivors [8] or cerebral palsy [9].”
Behrendt, F., & Schuster‐Amft, C. (2018) · BMJ Open · doi:10.1136/bmjopen-2017-019646 · Cited by 9
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
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