Estimating upper-limb impairment level in stroke survivors using wearable inertial sensors and a minimally-burdensome motor task
Abstract
Upper-limb paresis is the most common motor impairment post stroke. Current solutions to automate the assessment of upper-limb impairment impose a number of critical burdens on patients and their caregivers that preclude frequent assessment. In this work, we propose an approach to estimate upper-limb impairment in stroke survivors using two wearable inertial sensors, on the wrist and the sternum, and a minimally-burdensome motor task. Twenty-three stroke survivors with no, mild, or moderate upper-limb impairment performed two repetitions of one-to-two minute-long continuous, random (i.e., patternless), voluntary upper-limb movements spanning the entire range of motion. The three-dimensional timeseries of upper-limb movements were segmented into a series of one-dimensional submovements by employing a unique movement decomposition technique. An unsupervised clustering algorithm and a supervised regression model were used to estimate Fugl-Meyer Assessment (FMA) scores based on features extracted from these submovements. Our regression model estimated FMA scores with a normalized root mean square error of 18.2% (r2= 0.70) and needed as little as one minute of movement data to yield reasonable estimation performance. These results support the possibility of frequently monitoring stroke survivors' rehabilitation outcomes, ultimately enabling the development of individually-tailored rehabilitation programs.
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Two included studies used the Wolf Motor Function Test.
“Two included studies used the WMFT [65,67,73].”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [73]
FMA variants are the most commonly used batteries of tests among the included works.
“FMA variants are the most commonly used batteries of tests from the included works [68,69,73]”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [73]
Two included works used the coefficient of determination R2.
“Two works [65,73] used the coefficient of determination, denoted R 2”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [73]
In study [73], the regression model achieved an R^2 value of 0.985.
“R 2 value of 0.985”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [73]
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Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review
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“Two works [65,73] used the coefficient of determination, denoted R2 which is a statistic that will give some information about the goodness of fit of a model in regression models.”
BackgroundMethodResultInfluentialFusion of Multi-Sensor-Based Biomechanical Gait Analysis Using Vision and Wearable Sensor
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BackgroundDecomposition of Reaching Movements Enables Detection and Measurement of Ataxia
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“This body of work includes methods to estimate Functional Ability Scale (FAS) scores derived during the performance of tasks of the Wolf Motor Function Test (WMFT) [30], Fugl-Meyer Assessment Upper Extremity (FMA-UE) scale scores [31]–[33] and metrics of arm use highly correlated with Box and Block Test scores [34].”
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“Legend: APB—Abductor pollicis brevis; ARAT—Action Research Arm Test; BBS—Berg Balance Scale; BP—backpropagation; CCT—Controlled clinical trial; CR—Case report; CS—Case series; EMG—electromyography; FAC—Functional ambulation category; FCR-FD—flexor carpi radialis-flexor digitorum; FMA—Fugl-Meyer Assessment; HOA—Hand op…”
MethodInfluentialRecognizing wearable upper-limb rehabilitation gestures by a hybrid multi-feature neural network
Wang, S., Liu, J., Chen, S., Wang, S., Peng, Y., Liao, C., & Liu, L. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107424 · Cited by 20 · see citing works
Quantitative Upper Limb Impairment Assessment for Stroke Rehabilitation: A Review
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“4(a) is one of the most widely used motion sensors with the advantages of small size, low energy consumption, high measurement accuracy, and good stability [46], [48].”
BackgroundLee, S. I., Liu, Y., Vergara-Diaz, G. P., Pugliese, B. L., Black‐Schaffer, R. M., Stoykov, M. E., & Bonato, P. (2024) · Neurorehabilitation and neural repair · doi:10.1177/15459683241270066 · Cited by 16 · see citing works
“11,45 The velocity profiles of these movement segments exhibit varied morphology and temporal patterns in stroke survivors 38 due to impaired motor planning and control (eg, feedfor-ward and feedback control).”
InfluentialOubre, B., & Lee, S. I. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3337156 · Cited by 15 · see citing works
“…movement could be present in as many as three overlapping windows, this step ensured that subsequent processing did not consider duplicates of inertial data. that kinematic and morphological features of submovements— called movement elements —are related to upper-limb motor impairment [13], [15].”
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Estimating Upper Extremity Fugl-Meyer Assessment Scores From Reaching Motions Using Wearable Sensors
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“2(a)) (sliding window for time-series data is commonly used for time series machine learning [49], and specifically sliding window approach has been used as past benchmarks [29]).”
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Upper limb movement profiles during spontaneous motion in acute stroke
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“) [21,26–32], as are camera-based systems [5,33–36], to automate the stroke assessment test by reducing barriers and the obligations of experts or physiotherapists [1].”
BackgroundMethodInfluentialDatta, 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
“Accelerometer sensors have been previously used to predict clinical scores in stroke patients using various time and frequency domain analysis methods [9]–[11].”
MethodHwang, Y., Lu, W.-A., & Lin, B.-S. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3162416 · Cited by 11 · see citing works
Bai, J., Li, G., Lu, X., & Wen, X. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1362495 · Cited by 10 · see citing works
Miao, S. J., Dang, Y., Zhu, Q., Li, S., Shorfuzzaman, M., & Lv, H. (2021) · IEEE Access · doi:10.1109/access.2021.3080592 · Cited by 10 · see citing works
“[54] had patients wear inertial sensor devices for”
BackgroundShen, C., Ning, X., Zhu, Q., Miao, S. J., & Lv, H. (2021) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2021.100624 · Cited by 10 · see citing works
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“, 2019), with kinematic variables obtained by IMU-based system correlating with standard clinical assessments (Hughes et al., 2019b; Oubre et al., 2020; Chen et al., 2021).”
MethodLiu, Y., Oubre, B., Duval, C., Lee, S. I., & Daneault, J. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3177396 · Cited by 9 · see citing works
Oubre, B., & Lee, S. I. (2022) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi56158.2022.9926918 · Cited by 8 · see citing works
“data collected from ADLs demonstrated slightly improved performance in comparison to our recent work using a similar methodology and data collected from large, continuous, random movements [11].”
BackgroundMethodResultInfluentialIMU-Based Monitoring for Assistive Disease Diagnosis and Management of IoHT: A ReviewPreprint
Bo, F., Yerebakan, M. O., Dai, Y., Wang, W., Li, J., Gao, S., & Boyi, H. (2022) · doi:10.36227/techrxiv.19200482 · Cited by 10 · see citing works
Lee, J. H., Lee, S. I., & Choe, E. K. (2024) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3699723 · Cited by 7 · see citing works
“Wrist-worn devices were common in this research [67, 85], but other devices such as ring devices worn on fingers [41, 49] were also explored, which provided more targeted data on hand function but with limitations in battery life and user comfort.”
Bionic Design of a Novel Portable Hand-Elbow Coordinate Exoskeleton for Activities of Daily Living
Meng, Q., Liu, G., Meng, Q., Xu, X. S., Qin, L., & Yu, H. (2023) · Electronics · doi:10.3390/electronics12153326 · Cited by 7 · see citing works
Lu, Y., Ding, K., Dai, Y., Yin, J., Yao, J., Guo, L., Wang, J., & Wang, X. (2025) · Healthcare · doi:10.3390/healthcare13070708 · Cited by 6 · see citing works
“Oubre et al. [15] estimated the severity of upper limb impairment using Fugl–Meyer Assessment (FMA) scores with two inertial measurement units (IMUs).”
Liu, M., Lu, H.-Y., Tong, S. F., Liang, D., Sun, H., Xing, T., Shi, X., Yu, H., & Tong, R. K. (2025) · Sensors · doi:10.3390/s25216663 · Cited by 6 · see citing works
“…balance between brevity and comprehensiveness: while studies using fewer or more generic actions (e.g., 1–2 min of routine motion or 3–4 ADLs [29]) achieve speed, our more representa-Sensors and targeted set yields significantly superior prediction performance without compromising temporal…”
InfluentialMuscle Activation Visualization System Using Adaptive Assessment and Forces-EMG Mapping
Wei, X., Chen, Y., Jia, X., Chen, Y., & Xie, L. (2021) · IEEE Access · doi:10.1109/access.2021.3067360 · Cited by 6 · see citing works
“In the latest study, Oubre and his colleagues tried to draw an unsupervised clustering algorithm and a supervised regression model into Fugl-Meyer Assessment scores based on movement features [15].”
MethodStructure, sensing and control of pneumatic upper limb rehabilitation system: Review and prospect
Zhang, Y., Zhang, S., Cai, M., Shi, Y. X., Yu, Q., Wei, F., Li, S., & Liu, C. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110714 · Cited by 5 · see citing works
Gutiérrez, R., McCrady, A., Masterson, C., Tolman, S., Boukhechba, M., Barnes, L. E., Blemker, S. S., & Scharf, R. J. (2022) · JMIR Research Protocols · doi:10.2196/40856 · Cited by 5 · see citing works
“Many of these works used cameras and multiple wireless sensors to track upper limb movement to report angles of motion [17,18].”
BackgroundSpatiotemporal Parameterization of Human Reaching Movements Based on Time Base Generator
Kittaka, M., Furui, A., Sakai, H., Morasso, P., & Tsuji, T. (2020) · IEEE Access · doi:10.1109/access.2020.3000273 · Cited by 5 · see citing works
“showed that the segmented velocity profile during random (patternless) voluntary upper-limb movements featured an approximately bell-shaped morphology [34].”
BackgroundCastellanos-Ruíz, J., Montealegre-Mesa, L. M., Martínez-Toro, B. D., Gallo-Serna, J. J., & Almanza-Fuentes, O. (2020) · Universidad y Salud · doi:10.22267/rus.212301.214 · Cited by 5 · see citing works
“específicamente en usuarios con ataxia cerebelosa para calcular la aceleración, el ritmo y el coeficiente de variación de la longitud del paso(34).”
BackgroundLiang, D., Tong, S. F., Lu, H.-Y., Liu, M., Wang, Z., Xing, T., Yu, H., & Tong, R. K. (2025) · Machines · doi:10.3390/machines13111028 · Cited by 4 · see citing works
Handedness and brain lateralization: A nonlinear motor approach combined with EEG
Ramos, Y. E., Santos, M. T., Yamamoto, I. N. R., Accioly, C. B. D. C., Daneault, J., Filho, D. A., & Miranda, J. G. V. (2025) · Human Movement Science · doi:10.1016/j.humov.2025.103425 · Cited by 4 · see citing works
Understanding voluntary human movement variability through data-driven segmentation and clustering
Daneault, J., Oubre, B., Miranda, J. G. V., & Lee, S. I. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1278653 · Cited by 4 · see citing works
Nam, H. S., Han, S., Leigh, J., & Bang, M. S. (2024) · Hong Kong Journal of Occupational Therapy · doi:10.1177/15691861241241775 · Cited by 4 · see citing works
“They usually estimated clinical functional evaluation scores, such as the Fugl-Meyer Assessment score (Fugl-Meyer et al., 1975) and WMFT, usually employing machine learning algorithms (Otten et al., 2015; Oubre et al., 2020).”
MethodRamos, Y. E., Torres, Â. F. S. D. O. E., Accioly, C. B. D. C., Matias, F. S., & Miranda, J. G. V. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.117412 · Cited by 3 · see citing works
Zhang, C., Zou, J., Ma, Z., Wu, Q., Sheng, Z., & Yan, Z. G. (2021) · Traitement du signal · doi:10.18280/ts.380633 · Cited by 3 · see citing works
“Brain trauma, stroke, and cerebral palsy are the main causes of upper limb dysfunction, which brings huge pain and burden to patients and their relatives [1-6].”
BackgroundIMU-Based Monitoring for Assistive Disease Diagnosis and Management of IoHT: A ReviewPreprint
Bo, F., Yerebakan, M. O., Dai, Y., Wang, W., Li, J., Gao, S., & Boyi, H. (2022) · doi:10.36227/techrxiv.19200482.v1 · Cited by 3 · see citing works
Wang, R. Z., Lang, C. E., Stoykov, M. E., Bonato, P., & Lee, S. I. (2025) · medRxiv · doi:10.1101/2025.01.13.25320461 · Cited by 3 · see citing works
“The homogeneous and outlier sets were identified using the unsupervised clustering 15 methods outlined in prior work [19].”
InfluentialEstimating 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 2 · see citing works
Xu, P., Zhou, J., Kausar, N., Lin, C., Lü, Q., Ghaderpour, E., Pamucar, D. S., & Zadeh, A. M. (2024) · Demonstratio Mathematica · doi:10.1515/dema-2023-0261 · Cited by 1 · see citing works
Liu, Y., Pugliese, B. L., Vergara-Diaz, G. P., O’Brien, A., Black‐Schaffer, R. M., Bonato, P., & Lee, S. I. (2025) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2025.3635677 · Cited by 1 · see citing works
“However, its e ff ectiveness in practice heavily depends on the accuracy of linear movement detection.”
InfluentialLi, L., Wang, J., Chen, J., Sun, S., & Peng, W. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1737407 · Cited by 1 · see citing works
Global trends in wearable sensors for stroke motor rehabilitation: A bibliometric analysis
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RetractedA Novel Design for an Upper-Limb Rehabilitation Assisting Device
Amador, L. D. F., Castillo-Castañeda, E., & Carbone, G. (2022) · Mechanisms and machine science · doi:10.1007/978-3-031-10776-4_59 · Cited by 1 · see citing works
Lee, J.-H. (2021) · International Journal of Science and Research Archive · doi:10.30574/ijsra.2021.3.1.0109
“Introduction In daily life, the upper limbs frequently perform complicated motions, such as functional reaching out, grasping, and lifting [1].”
BackgroundResearch on Estimation Method of Lower Limb Muscle Strength Based on Surface ElectromyographyPreprint
Jie, S., & Tang, G. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.3999279
Jerk Optimization Model of Motor Control for Continuous Movement - A Genetic Algorithm ApproachPreprint
Silva, M. S., de Araújo, J. R. P., Daneault, J., & Miranda, J. G. V. (2022) · Research Square · doi:10.21203/rs.3.rs-1677071/v1
Barnes, L. E., Gutiérrez, R., McCrady, A., Tolman, S., Boukhechba, M., Blemker, S. S., Scharf, R. J., & Masterson, C. (2022) · doi:10.2196/preprints.40856
Evaluating the Responsiveness of Wearable-Based Motor Assessment for Stroke Upper-Limb Impairments
Lee, J., Dombrow, B., Stoykov, M. E., & Lee, S. I. (2024) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn63547.2024.10780580
A Serious Game with Leap Motion for Hand Motor Function Assessment
Shan, X., Liu, T., & Liu, Z. (2025) · International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC) · doi:10.1145/3745533.3745622
“…Oubre et al. used unsupervised learning to extract features during three upper limb movement patterns using only two wearable inertial sensors on the wrist and sternum, which were used to estimate FMA scores of upper limb motor deficits in patients after constructing a regression model [20].”
Savchits, D. O., Prokopenko, S. V., & Subocheva, S. A. (2025) · Physical and rehabilitation medicine medical rehabilitation · doi:10.36425/rehab689262
Submovement Characteristics are Related to the Presence of Parkinson’s Disease
Liu, Y., Oubre, B., Duval, C., Lee, S. I., & Daneault, J. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11254010
“Prior work found that features of the dense, homogeneous set of movement elements with consistent morphologies and the complementary outlier set are both related to upper-limb impairment in stroke survivors [8].”
InfluentialZhang, X., Liang, J., Chen, M., Xu, R., & Meng, L. (2025) · Journal of intelligent medicine. · doi:10.1002/jim4.70013
“45 Previous studies, such as that by Brandon Oubre et al., 13 developed an assessment paradigm using 1 minute motion data, but their method was restricted to patients with mild ‐ to ‐ moderate impairments capable of full ‐ range active movements.”
Silva, M. S., Santos, D. D. S., Trigo, C. F., Vieira, J. P. B. C., de Castro, M. R., Santos, G. V., d’Alencar, M. S., de Araújo, J. R. P., Liu, Y., Lee, S. I., et al. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110099
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Islam, A., Rahman, A., Salgado, A. A. V., Scharf, R. J., Blemker, S. S., & Barnes, L. E. (2026) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi69079.2026.00045
“…reduces dependence on a fixed activity set and supports robust evaluation even when patients complete only a subset of tasks — a critical advantage in pediatric neuromuscular populations, where fatigue, limited attention span, and physical constraints often preclude extensive task batteries [12].”
Rahman, A., Islam, A., McCrady, A., Salgado, A. A. V., Blemker, S. S., Scharf, R. J., & Barnes, L. E. (2026) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi69079.2026.00044
A Flexible Hand Exoskeleton Robot With Finger Joint Angle and Torque Sensing Capabilities
Su, C., Li, G., Liang, X., Wang, W., & Xiong, P. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3725262
“On the one hand, insu ffi cient sensing capabilities make it di ffi cult to obtain accurate rehabilitation assessments, which in turn a ff ect the implementation of personalized rehabilitation strategies [16], [17], [18].”
Wang, R., Lang, C. E., Stoykov, M. E., Bonato, P., & Lee, S. I. (2026) · Science Translational Medicine · doi:10.1126/scitranslmed.adw3644
Low-Cost AI-Based Wearable Rehabilitation Monitoring System for Upper-Limb Stroke Patients
Anisha, N., & Kumar, U. S. (2026) · Journal of Electronics and Informatics · doi:10.36548/jei.2026.4.006
“Oubre et al. [1] proposed a wearable sensor-based system, which uses inertial sensors attached to the wrist and sternum region to quantify upper limb impairment ISSN: 2582-3825 based on voluntary movement.”
Arumugam, K., Sasidharan, S., Prabhakaran, P., Kavya, M., Thiyam, D. B., & Shelishiyah, R. (2026) · Acupuncture & Electro-Therapeutics Research · doi:10.1177/03601293261484314
Zhu, H., & Wang, H. (2026) · International Conference on Big Data and Intelligent Medicine (BDIMed) · doi:10.1145/3816325.3843661
“(2020)[14] Wrist and sternum IMUs; 1-2 min of randomupper-limbmovement FMA-UEtotal-scoreestimation Submovements were segmented and unsupervised clustering was used to characterize movement patterns, followed by supervised regression to estimate FMA.”
- Authors
- Oubre, B., Daneault, J.-F., Jung, H.-T., Whritenour, K., Miranda, J. G. V., Park, J., Ryu, T., Kim, Y., & Lee, S. I.
- Affiliations
- University of Massachusetts Amherst (US)
- ORCID
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2020-03
- Volume
- 28
- Issue
- 3
- Pages
- 601-611
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W2999027068
- Cited by
- 78
- References
- 60
- Citations by year
- 2026: 11 · 2025: 14 · 2024: 11 · 2023: 8 · 2022: 15 · 2021: 14 · 2020: 5
- FWCI
- 5.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Division of Information and Intelligent Systems (1755687)
- Topics
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Keywords
- stroke, upper limb paresis, wearable inertial sensors, Fugl-Meyer Assessment, movement decomposition, unsupervised clustering
- MeSH terms
- Stroke Rehabilitation, Wearable Electronic Devices, Humans, Paresis, Survivors, Recovery of Function, Stroke, Upper Extremity
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