Head motion classification using thread-based sensor and machine learning algorithm

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 sens...

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Bibliographic Details
Published in:Scientific reports 2021-01, Vol.11 (1), p.2646-2646, Article 2646
Main Authors: Jiang, Yiwen, Sadeqi, Aydin, Miller, Eric L., Sonkusale, Sameer
Format: Article
Language:English
Subjects:
639/166/987
639/705/117
Algorithms
Bayesian analysis
Carbon
Classification
Computer applications
Data collection
Data processing
Educational technology
Head
Humanities and Social Sciences
Interfaces
Learning algorithms
Machine learning
Methods
multidisciplinary
Range of motion
Rehabilitation
Science
Science (multidisciplinary)
Sensors
Usability
Virtual reality
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-81284-7