ArmSleeve: A Patient Monitoring System to Support Occupational Therapists in Stroke Rehabilitation
Abstract
This paper describes the design of "ArmSleeve", a patient monitoring system to support occupational therapists in their upper limb rehabilitation work with stroke patients. Occupational therapists can provide rehabilitation in clinics, but they have limited insights into how much their patients use their affected arm and hand in daily life, which is critical for effective recovery to occur. Our work addresses this problem through three interrelated studies: (1) interviews with therapists to examine their current rehabilitation practices; (2) the design of the "ArmSleeve Sensor" to monitor a patient's upper limb movement; and (3) the design and evaluation of the "ArmSleeve Dashboard" to visualize this information for therapists. The findings show the importance of collecting objective data to assess exercise and activities outside therapy, but also a lack of contextual information to interpret this data. We discuss considerations for how to address this issue through patient engagement as well as considerations for designing wearable sensor technology that is usable in everyday life.
Integrity signals
Registry
Cites
Statistics
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?
Several technological and visualization tools were developed to monitor stroke recovery progress, particularly monitoring the patient's upper limb movement during stroke recovery.
“Several technological and visualization tools were developed to monitor stroke recovery progress, particularly monitoring the patient's upper limb movement during stroke recovery [28, 46, 47].”
From A Data Visualization Tool for Patients and Healthcare Providers to Communicate during Inpatient Stroke Rehabilitation (2024) · cites this paper as [47]
Existing stroke recovery monitoring systems were designed to be used by healthcare providers or caregivers, not by both healthcare providers and patients for communication during inpatient stroke rehabilitation.
“Furthermore, these systems were designed to be used by healthcare providers [46, 47] or caregivers [28] for monitoring patient's recovery progress. They were not developed to be used by healthcare providers and patients for communication during inpatient stroke rehabilitation.”
From A Data Visualization Tool for Patients and Healthcare Providers to Communicate during Inpatient Stroke Rehabilitation (2024) · cites this paper as [47]
Cites
Using thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414 · see citing works
Interventions for improving upper limb function after stroke
Pollock, A., Farmer, S. E., Brady, M. C., Langhorne, P., Mead, G. E., Mehrholz, J., & van Wijck, F. (2014) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd010820.pub2 · Cited by 909 · see citing works
Understanding quantified-selfers' practices in collecting and exploring personal data
Choe, E. K., Lee, N. B., Lee, B., Pratt, W., & Kientz, J. A. (2014) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557372 · Cited by 766 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Human Digital Twin-based interactive dashboards for informal caregivers of stroke patients
Lauer-Schmaltz, M. W., Kerim, I., Hansen, J. P., Gulyás, G. M., & Andersen, H. B. (2023) · Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments · doi:10.1145/3594806.3594824 · Cited by 18 · see citing works
Ramesh, S. H., Ouskine, A., Khorasani, E., Ebrahimipour, M., Finestone, H., Chan, A. D. C., & Rajabiyazdi, F. (2024) · Graphics Interface · doi:10.1145/3670947.3670978 · Cited by 7 · see citing works
- Authors
- Ploderer, B., Fong, J., Withana, A., Klaic, M., Nair, S., Crocher, V., Vetere, F., & Nanayakkara, S.
- Affiliations
- Queensland University of Technology (AU)
- The University of Melbourne (AU)
- Singapore University of Technology and Design (SG)
- The Royal Melbourne Hospital (AU)
- ORCID
- Venue
- Proceedings of the 2016 ACM Conference on Designing Interactive Systems
- Publisher
- ACM
- Published
- 2016-06-04
- Pages
- 700-711
- OpenAlex
- W2419612215
- Cited by
- 46
- References
- 3
- Citations by year
- 2025: 1 · 2024: 4 · 2023: 5 · 2022: 6 · 2021: 4 · 2020: 4 · 2019: 6 · 2018: 6 · 2017: 6 · 2016: 4
- FWCI
- 17.85
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Designing Interactive Systems Conference (publishedVersion)
- 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 › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- stroke rehabilitation, upper limb rehabilitation, occupational therapy, patient monitoring, wearable sensors, data visualization, patient engagement
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
