ArticleNeurorehabilitation and neural repair2020
Improving Accelerometry-Based Measurement of Functional Use of the Upper Extremity After Stroke: Machine Learning Versus Counts Threshold Method.
Article in Neurorehabilitation and neural repair, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05371847 (Identifying Prognostic Variables for Persistent Upper Limb Dysfunctions After Breast Cancer Treatment), which is not on this map. Cited by 30 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Identifying Prognostic Variables for Persistent Upper Limb Dysfunctions After Breast Cancer Treatment: Longitudinal Cohort Study
Who cites it
30 citing papers in PubMed.
- Changes in Unimanual and Bimanual Upper Extremity Use During the Subacute Phase Post-Stroke Assessed in Supervised and Unsupervised Contexts.Neurorehabilitation and neural repair · 2026Article
- From Metrics to Meaning in Neurological Rehabilitation: Clinicians' Perspectives on Digital Metrics of Upper Limb Functioning-A Focus Group Study.JMIR rehabilitation and assistive technologies · 2026Article
- Challenges in Clinical Research for Nerve Injuries (Nerve SPACE 2025).Journal of hand surgery global online · 2026Article
- Lower limb prosthetic users' activity classification through real-world inertial sensing.PloS one · 2026Article
- Toward Sensor-to-Text Generation: Leveraging LLM-Based Video Annotations for Stroke Therapy Monitoring.Bioengineering (Basel, Switzerland) · 2025Article
- Accelerometry and the Capacity-Performance Gap: Case Series Report in Upper-Extremity Motor Impairment Assessment Post-Stroke.Bioengineering (Basel, Switzerland) · 2025Article
- Beyond Arm Capacity in Chronic Stroke: Evaluating Paretic Arm Non-Use Through Arm Efficiency-A Cross-Sectional Study.Neurorehabilitation and neural repair · 2025Article
- Factors associated with upper extremity use after stroke: a scoping review of accelerometry studies.Journal of neuroengineering and rehabilitation · 2025Article
- Enhancing rehabilitation in stroke survivors: a deep learning approach to access upper extremity movement using accelerometry data.Frontiers in artificial intelligence · 2025Article
- Closed-loop versus open-loop "remind-to-move" treatment using wearables for hemiparetic upper extremity in patients after stroke: A proof-of-concept study.Wearable technologies · 2025Article
- Enhancing Activity Recognition After Stroke: Generative Adversarial Networks for Kinematic Data Augmentation.Sensors (Basel, Switzerland) · 2024Article
- A data-driven approach to detect upper limb functional use during daily life in breast cancer survivors using wrist-worn sensors.Scientific reports · 2024Article
- Article
- Assessing Upper Limb Function in Breast Cancer Survivors Using Wearable Sensors and Machine Learning in a Free-Living Environment.Sensors (Basel, Switzerland) · 2023Article
- Quantitative Assessment of Upper Limb Movement in Post-Stroke Adults for Identification of Sensitive Measures in Reaching and Lifting Activities.Journal of clinical medicine · 2023Article
- Comparison of wrist actimetry variables of paretic upper limb use in post stroke patients for ecological monitoring.Journal of neuroengineering and rehabilitation · 2023Article
- Measurement of Functional Use in Upper Extremity Prosthetic Devices Using Wearable Sensors and Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- Concurrent validity of machine learning-classified functional upper extremity use from accelerometry in chronic stroke.Frontiers in physiology · 2023Article
- Classification Models of Action Research Arm Test Activities in Post-Stroke Patients Based on Human Hand Motion.Sensors (Basel, Switzerland) · 2022Article
- The use of wearable sensors to assess and treat the upper extremity after stroke: a scoping review.Disability and rehabilitation · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundWrist-worn accelerometry provides objective monitoring of upper-extremity functional use, such as reaching tasks, but also detects nonfunctional movements, leading to ambiguity in monitoring results.
objectiveCompare machine learning algorithms with standard methods (counts ratio) to improve accuracy in detecting functional activity.
methodsHealthy controls and individuals with stroke performed unstructured tasks in a simulated community environment (Test duration = 26 ± 8 minutes) while accelerometry and video were synchronously recorded. Human annotators scored each frame of the video as being functional or nonfunctional activity, providing ground truth. Several machine learning algorithms were developed to separate functional from nonfunctional activity in the accelerometer data. We also calculated the counts ratio, which uses a thresholding scheme to calculate the duration of activity in the paretic limb normalized by the less-affected limb.
resultsThe counts ratio was not significantly correlated with ground truth and had large errors (
conclusionsIn our sample, the counts ratio did not accurately reflect functional activity. Machine learning algorithms were more accurate, and future work should focus on the development of a clinical tool.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.