Attachable Inertial Device with Machine Learning toward Head Posture Monitoring in Attention Assessment
The monitoring of head posture is crucial for interactive learning, in order to build feedback with learners’ attention, especially in the explosion of digital teaching that occurred during the current COVID-19 pandemic. However, conventional monitoring based on computer vision remains a great chall...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Micromachines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-666X/13/12/2212 |
| _version_ | 1827637673673621504 |
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| author | Ying Peng Chao He Hongcheng Xu |
| author_facet | Ying Peng Chao He Hongcheng Xu |
| author_sort | Ying Peng |
| collection | DOAJ |
| description | The monitoring of head posture is crucial for interactive learning, in order to build feedback with learners’ attention, especially in the explosion of digital teaching that occurred during the current COVID-19 pandemic. However, conventional monitoring based on computer vision remains a great challenge in the multi-freedom estimation of head posture, owing to low-angle annotation and limited training accuracy. Here, we report a fully integrated attachable inertial device (AID) that comfortably monitors in situ head posture at the neck, and provides a machine learning-based assessment of attention. The device consists of a stretchable inertial sensing unit and a fully integrated circuit-based system, as well as mechanically compliant encapsulation. Due to the mechanical flexibility, the device can be seamlessly attach to a human neck’s epidermis without frequent user interactions, and wirelessly supports six-axial inertial measurements, thereby obtaining multidimensional tracking of individual posture. These head postures (40 types) are then divided into 10 rotation actions which correspond to diverse situations that usually occur in daily activities of teaching. Benefiting from a 2D convolutional neural network (CNN)-based machine learning model, their classification and prediction of head postures can be used to analyze and infer attention behavior. The results show that the proposed 2D CNN-based machine learning method can effectively distinguish the head motion posture, with a high accuracy of 98.00%, and three actual postures were successfully verified and evaluated in a predefined attention model. The inertial monitoring and attention evaluation based on attachable devices and machine learning will have potential in terms of learning feedback and planning for learners. |
| first_indexed | 2024-03-09T16:05:07Z |
| format | Article |
| id | doaj.art-61b95191c5da43dbba7db8b908dc4cec |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-09T16:05:07Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-61b95191c5da43dbba7db8b908dc4cec2023-11-24T16:46:05ZengMDPI AGMicromachines2072-666X2022-12-011312221210.3390/mi13122212Attachable Inertial Device with Machine Learning toward Head Posture Monitoring in Attention AssessmentYing Peng0Chao He1Hongcheng Xu2Normal College of Liupanshui, Liupanshui 553000, ChinaNormal College of Liupanshui, Liupanshui 553000, ChinaSchool of Mechano-Electronic Engineering, Xidian University, Xi’an 710071, ChinaThe monitoring of head posture is crucial for interactive learning, in order to build feedback with learners’ attention, especially in the explosion of digital teaching that occurred during the current COVID-19 pandemic. However, conventional monitoring based on computer vision remains a great challenge in the multi-freedom estimation of head posture, owing to low-angle annotation and limited training accuracy. Here, we report a fully integrated attachable inertial device (AID) that comfortably monitors in situ head posture at the neck, and provides a machine learning-based assessment of attention. The device consists of a stretchable inertial sensing unit and a fully integrated circuit-based system, as well as mechanically compliant encapsulation. Due to the mechanical flexibility, the device can be seamlessly attach to a human neck’s epidermis without frequent user interactions, and wirelessly supports six-axial inertial measurements, thereby obtaining multidimensional tracking of individual posture. These head postures (40 types) are then divided into 10 rotation actions which correspond to diverse situations that usually occur in daily activities of teaching. Benefiting from a 2D convolutional neural network (CNN)-based machine learning model, their classification and prediction of head postures can be used to analyze and infer attention behavior. The results show that the proposed 2D CNN-based machine learning method can effectively distinguish the head motion posture, with a high accuracy of 98.00%, and three actual postures were successfully verified and evaluated in a predefined attention model. The inertial monitoring and attention evaluation based on attachable devices and machine learning will have potential in terms of learning feedback and planning for learners.https://www.mdpi.com/2072-666X/13/12/2212attachable devicehead postureattentionmachine learninginertial measurement |
| spellingShingle | Ying Peng Chao He Hongcheng Xu Attachable Inertial Device with Machine Learning toward Head Posture Monitoring in Attention Assessment Micromachines attachable device head posture attention machine learning inertial measurement |
| title | Attachable Inertial Device with Machine Learning toward Head Posture Monitoring in Attention Assessment |
| title_full | Attachable Inertial Device with Machine Learning toward Head Posture Monitoring in Attention Assessment |
| title_fullStr | Attachable Inertial Device with Machine Learning toward Head Posture Monitoring in Attention Assessment |
| title_full_unstemmed | Attachable Inertial Device with Machine Learning toward Head Posture Monitoring in Attention Assessment |
| title_short | Attachable Inertial Device with Machine Learning toward Head Posture Monitoring in Attention Assessment |
| title_sort | attachable inertial device with machine learning toward head posture monitoring in attention assessment |
| topic | attachable device head posture attention machine learning inertial measurement |
| url | https://www.mdpi.com/2072-666X/13/12/2212 |
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