Detection and Classification of Teacher-Rated Children’s Activity Levels Using Millimeter-Wave Radar and Machine Learning: A Pilot Study in a Real Primary School Environment
Traditional assessments of children’s health and behavioral issues primarily rely on subjective evaluation by adult raters, which imposes major costs in time and human resource to the school system. This pilot study investigates the utilization of millimeter-wave radar coupled with machin...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10833603/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1825207010124103680 |
|---|---|
| author | Tianyi Wang Takuya Sakamoto Yu Oshima Itsuki Iwata Masaya Kato Haruto Kobayashi Manabu Wakuta Masako Myowa Tokomo Nishimura Atsushi Senju |
| author_facet | Tianyi Wang Takuya Sakamoto Yu Oshima Itsuki Iwata Masaya Kato Haruto Kobayashi Manabu Wakuta Masako Myowa Tokomo Nishimura Atsushi Senju |
| author_sort | Tianyi Wang |
| collection | DOAJ |
| description | Traditional assessments of children’s health and behavioral issues primarily rely on subjective evaluation by adult raters, which imposes major costs in time and human resource to the school system. This pilot study investigates the utilization of millimeter-wave radar coupled with machine learning for the objective and semi-automatic detection and classification of children’s activity levels, defined as restlessness, within a real classroom environment. Two objectives are pursued: confirming the feasibility of restlessness detection using millimeter-wave radar and applying standard machine learning method for restlessness classification. The experiment involves a nine-day observational study, using two radar systems to monitor the activities of 14 children in a primary school. Radar data analysis involves the extraction of distinctive features for restlessness detection and classification. Results indicate the successful detection of restlessness using millimeter-wave radar, demonstrating its potential to capture nuanced body movements in a privacy-protected manner. Machine learning models trained on radar data achieve a classification accuracy of 100%, outperforming other methods in terms of non-invasiveness, lack of body restraint, multi-target applications, and privacy protection. The study’s contributions extend to children, parents, and educational practitioners, emphasizing non-invasiveness, privacy protection, and evidence-based support. Despite limitations such as a short monitoring duration and a small sample size, this pilot study lays the foundation for future research in non-invasive restlessness detection using non-contact monitoring technologies. The integration of millimeter-wave radar and machine learning offers a promising avenue for efficient and ethical trait assessments in real-world educational environments, contributing to the advancement of child psychology and education. This work supports efforts for non-contact monitoring of children’s activity holding promise such as non-invasive, privacy protection, multi-targets, objective evaluation, and computer-aided screening. |
| format | Article |
| id | doaj-art-38458e4d561a4f92957d45012a439589 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-38458e4d561a4f92957d45012a4395892025-02-07T00:01:11ZengIEEEIEEE Access2169-35362025-01-0113231562317010.1109/ACCESS.2025.352703710833603Detection and Classification of Teacher-Rated Children’s Activity Levels Using Millimeter-Wave Radar and Machine Learning: A Pilot Study in a Real Primary School EnvironmentTianyi Wang0https://orcid.org/0000-0001-8066-6408Takuya Sakamoto1https://orcid.org/0000-0003-0177-879XYu Oshima2Itsuki Iwata3https://orcid.org/0009-0000-4307-2344Masaya Kato4Haruto Kobayashi5https://orcid.org/0009-0004-0327-6077Manabu Wakuta6https://orcid.org/0009-0004-8096-8093Masako Myowa7https://orcid.org/0000-0001-6080-106XTokomo Nishimura8https://orcid.org/0000-0002-4776-5153Atsushi Senju9https://orcid.org/0000-0002-8081-7170Institute for Multidisciplinary Sciences, Yokohama National University, Yokohama, Kanagawa, JapanDepartment of Electrical Engineering, Graduate School of Engineering, Kyoto University, Kyoto, JapanDepartment of Electrical Engineering, Graduate School of Engineering, Kyoto University, Kyoto, JapanDepartment of Electrical Engineering, Graduate School of Engineering, Kyoto University, Kyoto, JapanDepartment of Electrical Engineering, Graduate School of Engineering, Kyoto University, Kyoto, JapanDepartment of Electrical Engineering, Graduate School of Engineering, Kyoto University, Kyoto, JapanResearch Department, Institute of Child Developmental Science Research, Hamamatsu, Shizuoka, JapanGraduate School of Education, Kyoto University, Kyoto, JapanResearch Center for Child Mental Development, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, JapanResearch Center for Child Mental Development, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, JapanTraditional assessments of children’s health and behavioral issues primarily rely on subjective evaluation by adult raters, which imposes major costs in time and human resource to the school system. This pilot study investigates the utilization of millimeter-wave radar coupled with machine learning for the objective and semi-automatic detection and classification of children’s activity levels, defined as restlessness, within a real classroom environment. Two objectives are pursued: confirming the feasibility of restlessness detection using millimeter-wave radar and applying standard machine learning method for restlessness classification. The experiment involves a nine-day observational study, using two radar systems to monitor the activities of 14 children in a primary school. Radar data analysis involves the extraction of distinctive features for restlessness detection and classification. Results indicate the successful detection of restlessness using millimeter-wave radar, demonstrating its potential to capture nuanced body movements in a privacy-protected manner. Machine learning models trained on radar data achieve a classification accuracy of 100%, outperforming other methods in terms of non-invasiveness, lack of body restraint, multi-target applications, and privacy protection. The study’s contributions extend to children, parents, and educational practitioners, emphasizing non-invasiveness, privacy protection, and evidence-based support. Despite limitations such as a short monitoring duration and a small sample size, this pilot study lays the foundation for future research in non-invasive restlessness detection using non-contact monitoring technologies. The integration of millimeter-wave radar and machine learning offers a promising avenue for efficient and ethical trait assessments in real-world educational environments, contributing to the advancement of child psychology and education. This work supports efforts for non-contact monitoring of children’s activity holding promise such as non-invasive, privacy protection, multi-targets, objective evaluation, and computer-aided screening.https://ieeexplore.ieee.org/document/10833603/Machine learningmillimeter-wave radarnon-contact monitoringreal school environmentrestlessness |
| spellingShingle | Tianyi Wang Takuya Sakamoto Yu Oshima Itsuki Iwata Masaya Kato Haruto Kobayashi Manabu Wakuta Masako Myowa Tokomo Nishimura Atsushi Senju Detection and Classification of Teacher-Rated Children’s Activity Levels Using Millimeter-Wave Radar and Machine Learning: A Pilot Study in a Real Primary School Environment IEEE Access Machine learning millimeter-wave radar non-contact monitoring real school environment restlessness |
| title | Detection and Classification of Teacher-Rated Children’s Activity Levels Using Millimeter-Wave Radar and Machine Learning: A Pilot Study in a Real Primary School Environment |
| title_full | Detection and Classification of Teacher-Rated Children’s Activity Levels Using Millimeter-Wave Radar and Machine Learning: A Pilot Study in a Real Primary School Environment |
| title_fullStr | Detection and Classification of Teacher-Rated Children’s Activity Levels Using Millimeter-Wave Radar and Machine Learning: A Pilot Study in a Real Primary School Environment |
| title_full_unstemmed | Detection and Classification of Teacher-Rated Children’s Activity Levels Using Millimeter-Wave Radar and Machine Learning: A Pilot Study in a Real Primary School Environment |
| title_short | Detection and Classification of Teacher-Rated Children’s Activity Levels Using Millimeter-Wave Radar and Machine Learning: A Pilot Study in a Real Primary School Environment |
| title_sort | detection and classification of teacher rated children x2019 s activity levels using millimeter wave radar and machine learning a pilot study in a real primary school environment |
| topic | Machine learning millimeter-wave radar non-contact monitoring real school environment restlessness |
| url | https://ieeexplore.ieee.org/document/10833603/ |
| work_keys_str_mv | AT tianyiwang detectionandclassificationofteacherratedchildrenx2019sactivitylevelsusingmillimeterwaveradarandmachinelearningapilotstudyinarealprimaryschoolenvironment AT takuyasakamoto detectionandclassificationofteacherratedchildrenx2019sactivitylevelsusingmillimeterwaveradarandmachinelearningapilotstudyinarealprimaryschoolenvironment AT yuoshima detectionandclassificationofteacherratedchildrenx2019sactivitylevelsusingmillimeterwaveradarandmachinelearningapilotstudyinarealprimaryschoolenvironment AT itsukiiwata detectionandclassificationofteacherratedchildrenx2019sactivitylevelsusingmillimeterwaveradarandmachinelearningapilotstudyinarealprimaryschoolenvironment AT masayakato detectionandclassificationofteacherratedchildrenx2019sactivitylevelsusingmillimeterwaveradarandmachinelearningapilotstudyinarealprimaryschoolenvironment AT harutokobayashi detectionandclassificationofteacherratedchildrenx2019sactivitylevelsusingmillimeterwaveradarandmachinelearningapilotstudyinarealprimaryschoolenvironment AT manabuwakuta detectionandclassificationofteacherratedchildrenx2019sactivitylevelsusingmillimeterwaveradarandmachinelearningapilotstudyinarealprimaryschoolenvironment AT masakomyowa detectionandclassificationofteacherratedchildrenx2019sactivitylevelsusingmillimeterwaveradarandmachinelearningapilotstudyinarealprimaryschoolenvironment AT tokomonishimura detectionandclassificationofteacherratedchildrenx2019sactivitylevelsusingmillimeterwaveradarandmachinelearningapilotstudyinarealprimaryschoolenvironment AT atsushisenju detectionandclassificationofteacherratedchildrenx2019sactivitylevelsusingmillimeterwaveradarandmachinelearningapilotstudyinarealprimaryschoolenvironment |