{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T23:46:33Z","timestamp":1771458393762,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T00:00:00Z","timestamp":1676073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council of Taiwan","award":["111-2221-E-008-087"],"award-info":[{"award-number":["111-2221-E-008-087"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Fusing object detection techniques and stochastic variational inference, we proposed a new scheme for lightweight neural network models, which could simultaneously reduce model sizes and raise the inference speed. This technique was then applied in fast human posture identification. The integer-arithmetic-only algorithm and the feature pyramid network were adopted to reduce the computational complexity in training and to capture features of small objects, respectively. Features of sequential human motion frames (i.e., the centroid coordinates of bounding boxes) were extracted by the self-attention mechanism. With the techniques of Bayesian neural network and stochastic variational inference, human postures could be promptly classified by fast resolving of the Gaussian mixture model for human posture classification. The model took instant centroid features as inputs and indicated possible human postures in the probabilistic maps. Our model had better overall performance than the baseline model ResNet in mean average precision (32.5 vs. 34.6), inference speed (27 vs. 48 milliseconds), and model size (46.2 vs. 227.8 MB). The model could also alert a suspected human falling event about 0.66 s in advance.<\/jats:p>","DOI":"10.3390\/e25020336","type":"journal-article","created":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T03:07:57Z","timestamp":1676257677000},"page":"336","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Lightweight Deep Neural Network Embedded with Stochastic Variational Inference Loss Function for Fast Detection of Human Postures"],"prefix":"10.3390","volume":"25","author":[{"given":"Feng-Shuo","family":"Hsu","sequence":"first","affiliation":[{"name":"Bio-Microsystems Integration Laboratory, Department of Biomedical Sciences and Engineering, National Central University, Taoyuan 320317, Taiwan"},{"name":"Department of Psychiatry, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung 427213, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zi-Jun","family":"Su","sequence":"additional","affiliation":[{"name":"Bio-Microsystems Integration Laboratory, Department of Biomedical Sciences and Engineering, National Central University, Taoyuan 320317, Taiwan"},{"name":"Department of Computer Science, College of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 30093, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yamin","family":"Kao","sequence":"additional","affiliation":[{"name":"Bio-Microsystems Integration Laboratory, Department of Biomedical Sciences and Engineering, National Central University, Taoyuan 320317, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sen-Wei","family":"Tsai","sequence":"additional","affiliation":[{"name":"Department of Physical Medicine and Rehabilitation, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung 427213, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying-Chao","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Neurological Institute, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung 427213, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Po-Hsun","family":"Tu","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan 33304, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9414-3798","authenticated-orcid":false,"given":"Cihun-Siyong Alex","family":"Gong","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering, Chang Gung University, Taoyuan 33302, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2058-1263","authenticated-orcid":false,"given":"Chien-Chang","family":"Chen","sequence":"additional","affiliation":[{"name":"Bio-Microsystems Integration Laboratory, Department of Biomedical Sciences and Engineering, National Central University, Taoyuan 320317, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chander, H., Burch, R.F., Talegaonkar, P., Saucier, D., Luczak, T., Ball, J.E., Turner, A., Kodithuwakku Arachchige, S.N.K., Carroll, W., and Smith, B.K. (2020). Wearable Stretch Sensors for Human Movement Monitoring and Fall Detection in Ergonomics. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17103554"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1007\/s12652-019-01214-4","article-title":"Fall detection and human activity classification using wearable sensors and compressed sensing","volume":"11","author":"Kerdjidj","year":"2020","journal-title":"J. Ambient. Intell. Human Comput."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hsu, F.-S., Chang, T.-C., Su, Z.-J., Huang, S.-J., and Chen, C.-C. (2021). Smart Fall Detection Framework Using Hybridized Video and Ultrasonic Sensors. Micromachines, 12.","DOI":"10.3390\/mi12050508"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2471","DOI":"10.1038\/s41598-021-81115-9","article-title":"An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box","volume":"11","author":"Shu","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3679","DOI":"10.1007\/s00500-021-06717-x","article-title":"Human fall detection and activity monitoring: A comparative analysis of vision-based methods for classification and detection techniques","volume":"26","author":"Rastogi","year":"2022","journal-title":"Soft Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2529","DOI":"10.1007\/s13042-020-01138-y","article-title":"Human posture recognition based on multiple features and rule learning","volume":"11","author":"Ding","year":"2020","journal-title":"Int. J. Mach. Learn. Cyber."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Alanazi, T., and Muhammad, G. (2022). Human Fall Detection Using 3D Multi-Stream Convolutional Neural Networks with Fusion. Diagnostics, 12.","DOI":"10.3390\/diagnostics12123060"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fei, K., Wang, C., Zhang, J., Liu, Y., Xie, X., and Tu, Z. (2022). Flow-pose Net: An effective two-stream network for fall detection. Vis. Comput.","DOI":"10.1007\/s00371-022-02416-2"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.patrec.2020.09.029","article-title":"3D PostureNet: A unified framework for skeleton-based posture recognition","volume":"140","author":"Liu","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","article-title":"SSD: Single Shot MultiBox Detector","volume":"Volume 9905","author":"Leibe","year":"2016","journal-title":"Lecture Notes in Computer Science"},{"key":"ref_11","unstructured":"Araki, R., Onishi, T., Hirakawa, T., Yamashita, T., and Fujiyoshi, H. (June, January 31). MT-DSSD: Deconvolutional Single Shot Detector Using Multi Task Learning for Object Detection, Segmentation, and Grasping Detection. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Paris, France."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shen, Z., Liu, Z., Li, J., Jiang, Y.-G., and Xue, X. (2017, January 22\u201329). DSOD: Learning Deeply Supervised Object Detectors from Scratch. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.212"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_14","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"14153","DOI":"10.1038\/s41598-022-16828-6","article-title":"A lightweight neural network with multiscale feature enhancement for liver CT segmentation","volume":"12","author":"Ansari","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"14474","DOI":"10.1038\/s41598-022-18263-z","article-title":"Lightweight convolutional neural network for aircraft small target real-time detection in Airport videos in complex scenes","volume":"12","author":"Li","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_18","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201323). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_20","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (November, January 27). Searching for MobileNetV3. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_21","unstructured":"Tan, M., and Le, Q.V. (2019). MixConv: Mixed Depthwise Convolutional Kernels. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_24","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. arXiv."},{"key":"ref_25","unstructured":"Han, S., Mao, H., and Dally, W.J. (2015). Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.-T., and Sun, J. (2018, January 8\u201314). ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. Proceedings of the 15th European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., and Kalenichenko, D. (2018, January 18\u201323). Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00286"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhao, H., Liu, D., and Li, H. (2020). Efficient Integer-Arithmetic-Only Convolutional Neural Networks. arXiv.","DOI":"10.1109\/ISCAS51556.2021.9401448"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Luong, M.-T., Pham, H., and Manning, C.D. (2015). Effective Approaches to Attention-based Neural Machine Translation. arXiv.","DOI":"10.18653\/v1\/D15-1166"},{"key":"ref_31","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is All you Need. Proceedings of the 31st Annual Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4044","DOI":"10.1109\/TII.2018.2885363","article-title":"Nonlinear Gaussian Mixture Regression for Multimode Quality Prediction With Partially Labeled Data","volume":"15","author":"Yao","year":"2019","journal-title":"IEEE Trans. Industr. Inform."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","article-title":"Microsoft COCO: Common Objects in Context","volume":"Volume 8693","author":"Fleet","year":"2014","journal-title":"Lecture Notes in Computer Science"},{"key":"ref_34","unstructured":"Jia, D., Wei, D., Richard, S., Li, L.-J., Li, K., and Li, F.-F. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, USA."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The PASCAL Visual Object Classes Challenge: A Retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_36","first-page":"1303","article-title":"Stochastic Variational Inference","volume":"14","author":"Hoffman","year":"2013","journal-title":"J. Mach. Learn. Res."},{"key":"ref_37","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-Encoding Variational Bayes. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/01621459.2017.1285773","article-title":"Variational Inference: A Review for Statisticians","volume":"112","author":"Blei","year":"2017","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1038\/s41598-017-18931-5","article-title":"Unsupervised Learning and Pattern Recognition of Biological Data Structures with Density Functional Theory and Machine Learning","volume":"8","author":"Chen","year":"2018","journal-title":"Sci. Rep."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/2\/336\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:31:54Z","timestamp":1760121114000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/2\/336"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,11]]},"references-count":39,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["e25020336"],"URL":"https:\/\/doi.org\/10.3390\/e25020336","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,11]]}}}