ArticleSensors (Basel, Switzerland)2017
Motor Function Evaluation of Hemiplegic Upper-Extremities Using Data Fusion from Wearable Inertial and Surface EMG Sensors.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 4 of them syntheses that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Tracking Upper Limb Motion via Wearable Solutions: Systematic Review of Research From 2011 to 2023.Journal of medical Internet research · 2024Pooled it
- Effects of wearable device training on upper limb motor function in patients with stroke: a systematic review and meta-analysis.The Journal of international medical research · 2024Pooled it
- Wearable systems for shoulder kinematics assessment: a systematic review.BMC musculoskeletal disorders · 2019Pooled it
- From Qualitative to Quantitative Functional Assessment in Stroke Rehabilitation with a Focus on Ultrasound Role.Biomedicines · 2025Review
- Differentiating post-stroke patients from healthy individuals via vision-based skeleton-optical fusion.Journal of neuroengineering and rehabilitation · 2025Article
- Insights into motor impairment assessment using myographic signals with artificial intelligence: a scoping review.Biomedical engineering letters · 2025Review
- An Evaluation Model for Brain Ischemia Protection in Mice by Low-Intensity Pulsed Ultrasound Stimulation Based on Functional Cortico-Muscular Coupling.Bioengineering (Basel, Switzerland) · 2025Article
- Intelligent Assessment Techniques for Abnormal Movement Patterns in Neurological Disorders: Applications and Advances.Behavioural neurology · 2025Review
- Quantitative musculoskeletal monitoring and analysis in aquatic rehabilitation.Frontiers in electronics · 2025Article
- Role of joint interactions in upper limb joint movements: a disability simulation study using wearable inertial sensors for 3D motion capture.Journal of neuroengineering and rehabilitation · 2024Article
- Data-Driven Quantitation of Movement Abnormality after Stroke.Bioengineering (Basel, Switzerland) · 2023Article
- Association of Piriformis Thickness, Hip Muscle Strength, and Low Back Pain Patients with and without Piriformis Syndrome in Malaysia.Life (Basel, Switzerland) · 2023Article
- Wearable Intelligent Machine Learning Rehabilitation Assessment for Stroke Patients Compared with Clinician Assessment.Journal of clinical medicine · 2022Article
- A Novel Automated RGB-D Sensor-Based Measurement of Voluntary Items of the Fugl-Meyer Assessment for Upper Extremity: A Feasibility Study.Brain sciences · 2022Article
- [Research progress on intelligent assessment system for upper limb function of stroke patients].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Review
- Evaluation of surgical skill using machine learning with optimal wearable sensor locations.PloS one · 2022Article
- Does the Score on the MRC Strength Scale Reflect Instrumented Measures of Maximal Torque and Muscle Activity in Post-Stroke Survivors?Sensors (Basel, Switzerland) · 2021Article
- Proof-of-Concept of a Sensor-Based Evaluation Method for Better Sensitivity of Upper-Extremity Motor Function Assessment.Sensors (Basel, Switzerland) · 2021Article
- Effective recognition of human lower limb jump locomotion phases based on multi-sensor information fusion and machine learning.Medical & biological engineering & computing · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantitative evaluation of motor function is of great demand for monitoring clinical outcome of applied interventions and further guiding the establishment of therapeutic protocol. This study proposes a novel framework for evaluating upper limb motor function based on data fusion from inertial measurement units (IMUs) and surface electromyography (EMG) sensors. With wearable sensors worn on the tested upper limbs, subjects were asked to perform eleven straightforward, specifically designed canonical upper-limb functional tasks. A series of machine learning algorithms were applied to the recorded motion data to produce evaluation indicators, which is able to reflect the level of upper-limb motor function abnormality. Sixteen healthy subjects and eighteen stroke subjects with substantial hemiparesis were recruited in the experiment. The combined IMU and EMG data yielded superior performance over the IMU data alone and the EMG data alone, in terms of decreased normal data variation rate (NDVR) and improved determination coefficient (DC) from a regression analysis between the derived indicator and routine clinical assessment score. Three common unsupervised learning algorithms achieved comparable performance with NDVR around 10% and strong DC around 0.85. By contrast, the use of a supervised algorithm was able to dramatically decrease the NDVR to 6.55%. With the proposed framework, all the produced indicators demonstrated high agreement with the routine clinical assessment scale, indicating their capability of assessing upper-limb motor functions. This study offers a feasible solution to motor function assessment in an objective and quantitative manner, especially suitable for home and community use.
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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.