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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 鄭素芳 | zh_TW |
| dc.contributor.advisor | Suh-Fang Jeng | en |
| dc.contributor.author | Yohanes Purwanto | zh_TW |
| dc.contributor.author | Yohanes Purwanto | en |
| dc.date.accessioned | 2026-03-13T16:48:50Z | - |
| dc.date.available | 2026-03-14 | - |
| dc.date.copyright | 2026-03-13 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-10-03 | - |
| dc.identifier.citation | Abbott, A., Bartlett, D., Fanning, J., & Kramer, J. (2000). Infant Motor Development and Aspects of the Home Environment. Pediatric Physical Therapy, 12(2), 62-67.
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IEEE international conference on computer vision, ICCV, Venice, Italy. 鄭, 素. 芳., & 楊, 佩. 瑜. (2021). 與巴掌仙子共舞-週歲前的育兒技巧. 臺北臺灣. 金名圖書公司. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/102148 | - |
| dc.description.abstract | none | zh_TW |
| dc.description.abstract | Background: Preterm birth is a global healthcare issue because of the increased risk of neurodevelopmental impairments, with motor disorders being among the most common. Early motor assessment of preterm infants is crucial to help identify those who may develop motor disorders and require early intervention. Mobile applications integrated with artificial intelligence (AI) technology for infant motor assessment are increasingly used. However, such innovations have rarely been designed for infants older than three months. Purpose: This study had three purposes: (1) to upgrade a mobile application “Baby Go” to contain a parental perception assessment and an AI model for infant motor assessment in full-term and preterm infants, (2) to examine the agreement between the parental perception and AI results from the application, and (3) to evaluate the agreement of the parental perception and the AI results with physiotherapists’ labelling results. Methods: Preterm and full-term infants were recruited from the National Taiwan University Children's Hospital in Taiwan. The “Baby Go” application was upgraded to contain the features of parental perception of infant movements, AI assessment for 38 movements, developmental follow-up, and parental education. Parents were asked to register the “Baby Go” application and use it regularly. The validity of the parental perception and the AI assessment results was examined using physiotherapists’ labelling results as the gold standard. Results: Fifty parents registered the application, of which 45 parents (29 of preterm infants and 16 of full-term infants) uploaded at least one video. Movement-based assessment on 498 videos without technical problems showed that the agreement between the parents’ and physiotherapists’ results was 79%, the agreement between the physiotherapists’ and AI results was 79%, and the agreement between the parents’ and AI results was 73%. Age-based assessment on 61 completed trials without technical problems showed that the agreement between the parents’ and AI results was 97%, the agreement between the physiotherapists’ and AI results was 82%, and the agreement between the parents’ and physiotherapists’ results was 74%. Concurrent validity of the parental perception and AI results on 14 trials compared with the AIMS onsite assessment results showed agreement of 79%, sensitivity of 100%, specificity of 75%, positive predictive value of 40%, and negative predictive value of 100%. Conclusions: The upgraded 'Baby Go' application successfully integrates AI-based infant motor assessment but requires further development of the parental perception feature. AI assessments show nearly high agreement for movement-based assessment and high agreement for age-based evaluations with the videos with technical problems excluded. Future work needs to mitigate technical issues and may consider re-arrangement of movement sets to enhance the AI performance for remote infant motor assessment. The “Baby Go” application can serve as a supplement for parents in early infant motor screening and for pediatricians and physiotherapists in motor development follow-up and early intervention, but its parental observations and self-assessment have always followed the standardized assessments designed and monitored by health professionals. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2026-03-13T16:48:50Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2026-03-13T16:48:50Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acceptance Certificate i
Acknowledgment ii Abstract iii Table of Contents v Chapter 1. Literature Review 1 1.1 Preterm Infants and Neurodevelopmental Outcomes 1 1.2 Infant Motor Assessment 3 1.3 Home-based Infant Motor Assessment 6 1.4 Parental Perception of Infant Motor Assessment 8 1.5 Artificial Intelligence for Infant Motor Assessment 10 1.6 AI with Mobile Application for Infant Motor Assessment 13 1.7 Preliminary Study of AI and Parental Perception for Infant Motor Assessment 16 1.8 Study Purposes 18 1.9 Hypotesis 18 Chapter 2. Methods 19 2.1 Subjects 19 2.2 Data Collection 19 2.3 Development of “Baby Go” Application 21 2.4 Data Collection 26 2.5 The AIMS Assessment of Infant Movement 27 2.6 Parental Perception Assessment 29 2.7 AI Assessment 32 2.8 Physiotherapists’ Assessment 34 2.9 Movement- and Age-based Analysis 36 2.10 Parent Education in Application 38 2.11 Statistical Analysis 39 Chapter 3. Results 41 3.1 Infants’ Perinatal Data 41 3.2 Parents’ Sociodemographic Data 41 3.3 Video Uploaded for Movement and Age-based Assessment 42 3.4 Movement-based Assessment Results 43 3.5 Age-based Assessment 57 3.6 Concurrent Validity of AI Model for Age-based Assessment Compared with AIMS Assessment Results 60 Chapter 4. Discussion 61 4.1 Development and Improvement of “Baby Go” Application 61 4.2 Movement-based Assessment Result across Versions and AI Models 63 4.3 Technical Problems Affecting the AI Performance 64 4.4 Movement-based Assessment 66 4.5 Age-based Assessment 71 4.6 Clinical Implications 74 4.7 Limitations and Future Study 74 Chapter 5. Conclusion 76 References 77 Tables 85 Figures 113 Appendices 117 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | none | - |
| dc.subject | preterm infants | - |
| dc.subject | mobile application | - |
| dc.subject | artificial intelligence | - |
| dc.subject | infant motor assessment | - |
| dc.title | 透過行動應用程式進行父母感知與人工智慧嬰兒動作的效度研究 | zh_TW |
| dc.title | A Validity Study of Parental Perception and Artificial Intelligence of Infant Motor Assessment via Mobile Application | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 114-1 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 許永真;曹伯年;陳為堅 ;廖偉智 | zh_TW |
| dc.contributor.oralexamcommittee | Jane Yung-jen Hsu;Po-Nien Tsao;Wei J. Chen;Wei-Chih Liao | en |
| dc.subject.keyword | none, | zh_TW |
| dc.subject.keyword | preterm infants,mobile applicationartificial intelligenceinfant motor assessment | en |
| dc.relation.page | 130 | - |
| dc.identifier.doi | 10.6342/NTU202504540 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-10-03 | - |
| dc.contributor.author-college | 醫學院 | - |
| dc.contributor.author-dept | 物理治療學研究所 | - |
| dc.date.embargo-lift | 2026-03-14 | - |
| 顯示於系所單位： | 物理治療學系所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-114-1.pdf | 3.31 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。