ArticleScientific reports2021
Head motion classification using thread-based sensor and machine learning algorithm.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Flexible Resistive Sensors for Wearable and Ergonomics Applications: A Systematic Review.Sensors (Basel, Switzerland) · 2026Pooled it
- Assessing Low Back Movement with Motion Tape Sensor Data Through Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- Machine learning analysis of kinematic movement features during functional tasks to discriminate chronic neck pain patients from asymptomatic controls.Scientific reports · 2025Article
- Automated Fabrication of Smart Strain Sensing Threads.Micromachines · 2024Article
- Toward an AI Era: Advances in Electronic Skins.Chemical reviews · 2024Review
- AI-Driven Sensing Technology: Review.Sensors (Basel, Switzerland) · 2024Review
- Machine Learning-Enhanced Flexible Mechanical Sensing.Nano-micro letters · 2023Review
- Head Pitch Angular Velocity Discriminates (Sub-)Acute Neck Pain Patients and Controls Assessed with the DidRen Laser Test.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human machine interfaces that can track head motion will result in advances in physical rehabilitation, improved augmented reality/virtual reality systems, and aid in the study of human behavior. This paper presents a head position monitoring and classification system using thin flexible strain sensing threads placed on the neck of an individual. A wireless circuit module consisting of impedance readout circuitry and a Bluetooth module records and transmits strain information to a computer. A data processing algorithm for motion recognition provides near real-time quantification of head position. Incoming data is filtered, normalized and divided into data segments. A set of features is extracted from each data segment and employed as input to nine classifiers including Support Vector Machine, Naive Bayes and KNN for position prediction. A testing accuracy of around 92% was achieved for a set of nine head orientations. Results indicate that this human machine interface platform is accurate, flexible, easy to use, and cost effective.
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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.