ArticleScientific reports2024
Ηand dexterities assessment in stroke patients based on augmented reality and machine learning through a box and block test.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Associations Between Hip Mobility and Pain in Chronic Low Back Pain Using IMU and Markerless Motion Capture.Sensors (Basel, Switzerland) · 2026Article
- Computer Vision for Movement Observation and Recovery Enhancement (C-MORE): Box and Blocks Test.Bioengineering (Basel, Switzerland) · 2026Article
- Feasibility and User Experience of Immersive Virtual Reality-Based Rehabilitation in Patients With Stroke: Single-Arm Pretest-Posttest Pilot Study.JMIR serious games · 2026Article
- Exploring New Tools in Upper Limb Rehabilitation After Stroke Using an Exoskeletal Aid: A Pilot Randomized Control Study.Healthcare (Basel, Switzerland) · 2025Article
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Authors and funding
20 authors.
Funding
Abstract
A popular and widely suggested measure for assessing unilateral hand motor skills in stroke patients is the box and block test (BBT). Our study aimed to create an augmented reality enhanced version of the BBT (AR-BBT) and evaluate its correlation to the original BBT for stroke patients. Following G-power analysis, clinical examination, and inclusion-exclusion criteria, 31 stroke patients were included in this study. AR-BBT was developed using the Open Source Computer Vision Library (OpenCV). The MediaPipe's hand tracking library uses a palm and a hand landmark machine learning model to detect and track hands. A computer and a depth camera were employed in the clinical evaluation of AR-BBT following the principles of traditional BBT. A strong correlation was achieved between the number of blocks moved in the BBT and the AR-BBT on the hemiplegic side (Pearson correlation = 0.918) and a positive statistically significant correlation (p = 0.000008). The conventional BBT is currently the preferred assessment method. However, our approach offers an advantage, as it suggests that an AR-BBT solution could remotely monitor the assessment of a home-based rehabilitation program and provide additional hand kinematic information for hand dexterities in AR environment conditions. Furthermore, it employs minimal hardware equipment.
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Registered trials
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