{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:24:28Z","timestamp":1743060268742,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":39,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819620708"},{"type":"electronic","value":"9789819620715"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-2071-5_7","type":"book-chapter","created":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T15:34:16Z","timestamp":1735745656000},"page":"85-99","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SSDL: Sensor-to-Skeleton Diffusion Model with\u00a0Lipschitz Regularization for\u00a0Human Activity Recognition"],"prefix":"10.1007","author":[{"given":"Nikhil","family":"Sharma","sequence":"first","affiliation":[]},{"given":"Changchang","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Zhenghao","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Anne Hee Hiong","family":"Ngu","sequence":"additional","affiliation":[]},{"given":"Hugo","family":"Latapie","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Yan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,2]]},"reference":[{"issue":"1","key":"7_CR1","first-page":"5853917","volume":"2018","author":"RM Al-Eidan","year":"2018","unstructured":"Al-Eidan, R.M., Al-Khalifa, H., Al-Salman, A.M.: A review of wrist-worn wearable: sensors, models, and challenges. J. Sens. 2018(1), 5853917 (2018)","journal-title":"J. Sens."},{"issue":"1","key":"7_CR2","doi-asserted-by":"publisher","first-page":"329","DOI":"10.3390\/app14010329","volume":"14","author":"AI Alexan","year":"2024","unstructured":"Alexan, A.I., Alexan, A.R., Oniga, S.: Real-time machine learning for human activities recognition based on wrist-worn wearable devices. Appl. Sci. 14(1), 329 (2024)","journal-title":"Appl. Sci."},{"issue":"5","key":"7_CR3","doi-asserted-by":"publisher","first-page":"2188","DOI":"10.3390\/app11052188","volume":"11","author":"A Anagnostis","year":"2021","unstructured":"Anagnostis, A., Benos, L., Tsaopoulos, D., Tagarakis, A., Tsolakis, N., Bochtis, D.: Human activity recognition through recurrent neural networks for human-robot interaction in agriculture. Appl. Sci. 11(5), 2188 (2021)","journal-title":"Appl. Sci."},{"issue":"8","key":"7_CR4","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1038\/s41569-021-00522-7","volume":"18","author":"K Bayoumy","year":"2021","unstructured":"Bayoumy, K., et al.: Smart wearable devices in cardiovascular care: where we are and how to move forward. Nat. Rev. Cardiol. 18(8), 581\u2013599 (2021)","journal-title":"Nat. Rev. Cardiol."},{"issue":"4","key":"7_CR5","doi-asserted-by":"publisher","first-page":"3222","DOI":"10.1007\/s10489-024-05319-y","volume":"54","author":"X Chao","year":"2024","unstructured":"Chao, X., Ji, G., Qii, X.: Multi-view key information representation and multi-modal fusion for single-subject routine action recognition. Appl. Intell. 54(4), 3222\u20133244 (2024)","journal-title":"Appl. Intell."},{"key":"7_CR6","doi-asserted-by":"crossref","unstructured":"Chen, C., Jafari, R., Kehtarnavaz, N.: UTD-MHAD: a multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor. In: ICIP, pp. 168\u2013172 (2015)","DOI":"10.1109\/ICIP.2015.7350781"},{"key":"7_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107561","volume":"108","author":"LM Dang","year":"2020","unstructured":"Dang, L.M., Min, K., Wang, H., Piran, M.J., Lee, C.H., Moon, H.: Sensor-based and vision-based human activity recognition: a comprehensive survey. Pattern Recogn. 108, 107561 (2020)","journal-title":"Pattern Recogn."},{"issue":"10","key":"7_CR8","doi-asserted-by":"publisher","first-page":"11569","DOI":"10.1109\/JSEN.2020.3034614","volume":"21","author":"A Das","year":"2021","unstructured":"Das, A., Sil, P., Singh, P.K., Bhateja, V., Sarkar, R.: MMHAR-EnsemNet: a multi-modal human activity recognition model. IEEE Sens. J. 21(10), 11569\u201311576 (2021)","journal-title":"IEEE Sens. J."},{"issue":"7","key":"7_CR9","doi-asserted-by":"publisher","first-page":"1340","DOI":"10.1109\/TCYB.2014.2350774","volume":"45","author":"M Devanne","year":"2015","unstructured":"Devanne, M., Wannous, H., Berretti, S., Pala, P., Daoudi, M., Bimbo, A.D.: 3-D human action recognition by shape analysis of motion trajectories on Riemannian manifold. IEEE Trans. Cybern. 45(7), 1340\u20131352 (2015)","journal-title":"IEEE Trans. Cybern."},{"key":"7_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107728","volume":"111","author":"W Gao","year":"2021","unstructured":"Gao, W., Zhang, L., Teng, Q., He, J., Wu, H.: DanHAR: dual attention network for multimodal human activity recognition using wearable sensors. Appl. Soft Comput. 111, 107728 (2021)","journal-title":"Appl. Soft Comput."},{"key":"7_CR11","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: NeurIPS, vol. 33, pp. 6840\u20136851 (2020)"},{"key":"7_CR12","unstructured":"Hussein, M.E., Torki, M., Gowayyed, M.A., El-Saban, M.: Human action recognition using a temporal hierarchy of covariance descriptors on 3D joint locations. In: IJCAI, pp. 2466\u20132472 (2013)"},{"issue":"5","key":"7_CR13","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1109\/LSP.2017.2678539","volume":"24","author":"C Li","year":"2017","unstructured":"Li, C., Hou, Y., Wang, P., Li, W.: Joint distance maps based action recognition with convolutional neural networks. IEEE Signal Process. Lett. 24(5), 624\u2013628 (2017)","journal-title":"IEEE Signal Process. Lett."},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Lin, J., Gan, C., Han, S.: TSM: temporal shift module for efficient video understanding. In: ICCV, pp. 7082\u20137092 (2019)","DOI":"10.1109\/ICCV.2019.00718"},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Mutegeki, R., Han, D.S.: A CNN-LSTM approach to human activity recognition. In: ICAIIC, pp. 362\u2013366 (2020)","DOI":"10.1109\/ICAIIC48513.2020.9065078"},{"issue":"4","key":"7_CR16","doi-asserted-by":"publisher","first-page":"920","DOI":"10.1137\/22M1478835","volume":"5","author":"R Muthukumar","year":"2023","unstructured":"Muthukumar, R., Sulam, J.: Adversarial robustness of sparse local lipschitz predictors. SIAM J. Math. Data Sci. 5(4), 920\u2013948 (2023)","journal-title":"SIAM J. Math. Data Sci."},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Ni, J., Ngu, A.H., Yan, Y.: Progressive cross-modal knowledge distillation for human action recognition. In: MM, pp. 10\u201314 (2022)","DOI":"10.1145\/3503161.3548238"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Ni, J., Sarbajna, R., Liu, Y., Ngu, A.H., Yan, Y.: Cross-modal knowledge distillation for vision-to-sensor action recognition. In: ICASSP, pp. 4448\u20134452 (2022)","DOI":"10.1109\/ICASSP43922.2022.9746752"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., Bajcsy, R.: Berkeley MHAD: a comprehensive multimodal human action database. In: WACV, pp. 53\u201360 (2013)","DOI":"10.1109\/WACV.2013.6474999"},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Shang, Y., Duan, B., Zong, Z., Nie, L., Yan, Y.: Lipschitz continuity guided knowledge distillation. In: ICCV, pp. 10655\u201310664. IEEE (2021)","DOI":"10.1109\/ICCV48922.2021.01050"},{"key":"7_CR21","doi-asserted-by":"publisher","unstructured":"Shang, Y., Xu, D., Duan, B., Zong, Z., Nie, L., Yan, Y.: Lipschitz continuity retained binary neural network. In: Avidan, S., Brostow, G.J., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV, vol. 13671, pp. 603\u2013619. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20083-0_36","DOI":"10.1007\/978-3-031-20083-0_36"},{"key":"7_CR22","doi-asserted-by":"publisher","unstructured":"Shang, Y., Xu, D., Zong, Z., Nie, L., Yan, Y.: Network binarization via contrastive learning. In: Avidan, S., Brostow, G.J., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV, vol. 13671, pp. 586\u2013602. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20083-0_35","DOI":"10.1007\/978-3-031-20083-0_35"},{"key":"7_CR23","doi-asserted-by":"crossref","unstructured":"Shang, Y., Yuan, Z., Xie, B., Wu, B., Yan, Y.: Post-training quantization on diffusion models. In: CVPR, pp. 1972\u20131981 (2023)","DOI":"10.1109\/CVPR52729.2023.00196"},{"issue":"6","key":"7_CR24","doi-asserted-by":"publisher","first-page":"8575","DOI":"10.1109\/JSEN.2020.3045135","volume":"21","author":"SP Singh","year":"2020","unstructured":"Singh, S.P., Sharma, M.K., Lay-Ekuakille, A., Gangwar, D., Gupta, S.: Deep ConvLSTM with self-attention for human activity decoding using wearable sensors. IEEE Sens. J. 21(6), 8575\u20138582 (2020)","journal-title":"IEEE Sens. J."},{"key":"7_CR25","doi-asserted-by":"crossref","unstructured":"Veeriah, V., Zhuang, N., Qi, G.J.: Differential recurrent neural networks for action recognition. In: ICCV, pp. 4041\u20134049 (2015)","DOI":"10.1109\/ICCV.2015.460"},{"key":"7_CR26","doi-asserted-by":"crossref","unstructured":"Viswambaran, R.A., Chen, G., Xue, B., Nekooei, M.: Evolutionary design of recurrent neural network architecture for human activity recognition. In: CEC, pp. 554\u2013561 (2019)","DOI":"10.1109\/CEC.2019.8790050"},{"key":"7_CR27","doi-asserted-by":"crossref","unstructured":"Wang, P., Li, Z., Hou, Y., Li, W.: Action recognition based on joint trajectory maps using convolutional neural networks. In: MM, pp. 102\u2013106 (2016)","DOI":"10.1145\/2964284.2967191"},{"key":"7_CR28","doi-asserted-by":"crossref","unstructured":"Wei, X., Wang, Z.: TCN-attention-HAR: human activity recognition based on attention mechanism time convolutional network. Sci. Rep. 14, 7414 (2024)","DOI":"10.1038\/s41598-024-57912-3"},{"key":"7_CR29","unstructured":"Wu, J., Wang, H., Shang, Y., Shah, M., Yan, Y.: PTQ4DIT: post-training quantization for diffusion transformers. CoRR abs\/2405.16005 (2024)"},{"key":"7_CR30","doi-asserted-by":"crossref","unstructured":"Wu, Z., Sun, C., Xuan, H., Liu, G., Yan, Y.: WaveFormer: wavelet transformer for noise-robust video inpainting. In: Wooldridge, M.J., Dy, J.G., Natarajan, S. (eds.) AAAI, pp. 6180\u20136188 (2024)","DOI":"10.1609\/aaai.v38i6.28435"},{"key":"7_CR31","doi-asserted-by":"crossref","unstructured":"Wu, Z., Sun, C., Xuan, H., Yan, Y.: Deep stereo video inpainting. In: CVPR, pp. 5693\u20135702 (2023)","DOI":"10.1109\/CVPR52729.2023.00551"},{"key":"7_CR32","doi-asserted-by":"crossref","unstructured":"Wu, Z., Xuan, H., Sun, C., Guan, W., Zhang, K., Yan, Y.: Semi-supervised video inpainting with cycle consistency constraints. In: CVPR, pp. 22586\u201322595 (2023)","DOI":"10.1109\/CVPR52729.2023.02163"},{"key":"7_CR33","doi-asserted-by":"crossref","unstructured":"Wu, Z., Zhang, K., Sun, C., Xuan, H., Yan, Y.: Flow-guided deformable alignment network with self-supervision for video inpainting. In: ICASSP, pp.\u00a01\u20135. IEEE (2023)","DOI":"10.1109\/ICASSP49357.2023.10096432"},{"key":"7_CR34","doi-asserted-by":"publisher","unstructured":"Xiaochun, Y., Zengguang, L., Deyong, L., Xiaojun, R.: A novel CNN-based bi-LSTM parallel model with attention mechanism for human activity recognition with noisy data. Sci. Rep. 12(1) (2022). https:\/\/doi.org\/10.1007\/s44196-024-00689-0","DOI":"10.1007\/s44196-024-00689-0"},{"issue":"8","key":"7_CR35","first-page":"2405","volume":"29","author":"Z Yang","year":"2019","unstructured":"Yang, Z., Li, Y., Yang, J., Luo, J.: Action recognition with spatio-temporal visual attention on skeleton image sequences. TCSVT 29(8), 2405\u20132415 (2019)","journal-title":"TCSVT"},{"issue":"2","key":"7_CR36","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/MMUL.2012.24","volume":"19","author":"Z Zhang","year":"2012","unstructured":"Zhang, Z.: Microsoft kinect sensor and its effect. IEEE Multimedia 19(2), 4\u201310 (2012)","journal-title":"IEEE Multimedia"},{"key":"7_CR37","doi-asserted-by":"crossref","unstructured":"Zhao, R., Wang, K., Su, H., Ji, Q.: Bayesian graph convolution LSTM for skeleton based action recognition. In: ICCV, pp. 6881\u20136891 (2019)","DOI":"10.1109\/ICCV.2019.00698"},{"key":"7_CR38","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Tang, H., Wan, J., Yan, Y.: Monocular expressive 3D human reconstruction of multiple people. In: ICMR, pp. 423\u2013432 (2024)","DOI":"10.1145\/3652583.3658092"},{"key":"7_CR39","doi-asserted-by":"crossref","unstructured":"Zhiyuan, R., Zhihong, P., Xin, Z., Le, K.: Diffusion motion: generate text-guided 3D human motion by diffusion model. In: ICASSP, pp.\u00a01\u20135 (2023)","DOI":"10.1109\/ICASSP49357.2023.10096441"}],"container-title":["Lecture Notes in Computer Science","MultiMedia Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-2071-5_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T16:01:52Z","timestamp":1735747312000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-2071-5_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819620708","9789819620715"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-2071-5_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MMM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Multimedia Modeling","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nara","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 January 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 January 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mmm2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mmm2025.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}