{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T20:35:50Z","timestamp":1724531750858},"reference-count":35,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T00:00:00Z","timestamp":1632873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LGF19F010008"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"Falls are one of the main causes of elderly injuries. If the faller can be found in time, further injury can be effectively avoided. In order to protect personal privacy and improve the accuracy of fall detection, this paper proposes a fall detection algorithm using the CNN-Casual LSTM network based on three-axis acceleration and three-axis rotation angular velocity sensors. The neural network in this system includes an encoding layer, a decoding layer, and a ResNet18 classifier. Furthermore, the encoding layer includes three layers of CNN and three layers of Casual LSTM. The decoding layer includes three layers of deconvolution and three layers of Casual LSTM. The decoding layer maps spatio-temporal information to a hidden variable output that is more conducive relative to the work of the classification network, which is classified by ResNet18. Moreover, we used the public data set SisFall to evaluate the performance of the algorithm. The results of the experiments show that the algorithm has high accuracy up to 99.79%.<\/jats:p>","DOI":"10.3390\/info12100403","type":"journal-article","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T12:27:44Z","timestamp":1632918464000},"page":"403","source":"Crossref","is-referenced-by-count":15,"title":["Fall Detection with CNN-Casual LSTM Network"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-9079-6887","authenticated-orcid":false,"given":"Jiang","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Communication Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China"}]},{"given":"Jiale","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Communication Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China"}]},{"given":"Ao","family":"Zhan","sequence":"additional","affiliation":[{"name":"Department of Communication Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7467-2737","authenticated-orcid":false,"given":"Chengyu","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Communication Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Nari, M.I., Suprapto, S.S., Kusumah, I.H., and Adiprawita, W. (2016, January 29\u201330). A simple design of wearable device for fall detection with accelerometer and gyroscope. Proceedings of the 2016 International Symposium on Electronics and Smart Devices (ISESD), Bandung, Indonesia.","DOI":"10.1109\/ISESD.2016.7886698"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"(2017). World report on ageing and health. Indian J. Med Res., 145, 150\u2013151.","DOI":"10.4103\/0971-5916.207249"},{"key":"ref_3","unstructured":"National Bureau of Statistics of the People\u2019s Republic of China (2010). The Sixth National Population Census of the People\u2019s Republic of China, China National Bureau of Statistics."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.neucom.2011.09.037","article-title":"A survey on fall detection: Principles and approaches","volume":"100","author":"Mubashir","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1109\/JSEN.2016.2628099","article-title":"From fall detection to fall prevention: A generic classification of fall-related systems","volume":"17","author":"Chaccour","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1186\/1475-925X-12-66","article-title":"Challenges, issues and trends in fall detection systems","volume":"12","author":"Igual","year":"2013","journal-title":"BioMed. Eng. OnLine"},{"key":"ref_7","unstructured":"(2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Waheed, S.A., and Khader, P. (2017, January 14\u201316). A Novel Approach for Smart and Cost Effective IoT Based Elderly Fall Detection System Using Pi Camera. Proceedings of the 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Coimbatore, India.","DOI":"10.1109\/ICCIC.2017.8524486"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Huang, Z., Liu, Y., Fang, Y., and Horn, B. (2018, January 21\u201324). Video-based Fall Detection for Seniors with Human Pose Estimation. Proceedings of the 2018 4th International Conference on Universal Village (UV), Boston, MA, USA.","DOI":"10.1109\/UV.2018.8642130"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ogawa, Y., and Naito, K. (2020, January 4\u20136). Fall detection scheme based on temperature distribution with IR array sensor. Proceedings of the 2020 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE46568.2020.9043000"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sun, Y., Hang, R., Li, Z., Jin, M., and Xu, K. (2019, January 1\u20134). Privacy-Preserving Fall Detection with Deep Learning on mmWave Radar Signal. Proceedings of the 2019 IEEE Visual Communications and Image Processing (VCIP), Sydney, Australia.","DOI":"10.1109\/VCIP47243.2019.8965661"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Desai, K., Mane, P., Dsilva, M., Zare, A., and Ambawade, D. (2020, January 2\u20134). A Novel Machine Learning Based Wearable Belt For Fall Detection. Proceedings of the 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON), Greater Noida, India.","DOI":"10.1109\/GUCON48875.2020.9231114"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Astriani, M.S., Heryadi, Y., Kusuma, G.P., and Abdurachman, E. (2019, January 4\u20136). Long Short-Term Memory for Human Fall Detection Based Gamification on Unconstraint Smartphone Position. Proceedings of the 2019 International Congress on Applied Information Technology (AIT), Yogyakarta, Indonesia.","DOI":"10.1109\/AIT49014.2019.9144759"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7980","DOI":"10.1016\/j.eswa.2014.06.045","article-title":"Fall detection based on the gravity vector using a wide-angle camera","volume":"41","author":"Valera","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_15","first-page":"1","article-title":"A fall detection method based on a joint motion map using double convolutional neural networks","volume":"1","author":"Yao","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Agrawal, S.C., Tripathi, R.K., and Jalal, A.S. (2017, January 3\u20135). Human-fall detection from an indoor video surveillance. Proceedings of the 2017 8th International Conference on Computing, Delhi, India.","DOI":"10.1109\/ICCCNT.2017.8203923"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kong, X., Meng, Z., Lin, M., and Tomiyama, H. (September, January 30). A Privacy Protected Fall Detection IoT System for Elderly Persons Using Depth Camera. Proceedings of the 2018 International Conference on Advanced Mechatronic Systems (ICAMechS), Zhengzhou, China.","DOI":"10.1109\/ICAMechS.2018.8506987"},{"key":"ref_18","unstructured":"Tzeng, H.W., Chen, M.Y., and Chen, J.Y. (2010, January 1\u20133). Design of fall detection system with floor pressure and infrared image. Proceedings of the 2010 International Conference on System Science and Engineering, Taipei, Taiwan."},{"key":"ref_19","first-page":"197","article-title":"Fall Detection using Standoff Radar-based Sensing and Deep Convolutional Neural Network","volume":"67","author":"Sadreazami","year":"2019","journal-title":"IEEE Trans. Circuits Syst. II: Express Briefs"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yang, T., Cao, J., and Guo, Y. (2018, January 6\u201310). Placement selection of millimeter wave FMCW radar for indoor fall detection. Proceedings of the 2018 IEEE MTT-S International Wireless Symposium (IWS), Chengdu, China.","DOI":"10.1109\/IEEE-IWS.2018.8400812"},{"key":"ref_21","unstructured":"Gao, T., Yang, J., Huang, K., Hu, Q., and Zhao, F. (2018, January 10\u201312). Research and Implementation of Two-Layer Fall Detection Algorithm. Proceedings of the 2018 5th International Conference on Systems and Informatics (ICSAI), Nanjing, China."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mannini, A., Trojaniello, D., Cereatti, A., and Sabatini, A.M. (2016). A Machine Learning Framework for Gait Classification Using Inertial Sensors: Application to Elderly, Post-Stroke and Huntington\u2019s Disease Patients. Sensors, 16.","DOI":"10.3390\/s16010134"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Shahiduzzaman, K.M., Hei, X., Guo, C., and Cheng, W. (2019, January 20\u201322). Enhancing fall detection for elderly with smart helmet in a cloud-network-edge architecture. Proceedings of the 2019 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), Yilan, Taiwan.","DOI":"10.1109\/ICCE-TW46550.2019.8991972"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mousavi, S.A., Heidari, F., Tahami, E., and Azarnoosh, M. (2021, January 18\u201321). Fall detection system via smart phone and send people location. Proceedings of the 2020 28th European Signal Processing Conference (EUSIPCO), Amsterdam, Netherlands.","DOI":"10.23919\/Eusipco47968.2020.9287349"},{"key":"ref_25","first-page":"161","article-title":"Human Fall Detection Using Machine Learning Methods: A Survey","volume":"5","author":"Singh","year":"2019","journal-title":"Int. J. Math. Eng. Manag. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, R.D., Zhang, Y.L., Dong, L.P., Lu, J.W., and He, X. (2015, January 13\u201316). Fall detection algorithm for the elderly based on human characteristic matrix and SVM. Proceedings of the 2015 15th International Conference on Control, Automation and Systems (ICCAS), Busan, Korea.","DOI":"10.1109\/ICCAS.2015.7364809"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Otanasap, N. (2016, January 16\u201318). Pre-Impact Fall Detection Based on Wearable Device Using Dynamic Threshold Model. Proceedings of the 2016 17th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), Guangzhou, China.","DOI":"10.1109\/PDCAT.2016.083"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"10691","DOI":"10.3390\/s140610691","article-title":"Detecting falls with wearable sensors using machine learning techniques","volume":"14","author":"Barshan","year":"2014","journal-title":"Sensors"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kiprijanovska, I., Gjoreski, H., and Gams, M. (2020). Detection of Gait Abnormalities for Fall Risk Assessment Using Wrist-Worn Inertial Sensors and Deep Learning. Sensors, 20.","DOI":"10.3390\/s20185373"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1109\/TETC.2020.3027454","article-title":"Online Fall Detection using Recurrent Neural Networks on Smart Wearable Devices","volume":"9","author":"Musci","year":"2020","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5110","DOI":"10.1109\/JSEN.2019.2903482","article-title":"A low power fall sensing technology based on FD-CNN","volume":"19","author":"He","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sucerquia, A., Lopez, J.D., and Vargas-Bonilla, J.F. (2016). SisFall: A Fall and Movement Dataset. Sensors, 17.","DOI":"10.20944\/preprints201610.0096.v1"},{"key":"ref_33","unstructured":"Wang, Y., Gao, Z., Long, M., Wang, J., and Yu, P.S. (2018). PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning, PMLR."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jain, A., Zamir, A.R., Savarese, S., and Saxena, A. (2015, January 7\u201312). Structural-RNN: Deep Learning on Spatio-Temporal Graphs. Proceedings of the IEEE Conference on Computer Vision & Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2016.573"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hsieh, S.T., and Lin, C.L. (2020, January 4\u20137). Fall Detection Algorithm Based on MPU6050 and Long-Term Short-Term Memory network. Proceedings of the 2020 International Automatic Control Conference (CACS), Hsinchu, Taiwan.","DOI":"10.1109\/CACS50047.2020.9289769"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/10\/403\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T11:32:54Z","timestamp":1721388774000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/10\/403"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,29]]},"references-count":35,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["info12100403"],"URL":"https:\/\/doi.org\/10.3390\/info12100403","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,29]]}}}