{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T15:30:38Z","timestamp":1777735838856,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2020,7,28]],"date-time":"2020-07-28T00:00:00Z","timestamp":1595894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A fall detection module is an important component of community-based care for the elderly to reduce their health risk. It requires the accuracy of detections as well as maintains energy saving. In order to meet the above requirements, a sensing module-integrated energy-efficient sensor was developed which can sense and cache the data of human activity in sleep mode, and an interrupt-driven algorithm is proposed to transmit the data to a server integrated with ZigBee. Secondly, a deep neural network for fall detection (FD-DNN) running on the server is carefully designed to detect falls accurately. FD-DNN, which combines the convolutional neural networks (CNN) with long short-term memory (LSTM) algorithms, was tested on both with online and offline datasets. The experimental result shows that it takes advantage of CNN and LSTM, and achieved 99.17% fall detection accuracy, while its specificity and sensitivity are 99.94% and 94.09%, respectively. Meanwhile, it has the characteristics of low power consumption.<\/jats:p>","DOI":"10.3390\/s20154192","type":"journal-article","created":{"date-parts":[[2020,7,28]],"date-time":"2020-07-28T10:16:49Z","timestamp":1595931409000},"page":"4192","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["An Energy-Efficient Fall Detection Method Based on FD-DNN for Elderly People"],"prefix":"10.3390","volume":"20","author":[{"given":"Leyuan","family":"Liu","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yibin","family":"Hou","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"},{"name":"Beijing Engineering Research Center for IOT Software and Systems, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"He","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"},{"name":"Beijing Engineering Research Center for IOT Software and Systems, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan","family":"Lungu","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruihai","family":"Dong","sequence":"additional","affiliation":[{"name":"Insight Centre for Data Analytics, University College Dublin, Dublin 4, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,28]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2015). World Report on Ageing and Health, World Health Organization."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.annemergmed.2017.05.023","article-title":"Revisit, subsequent hospitalization, recurrent fall, and death within 6 months after a fall among elderly emergency department patients","volume":"70","author":"Tirrell","year":"2017","journal-title":"Ann. Emerg. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.gaitpost.2014.09.006","article-title":"How fear of falling can increase fall-risk in older adults: Applying psychological theory to practical observations","volume":"41","author":"Young","year":"2015","journal-title":"Gait Posture"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Angal, Y., and Jagtap, A. (2016, January 2\u20133). Fall detection system for older adults. Proceedings of the 2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT), New York, NY, USA.","DOI":"10.1109\/ICAECCT.2016.7942595"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.ijmedinf.2017.12.015","article-title":"The state of knowledge on technologies and their use for fall detection: A scoping review","volume":"111","author":"Lapierre","year":"2018","journal-title":"Int. J. Med Inform."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1109\/JBHI.2015.2425932","article-title":"Fall detection using smartphone audio features","volume":"20","author":"Cheffena","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1109\/MIM.2017.8121952","article-title":"Vision-based fall detection system for improving safety of elderly people","volume":"20","author":"Harrou","year":"2017","journal-title":"IEEE Instrum. Meas. Mag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"149","DOI":"10.3233\/AIS-160369","article-title":"Camera-based fall detection using real-world versus simulated data: How far are we from the solution?","volume":"8","author":"Debard","year":"2016","journal-title":"J. Ambient Intell. Smart Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6441","DOI":"10.3390\/s150306441","article-title":"MEMS sensor technologies for human centred applications in healthcare, physical activities, safety and environmental sensing: A review on research activities in Italy","volume":"15","author":"Ciuti","year":"2015","journal-title":"Sensors"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Casilari, E., and Oviedo-Jim\u00e9nez, M.A. (2015). Automatic fall detection system based on the combined use of a smartphone and a smartwatch. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0140929"},{"key":"ref_11","first-page":"2","article-title":"Development of a wearable-sensor-based fall detection system","volume":"2015","author":"Wu","year":"2015","journal-title":"Int. J. Telemed. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"\u00d6zdemir, A. (2016). An analysis on sensor locations of the human body for wearable fall detection devices: Principles and practice. Sensors, 16.","DOI":"10.3390\/s16081161"},{"key":"ref_13","unstructured":"Qu, W., Lin, F., Wang, A., and Xu, W. (2015). Evaluation of a Low-Complexity Fall Detection Algorithm on Wearable Sensor Towards Falls and Fall-Alike Activities, Proceedings of the 2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Philadelphia, PA, USA, 12 December 2015, IEEE."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Qu, W., Lin, F., and Xu, W. (2016, January 27\u201329). A Real-Time Low-Complexity Fall Detection System on the Smartphone. Proceedings of the 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), Washington, DC, USA.","DOI":"10.1109\/CHASE.2016.73"},{"key":"ref_15","unstructured":"Salgado, P., and Afonso, P. (2014, January 21\u201323). Body Fall Detection with Kalman Filter and SVM. Proceedings of the 11th Portuguese Conference on Automatic Control (CONTROLO), Porto, Portugal."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1450059","DOI":"10.4015\/S1016237214500598","article-title":"Fall detection using three wearable triaxial accelerometers and a decision-tree classifier","volume":"26","author":"Luo","year":"2014","journal-title":"Biomed. Eng. Appl. Basis Commun."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Khojasteh, S., Villar, J., Chira, C., Gonz\u00e1lez, V., and De La Cal, E. (2018). Improving fall detection using an on-wrist wearable accelerometer. Sensors, 18.","DOI":"10.3390\/s18051350"},{"key":"ref_18","unstructured":"Huynh, Q.T., Nguyen, U.D., and Tran, B.Q. (2018, January 27\u201329). A Cloud-Based System for In-Home Fall Detection and Activity Assessment. Proceedings of the International Conference on the Development of Biomedical Engineering in Vietnam, Ho Chi Minh, Vietnam."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.micpro.2017.10.014","article-title":"Energy efficient wearable sensor node for IoT-based fall detection systems","volume":"56","author":"Gia","year":"2018","journal-title":"Microprocess. Microsyst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1140\/epjp\/i2019-12429-1","article-title":"Solutions of the Dirac-Weyl equation in graphene under magnetic fields in the Cartesian coordinate system","volume":"134","author":"Hosseini","year":"2019","journal-title":"Eur. Phys. J. Plus"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103946","DOI":"10.1016\/j.ijmedinf.2019.08.006","article-title":"Fall detection and fall risk assessment in older person using wearable sensors: A systematic review","volume":"130","author":"Bet","year":"2019","journal-title":"Int. J. Med Inform."},{"key":"ref_22","unstructured":"(2020, January 09). MPU-6050 Six-Axis (Gyro + Accelerometer) MEMS MotionTracking\u2122 Devices. Available online: https:\/\/www.invensense.com\/products\/motion-tracking\/6-axis\/mpu-6050\/."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Santos, G.L., Endo, P.T., Monteiro, K.H.d.C., Rocha, E.d.S., Silva, I., and Lynn, T. (2019). Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks. Sensors, 19.","DOI":"10.3390\/s19071644"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1007\/s11760-018-1393-7","article-title":"Human activity classification using long short-term memory network","volume":"13","author":"Welhenge","year":"2019","journal-title":"Signal Image Video Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.neucom.2003.09.006","article-title":"Predictions of a model of spatial attention using sum-and max-pooling functions","volume":"56","author":"Hamker","year":"2004","journal-title":"Neurocomputing"},{"key":"ref_26","first-page":"254","article-title":"Deep motif dashboard: Visualizing and understanding genomic sequences using deep neural networks","volume":"22","author":"Lanchantin","year":"2017","journal-title":"Pac. Symp. Biocomput."},{"key":"ref_27","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sucerquia, A., L\u00f3pez, J., and Vargas-Bonilla, J. (2017). SisFall: A fall and movement dataset. Sensors, 17.","DOI":"10.3390\/s17010198"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Vavoulas, G., Pediaditis, M., Spanakis, E.G., and Tsiknakis, M. (2013, January 10\u201313). The Mobifall Dataset: An Initial Evaluation of Fall Detection Algorithms Using Smartphones. Proceedings of the 13th IEEE International Conference on BioInformatics and BioEngineering, Chania, Greece.","DOI":"10.1109\/BIBE.2013.6701629"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.inffus.2018.05.002","article-title":"Distributed fusion filter for multi-sensor systems with finite-step correlated noises","volume":"46","author":"Tian","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xiao, D., Yu, Z., Yi, F., Wang, L., Tan, C.C., and Guo, B. (2016, January 25\u201327). Smartswim: An infrastructure-free swimmer localization system based on smartphone sensors. Proceedings of the International Conference on Smart Homes and Health Telematics, Wuhan, China.","DOI":"10.1007\/978-3-319-39601-9_20"},{"key":"ref_32","first-page":"2121","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"Duchi","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_33","first-page":"1504","article-title":"Equilibrated adaptive learning rates for non-convex optimization","volume":"1","author":"Dauphin","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv, Available online: https:\/\/arxiv.org\/pdf\/1412.6980v1.pdf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","article-title":"The WEKA data mining software: An update","volume":"11","author":"Hall","year":"2009","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ord\u00f3\u00f1ez, F., and Roggen, D. (2016). Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. 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