{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T10:18:19Z","timestamp":1787653099443,"version":"build-2736575974"},"reference-count":34,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T00:00:00Z","timestamp":1787616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019345","name":"King Salman Center for Disability Research","doi-asserted-by":"publisher","award":["KSRG-2026-593"],"award-info":[{"award-number":["KSRG-2026-593"]}],"id":[{"id":"10.13039\/501100019345","id-type":"DOI","asserted-by":"publisher"}]},{"award":["KSRG-2026-593"],"award-info":[{"award-number":["KSRG-2026-593"]}],"id":[{"id":"https:\/\/ror.org\/01ht2b307","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Hybrid CNN\u2013Transformer (HCT) synthesis for automated neurodevelopmental diagnostics is an effective approach to constructing intelligent detection systems that are not merely oriented toward feature classification but primarily toward solving spatiotemporal pattern recognition problems in motor disorder assessment. In neurodevelopmental diagnostics, existing automated methods rely on fixed, single-model architectures that process spatial or temporal motion features independently, failing to adapt to the heterogeneous motor irregularities characteristic of developmental coordination disorder and degrading detection sensitivity and generalization across diverse patient populations. There is therefore a pressing need for models capable of simultaneously capturing intra-frame spatial coordination patterns and inter-frame temporal movement dependencies against interrelated diagnostic criteria including accuracy, sensitivity, and motor irregularity specificity. To address this challenge, this paper proposes HCT, a novel framework that integrates ResNet-based spatial feature extraction from optical flow maps and pose estimation skeletons with multi-head self-attention Transformer encoding for modeling long-range temporal dependencies across multi-frame motion sequences. Unlike conventional single-stream approaches, where spatial and temporal processing remain confined to independent architectures, HCT decouples spatiotemporal feature learning through a cross-modal fusion pipeline, constructing a unified discriminative architecture that captures motor coordination dependencies between motion imaging inputs and multiple diagnostic criteria simultaneously. The convolutional encoder generates diverse joint displacement features, which are consolidated through cross-modal attention fusion into a robust, unified embedding with enhanced generalization and resilience to inter-individual motor variability. Integration within neurodevelopmental assessment frameworks facilitates reliable developmental coordination disorder classification, motor irregularity prediction, and interpretable diagnostic decision support, advancing the accuracy, flexibility, and clinical validity of intelligent motor disorder diagnostic systems.<\/jats:p>","DOI":"10.3390\/bioengineering13090970","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T09:04:30Z","timestamp":1787648670000},"page":"970","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid CNN\u2013Transformer Framework for Automated Detection of Developmental Coordination Disorder from Motion Imaging Sequences"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7263-9477","authenticated-orcid":false,"given":"Khaled Mahmoud","family":"Heba","sequence":"first","affiliation":[{"name":"Department of Architecture, College of Engineering and Information Technology, Onaizah Colleges, Onaizah 56447, Saudi Arabia"},{"name":"King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia"},{"name":"Department of Architecture, Faculty of Engineering, Al-Azhar University, Nasr City, Cairo 11884, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abbas Hassan Abbas","family":"Atya","sequence":"additional","affiliation":[{"name":"King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia"},{"name":"Department of Electrical Engineering, College of Engineering and Information Technology, Onaizah Colleges, Onaizah 56447, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noor Hazim Saleh","family":"Alrawashdeh","sequence":"additional","affiliation":[{"name":"Karak Government Hospital (KGH), Al Karak 61101, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0254-3351","authenticated-orcid":false,"given":"Sana","family":"Shahab","sequence":"additional","affiliation":[{"name":"Department of Business Administration, College of Business Administration, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohd","family":"Anjum","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Aligarh Muslim University, Aligarh 202002, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ke, L., Su, X., Yang, S., Du, Z., Huang, S., and Wang, Y. (2023). New trends in developmental coordination disorder: Multivariate, multidimensional and multimodal. Front. Psychiatry, 14.","DOI":"10.3389\/fpsyt.2023.1116369"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104994","DOI":"10.1016\/j.ridd.2025.104994","article-title":"A systematic review of language and motor skills in children with developmental coordination disorder (DCD) and developmental language disorder (DLD)","volume":"161","author":"Kisjes","year":"2025","journal-title":"Res. Dev. Disabil."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104658","DOI":"10.1016\/j.ridd.2023.104658","article-title":"The impact for DCD\u2013USA study: The current state of Developmental Coordination Disorder (DCD) in the United States of America","volume":"145","author":"Tamplain","year":"2024","journal-title":"Res. Dev. Disabil."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1007\/s42600-024-00358-3","article-title":"Psychomotor intervention through serious games in children and adolescents with autism spectrum disorder using a therapeutic robot","volume":"40","author":"Panceri","year":"2024","journal-title":"Res. Biomed. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1007\/s10803-024-06563-4","article-title":"The use of digital interventions for children and adolescents with autism spectrum disorder\u2014A meta-analysis","volume":"56","author":"Xu","year":"2026","journal-title":"J. Autism Dev. Disord."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Subara-Zukic, E., Cole, M.H., McGuckian, T.B., Steenbergen, B., Green, D., Smits-Engelsman, B.C., Lust, J.M., Abdollahipour, R., Domell\u00f6f, E., and Deconinck, F.J.A. (2022). Behavioral and neuroimaging research on developmental coordination disorder (DCD): A combined systematic review and meta-analysis of recent findings. Front. Psychol., 13.","DOI":"10.3389\/fpsyg.2022.809455"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"111311","DOI":"10.1016\/j.brainresbull.2025.111311","article-title":"Towards automatic assessment of atypical early motor development?","volume":"224","author":"Ossmy","year":"2025","journal-title":"Brain Res. Bull."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Habiba, M., Pearlmutter, B.A., and Maleki, M. (2026). Probabilistic and Generative Approaches to Continuous Time Series Forecasting. Recent Trends in Modelling the Continuous Time Series Using Deep Learning, Springer.","DOI":"10.1007\/978-3-032-18022-3"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1109\/TCDS.2025.3574145","article-title":"Advancements in Automated Assessment and Diagnosis of Autism Spectrum Disorder through Multi-modality Sensing Technologies: Survey of the Last Decade","volume":"17","author":"Shamhan","year":"2025","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ahmed, H.B., and Alzuoubi, M. (2025). Designing accessible virtual reality interfaces using reinforcement learning for users with motor and sensory impairments. PatterniQ Mining, Sahara Digital Publications.","DOI":"10.70023\/sahd\/250201"},{"key":"ref_11","first-page":"51","article-title":"Hybrid Molecular Nano Frameworks for Disease Modeling Using Neuro Symbolic Machine Learning","volume":"1","author":"Ismail","year":"2025","journal-title":"J. Nano Mol. Intell. Virtual Health Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1177\/01445987241269496","article-title":"Comparative study of long short-term memory (LSTM), bidirectional LSTM, and traditional machine learning approaches for energy consumption prediction","volume":"43","author":"Alizadegan","year":"2025","journal-title":"Energy Explor. Exploit."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"74069","DOI":"10.1109\/ACCESS.2024.3394930","article-title":"MPNet-GRUs: Sentiment analysis with masked and permuted pre-training for language understanding and gated recurrent units","volume":"12","author":"Loh","year":"2024","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"109683","DOI":"10.1016\/j.sigpro.2024.109683","article-title":"Integrating self-attention mechanisms in deep learning: A novel dual-head ensemble transformer with its application to bearing fault diagnosis","volume":"227","author":"Snyder","year":"2025","journal-title":"Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4976","DOI":"10.1109\/JSEN.2025.3639076","article-title":"Diagnosis and severity assessment of Parkinson\u2019s disease using wearable sensors and Wide-CNN-Transformer Network","volume":"26","author":"Ji","year":"2025","journal-title":"IEEE Sens. J."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shi, L., Wang, R., Zhao, J., Zhang, J., and Kuang, Z. (2024). Detection of rehabilitation training effect of upper limb movement disorder based on MPL-CNN. Sensors, 24.","DOI":"10.3390\/s24041105"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e70100","DOI":"10.1002\/brb3.70100","article-title":"CNN-Based Neurodegenerative Disease Classification Using QR-Represented Gait Data","volume":"14","year":"2024","journal-title":"Brain Behav."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"122074","DOI":"10.1016\/j.eswa.2023.122074","article-title":"Portable vision-based gait assessment for post-stroke rehabilitation using an attention-based lightweight CNN","volume":"238","author":"Zhou","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3188","DOI":"10.1109\/TNSRE.2023.3299355","article-title":"A temporal dependency learning CNN with attention mechanism for MI-EEG decoding","volume":"31","author":"Ma","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4564","DOI":"10.1109\/TCSVT.2023.3269666","article-title":"Spatiotemporal learning transformer for video-based human pose estimation","volume":"33","author":"Gai","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"112476","DOI":"10.1109\/ACCESS.2025.3583930","article-title":"Spatial-Temporal Transformer for Optimizing Human Health Through Skeleton-Based Body Sports Action Recognition","volume":"13","author":"Liang","year":"2025","journal-title":"IEEE Access"},{"key":"ref_22","first-page":"85","article-title":"A survey on spatio-temporal prediction: From transformers to foundation models","volume":"58","author":"Mao","year":"2025","journal-title":"ACM Comput. Surv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e0299252025","DOI":"10.1523\/JNEUROSCI.0299-25.2025","article-title":"Motor sequence learning involves better prediction of the next action and optimization of movement trajectories","volume":"45","author":"Kashefi","year":"2025","journal-title":"J. Neurosci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.1109\/JBHI.2024.3511601","article-title":"Enhancing recognition of stereotyped movements in asd children through action pattern mining and multi-channel fusion","volume":"29","author":"Zhang","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"114810","DOI":"10.1016\/j.asoc.2026.114810","article-title":"MCADFNet: Multi-scale cross-modal attention dynamic decision fusion network for Parkinson\u2019s quantitative grading","volume":"193","author":"Liu","year":"2026","journal-title":"Appl. Soft Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"15524","DOI":"10.1109\/JSEN.2026.3680124","article-title":"Unilateral Upper Limb Fine Motor Imagery Recognition via Symmetric Cross-Modal Attention EEG-EMG Fusion","volume":"26","author":"Dong","year":"2026","journal-title":"IEEE Sens. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1952","DOI":"10.1109\/TAFFC.2026.3676489","article-title":"Attention-Emotion Assessment of ASD Children via Representation Learning based on Cross-Modal Disentanglement and Attention Alignment","volume":"17","author":"Zhou","year":"2026","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105359","DOI":"10.1016\/j.bspc.2023.105359","article-title":"Temporal\u2013spatial transformer based motor imagery classification for BCI using independent component analysis","volume":"87","author":"Hameed","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Teng, X., Li, X., and Wei, B. (2025). ModFus-PD: Synergizing cross-modal attention and contrastive learning for enhanced multimodal diagnosis of Parkinson\u2019s disease. Front. Comput. Neurosci., 19.","DOI":"10.3389\/fncom.2025.1604399"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, D., Wu, P., Chen, M., Wu, M., Zhang, T., and Li, C. (2024). LS-VIT: Vision Transformer for action recognition based on long and short-term temporal difference. Front. Neurorobot., 18.","DOI":"10.3389\/fnbot.2024.1457843"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Qin, J., Chen, S., Ye, Z., Liu, J., and Liu, Z. (2025). Video swin-CLSTM transformer: Enhancing human action recognition with optical flow and long-term dependencies. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0327717"},{"key":"ref_32","first-page":"72","article-title":"Soccer Action Detection Using TimeSformer: A Transformer-Based Approach to Video Understanding","volume":"41","author":"Rahaman","year":"2026","journal-title":"J. Comput. Sci. Coll."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shin, J., Miah, A.S.M., Kakizaki, M., Hassan, N., and Tomioka, Y. (2025). Autism spectrum disorder detection using skeleton-based body movement analysis via dual-stream deep learning. Electronics, 14.","DOI":"10.3390\/electronics14112231"},{"key":"ref_34","unstructured":"(2026, March 26). Available online: https:\/\/rose1.ntu.edu.sg\/dataset\/actionRecognition\/."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/9\/970\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T09:18:55Z","timestamp":1787649535000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/9\/970"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,25]]},"references-count":34,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["bioengineering13090970"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13090970","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,25]]}}}