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.
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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]
Citations
Cites
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The random subspace method for constructing decision forests
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“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×
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MethodWOLF, S., LeCraw, D. E., BARTON, L., & Jann, B. B. (1989) · Experimental Neurology · doi:10.1016/s0014-4886(89)80005-6 · Cited by 892
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A review of wearable sensors and systems with application in rehabilitation
Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-9-21 · Cited by 2,396
Self-citationActivity Recognition Using a Single Accelerometer Placed at the Wrist or Ankle
MANNINI, A., INTILLE, S. S., ROSENBERGER, M., SABATINI, A. M., & HASKELL, W. (2013) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e31829736d6 · Cited by 383
“Activity counts summarize data in an epoch, simplifying data management, analysis, and interpretation; however, information about the structure of the raw accelerometer signal is lost, which might be used by algorithms to infer specific activity type (3,17), gait parameters (gait phase detection, walking speed estimat…”
MethodThe need to approximate the use-case in clinical machine learning
Saeb, S., Lonini, L., Jayaraman, A., Mohr, D. C., & Kording, K. P. (2017) · GigaScience · doi:10.1093/gigascience/gix019 · Cited by 367
Recent Advances in Wearable Sensors for Health Monitoring
Rodgers, M. M., Pai, V. M., & Conroy, R. S. (2015) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2357257 · Cited by 325
“[64] used accelerometers placed on the arm to derive accurate”
MethodMaceira-Elvira, P., Popa, T., Schmid, A., & Hummel, F. C. (2019) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-019-0612-y · Cited by 291
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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