Online Fall Detection Using Recurrent Neural Networks on Smart Wearable Devices
Open accessAbstract
Unintentional falls can cause severe injuries and even death, especially if no immediate assistance is given. A fall detection system aims to detect a fall as soon as it occurs, therefore issuing an automatic assistance request. Wearable embedded sensors are emerging as the most viable solution for continuous monitoring since they are more effective, less intrusive and less expensive than other systems. Tailoring a deep learning method to the requirements of microcontrollers entails matching very stringent constraints in terms of both memory and computational power. In addition, datasets acquired with wearable devices are relatively scarce and not necessarily devised for supervised learning. In this work, we discuss the design of a software architecture based on recurrent neural networks which can be effective for fall detection while running entirely onboard a wearable device. The well-known and publicly-available SisFall dataset was adopted and extended with fine-grained temporal annotations to cope with the supervised training of recurrent neural networks. We then show that an appropriate process of architectural minimization together with accurate hyperparameters selection leads to a workable model which compares favorably with other detection techniques. The embedding of the resulting architecture has been validated using a state-of-art hardware device.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
LSTMs maintain an internal memory state, allowing the model to distinguish between a 'controlled descent' and a 'sudden drop' based on the velocity history of the movement.
“Unlike static classifiers, LSTMs maintain an internal memory state, allowing the model to distinguish between a “controlled descent” and a “sudden drop” based on the velocity history of the movement [18].”
From Safe Fall: Use of Predictive Modeling and Machine Vision Techniques for Fall Analysis and Fall Quality (2026) · cites this paper as [18]
The Deep Learning Approach and Training and Inference sections describe an LSTM classifier that processes successive accelerometer readings and passes information between cells. The Dataset and Labeling section distinguishes falls from activities such as sitting on a chair. The paper does not say that the model distinguishes a “controlled descent” from a “sudden drop” specifically by using velocity history; its stated input is accelerometer readings. (LLM, conf=0.85)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · see citing works
TensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,369 · see citing works
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172 · see citing works
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382 · see citing works
Han, S., Mao, H., & Dally, W. J. (2015) · arXiv · doi:10.48550/arxiv.1510.00149 · Cited by 4,662 · see citing works
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
Ordóñez, F., & Roggen, D. (2016) · Sensors · doi:10.3390/s16010115 · Cited by 2,761 · cited 1× · see citing works
“n of the classifier. Indeed, they investigate the use of ANN, KNN, Radial Basis Function (RBF), Probabilistic Principal Component Analysis (PPCA) and Linear Discriminant Analysis (LDA). The authors of [14] utilize a DL technique to detect falls. The architecture is composed by two parts: a Convolutional Network and a R…”
MethodA Critical Review of Recurrent Neural Networks for Sequence LearningPreprint
Lipton, Z. C., Berkowitz, J. A., & Elkan, C. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.00019 · Cited by 2,711 · see citing works
Binarized Neural NetworksPreprint
Hubara, I., Soudry, D., & El-Yaniv, R. (2016) · arXiv.org · doi:10.48550/arxiv.1602.02505 · Cited by 2,241 · see citing works
TVM: an automated end-to-end optimizing compiler for deep learning
(2018) · arXiv (Cornell University) · doi:10.5555/3291168.3291211 · Cited by 1,170 · see citing works
Compressing Deep Convolutional Networks using Vector QuantizationPreprint
Gong, Y., Liu, L., Yang, M., & Bourdev, L. D. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.6115 · Cited by 1,047 · see citing works
Predicting Parameters in Deep Learning
Denil, M., Shakibi, B., Dinh, L., Ranzato, M., & de Freitas, N. (2013) · arXiv · doi:10.48550/arxiv.1306.0543 · Cited by 853 · see citing works
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones
Micucci, D., Mobilio, M., & Napoletano, P. (2017) · Applied Sciences · doi:10.3390/app7101101 · Cited by 596 · see citing works
Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information
Li, Q., Stankovic, J. A., Hanson, M., Barth, A. T., Lach, J., & Zhou, G. (2009) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2009.46 · Cited by 588 · see citing works
SisFall: A Fall and Movement Dataset
Sucerquia, A., López, J., & Vargas-Bonilla, J. (2017) · Sensors · doi:10.3390/s17010198 · Cited by 530 · see citing works
A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor
Bourke, A. K., & Lyons, G. M. (2007) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2006.12.001 · Cited by 510 · see citing works
A smartphone-based fall detection system
Abbate, S., Avvenuti, M., Bonatesta, F., Cola, G., Corsini, P., & Vecchio, A. (2012) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2012.08.003 · Cited by 358 · see citing works
Fleming, J., & Brayne, C. (2008) · BMJ · doi:10.1136/bmj.a2227 · Cited by 324 · see citing works
Fixed-point feedforward deep neural network design using weights +1, 0, and −1
Hwang, K., & Sung, W. (2014) · SiPS ... design and implementation - IEEE Workshop on Signal Processing Systems · doi:10.1109/sips.2014.6986082 · Cited by 258 · see citing works
Fixed point optimization of deep convolutional neural networks for object recognition
Anwar, S., Hwang, K., & Sung, W. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178146 · Cited by 246 · see citing works
SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning
Mauldin, T. R., Canby, M. E., Metsis, V., Ngu, A., & Rivera, C. (2018) · Italian National Conference on Sensors · doi:10.3390/s18103363 · Cited by 236 · see citing works
Smartphone-Based Solutions for Fall Detection and Prevention: Challenges and Open Issues
Habib, M. A., Mohktar, M. S., Kamaruzzaman, S. B., Lim, K. S., Pin, T., & Ibrahim, F. (2014) · Sensors · doi:10.3390/s140407181 · Cited by 219 · see citing works
Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry
Aicha, A. N., Englebienne, G., van Schooten, K. S., Pijnappels, M. A. G. M., & Kröse, B. (2018) · Sensors · doi:10.3390/s18051654 · Cited by 208 · see citing works
UMAFall: A Multisensor Dataset for the Research on Automatic Fall Detection
Casilari, E., A.Santoyo-Ramón, J., & Cano, J. M. B. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.06.110 · Cited by 190 · see citing works
New Advances and Challenges of Fall Detection Systems: A Survey
Xu, T., Zhou, Y., & Zhu, J. H. (2018) · Applied Sciences · doi:10.3390/app8030418 · Cited by 181 · see citing works
Detecting Falls as Novelties in Acceleration Patterns Acquired with Smartphones
Medrano, C., Igual, R., Plaza, I., & Castro, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094811 · Cited by 176 · see citing works
Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions
Rajagopalan, R., Litvan, I., & Jung, T. (2017) · Sensors · doi:10.3390/s17112509 · Cited by 176 · see citing works
Energy efficient wearable sensor node for IoT-based fall detection systems
Nguyen Gia, T., Sarker, V. K., Tcarenko, I., Rahmani, A. M., Westerlund, T., Liljeberg, P., & Tenhunen, H. (2018) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2017.10.014 · Cited by 146 · see citing works
Analysis of Public Datasets for Wearable Fall Detection Systems
Casilari, E., A.Santoyo-Ramón, J., & Cano, J. M. B. (2017) · Sensors · doi:10.3390/s17071513 · Cited by 140 · see citing works
Improving Fall Detection Using an On-Wrist Wearable Accelerometer
Khojasteh, S. B., Villar, J. R., Chira, C., González, V. M., & de la Cal, E. (2018) · Sensors · doi:10.3390/s18051350 · Cited by 140 · see citing works
Optimization of an Accelerometer and Gyroscope-Based Fall Detection Algorithm
Huynh, Q. T., Nguyen, U. D., Irazabal, L. B., Ghassemian, N., & Tran, B. Q. (2015) · Journal of Sensors · doi:10.1155/2015/452078 · Cited by 134 · see citing works
Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer
Sucerquia, A., López, J. D., & Vargas‐Bonilla, J. F. (2018) · Sensors · doi:10.3390/s18041101 · Cited by 120 · see citing works
Multiple comparator classifier framework for accelerometer-based fall detection and diagnostic
Gibson, R. M., Amira, A., Ramzan, N., Casaseca‐de‐la‐Higuera, P., & Pervez, Z. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.10.062 · Cited by 107 · see citing works
Mobile activity recognition and fall detection system for elderly people using Ameva algorithm
de la Concepción, M. Á. Á., Morillo, L. M. S., Álvarez-García, J. A., & Abril, L. G. (2016) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2016.05.002 · Cited by 103 · see citing works
A Combined Smartphone and Smartwatch Fall Detection System
Vilarinho, T., Farshchian, B. A., Bajer, D. G., Dahl, O. H., Egge, I., Hegdal, S. S., Lones, A., Slettevold, J. N., & Weggersen, S. M. (2015) · 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing · doi:10.1109/cit/iucc/dasc/picom.2015.216 · Cited by 98 · see citing works
Wearable Fall Detector Using Recurrent Neural Networks
Luna-Perejón, F., Domínguez-Morales, M., & Balcells, A. (2019) · Sensors · doi:10.3390/s19224885 · Cited by 94 · see citing works
Bayesian recognition of motion related activities with inertial sensors
Frank, K., Nadales, M. J. V., Robertson, P., & Pfeifer, T. A. (2010) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/1864431.1864480 · Cited by 88 · see citing works
Vavoulas, G., Pediaditis, M., Chatzaki, C., Spanakis, E. G., & Tsiknakis, M. (2014) · International Journal of Monitoring and Surveillance Technologies Research · doi:10.4018/ijmstr.2014010103 · Cited by 82 · see citing works
Analysis of Android Device-Based Solutions for Fall Detection
Casilari, E., Luque, R., & Morón, M. J. (2015) · Sensors · doi:10.3390/s150817827 · Cited by 81 · see citing works
Fall Detection using Ceiling-mounted 3D Depth Camera
Kępski, M., & Kwolek, B. (2014) · International Conference on Computer Vision Theory and Applications · doi:10.5220/0004742406400647 · Cited by 73 · see citing works
A smartphone-based fall detection system for the elderly
Tsinganos, P., & Skodras, A. N. (2017) · International symposium on image and signal processing and analysis/ISPA ... · doi:10.1109/ispa.2017.8073568 · Cited by 68 · see citing works
Embedded Real-Time Fall Detection with Deep Learning on Wearable Devices
Torti, E., Fontanella, A., Musci, M., Blago, N., Pau, D. P., Leporati, F., & Piastra, M. (2018) · Euromicro Conference on Digital System Design (DSD) · doi:10.1109/dsd.2018.00075 · Cited by 65 · see citing works
Self-citationLow-power technologies for wearable telecare and telehealth systems: A review
Wang, C., Lü, W., Narayanan, M. R., Redmond, S. J., & Lovell, N. H. (2015) · Biomedical Engineering Letters · doi:10.1007/s13534-015-0174-2 · Cited by 57 · see citing works
Embedding Recurrent Neural Networks in Wearable Systems for Real-Time Fall Detection
Torti, E., Fontanella, A., Musci, M., Blago, N., Pau, D. P., Leporati, F., & Piastra, M. (2019) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2019.102895 · Cited by 47 · see citing works
Self-citationFall detection using supervised machine learning algorithms: A comparative study
Zerrouki, N., Harrou, F., Houacine, A., & Sun, Y. (2016) · International Conference on Modelling, Identification and Control · doi:10.1109/icmic.2016.7804195 · Cited by 44 · see citing works
A multi-sensor approach for fall risk prediction and prevention in elderly
Majumder, A. J. A., Zerin, I., Ahamed, S. I., & Smith, R. O. (2014) · ACM SIGAPP Applied Computing Review · doi:10.1145/2600617.2600621 · Cited by 35 · see citing works
An artificial neural network–based fall detection
Yoo, S., & Oh, D. (2018) · International Journal of Engineering Business Management · doi:10.1177/1847979018787905 · Cited by 33 · see citing works
Fall Detection Using Commodity Smart Watch and Smart Phone
Maglogiannis, I. G., Ioannou, C., Spyroglou, G., & Tsanakas, P. D. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44654-6_7 · Cited by 31 · see citing works
Fall Detection with Supervised Machine Learning using Wearable Sensors
Giuffrida, D., Benetti, G., Martini, D. D., & Facchinetti, T. (2019) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin41052.2019.8972246 · Cited by 26 · see citing works
Self-citationGonzález-Cañete, F. J., & Casilari, E. (2020) · Sensors · doi:10.3390/s20030622 · Cited by 21 · see citing works
Fall Detection Systems for Elderly Care: A Survey
Mohamed, O., Choi, H., & Iraqi, Y. (2014) · International Conference on New Technologies, Mobility and Security (NTMS) · doi:10.1109/ntms.2014.6814018 · Cited by 20 · see citing works
Improving the accuracy of existing camera based fall detection algorithms through late fusion
Baldewijns, G., Debard, G., Mertes, G., Croonenborghs, T., & Vanrumste, B. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037406 · Cited by 16 · see citing works
Triaxial accelerometer-based real time fall event detection
Hou, Y., Li, N., & Huang, Z. (2012) · International Conference on Information Society · Cited by 16 · see citing works
Deep Recurrent Neural Networks for Edge Monitoring of Personal Risk and Warning Situations
Torti, E., Musci, M., Guareschi, F., Leporati, F., & Piastra, M. (2019) · Scientific Programming · doi:10.1155/2019/9135196 · Cited by 11 · see citing works
Self-citation
Flagged first, then by citations
1 of the 56 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- P. Feng, M. Yu, S. M. Naqvi, and J. A. Chambers, "Deep learning for posture analysis in fall detection," in 2014 19th International Conference on Digital Signal Processing, Aug 2014, pp. 12-17.
Cited by
Sorted by citations
Queralta, J. P., Gia, T. N., Tenhunen, H., & Westerlund, T. (2019) · International Conference on Telecommunications and Signal Processing (TSP) · doi:10.1109/tsp.2019.8768883 · Cited by 154 · see citing works
Yu, X., Jang, J., & Xiong, S. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.692865 · Cited by 134 · see citing works
Intelligent Wearable Systems: Opportunities and Challenges in Health and Sports
Yang, L., Amin, O., & Shihada, B. (2024) · ACM Computing Surveys · doi:10.1145/3648469 · Cited by 145 · see citing works
Jain, R., & Semwal, V. B. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3213814 · Cited by 118 · see citing works
Casilari, E., Lora-Rivera, R., & García-Lagos, F. (2020) · Sensors · doi:10.3390/s20051466 · Cited by 102 · see citing works
Wearable Fall Detector Using Recurrent Neural Networks
Luna-Perejón, F., Domínguez-Morales, M., & Balcells, A. (2019) · Sensors · doi:10.3390/s19224885 · Cited by 94 · see citing works
EEG-Based Alzheimer’s Disease Recognition Using Robust-PCA and LSTM Recurrent Neural Network
Alessandrini, M., Biagetti, G., Crippa, P., Falaschetti, L., Luzzi, S., & Turchetti, C. (2022) · Sensors · doi:10.3390/s22103696 · Cited by 89 · see citing works
Wearables and Deep Learning Classify Fall Risk From Gait in Multiple Sclerosis
Meyer, B. M., Tulipani, L. J., Gurchiek, R. D., Allen, D. A., Adamowicz, L., Larie, D., Solomon, A. J., Cheney, N., & McGinnis, R. S. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3025049 · Cited by 84 · see citing works
AI-Reinforced Wearable Sensors and Intelligent Point-of-Care Tests
Yammouri, G., & Lahcen, A. A. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14111088 · Cited by 89 · see citing works
Diab, M. S., & Rodriguez–Villegas, E. (2022) · IEEE Access · doi:10.1109/access.2022.3206782 · Cited by 80 · see citing works
FEEL: FEderated LEarning Framework for ELderly Healthcare Using Edge-IoMT
Ghosh, S., & Ghosh, S. K. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3233300 · Cited by 80 · see citing works
Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls
Palmerini, L., Klenk, J., Becker, C., & Chiari, L. (2020) · Sensors · doi:10.3390/s20226479 · Cited by 73 · see citing works
Alessandrini, M., Biagetti, G., Crippa, P., Falaschetti, L., & Turchetti, C. (2021) · Electronics · doi:10.3390/electronics10141715 · Cited by 69 · see citing works
Embedded Real-Time Fall Detection with Deep Learning on Wearable Devices
Torti, E., Fontanella, A., Musci, M., Blago, N., Pau, D. P., Leporati, F., & Piastra, M. (2018) · Euromicro Conference on Digital System Design (DSD) · doi:10.1109/dsd.2018.00075 · Cited by 65 · see citing works
Self-citationApplying deep learning technology for automatic fall detection using mobile sensors
Wu, X., Zheng, Y., Chu, C., Cheng, L., & Kim, J. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103355 · Cited by 60 · see citing works
Zurbuchen, N., Wilde, A., & Bruegger, P. (2021) · Sensors · doi:10.3390/s21030938 · Cited by 60 · see citing works
NT-FDS—A Noise Tolerant Fall Detection System Using Deep Learning on Wearable Devices
Waheed, M., Afzal, H., & Mehmood, K. (2021) · Sensors · doi:10.3390/s21062006 · Cited by 60 · see citing works
Jaén-Vargas, M., Leiva, K. M. R., Fernandes, F., Gonçalves, S. B., Silva, M. T., Lopes, D. S., & Olmedo, J. J. S. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.1052 · Cited by 60 · see citing works
A practical wearable fall detection system based on tiny convolutional neural networks
Yu, X., Park, S., Kim, D., Kim, E., Kim, J., Kim, W., An, Y., & Xiong, S. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105325 · Cited by 58 · see citing works
A Novel Embedded Deep Learning Wearable Sensor for Fall Detection
Campanella, S., Alnasef, A., Falaschetti, L., Belli, A., Pierleoni, P., & Palma, L. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3375603 · Cited by 57 · see citing works
A cross-dataset deep learning-based classifier for people fall detection and identification
Delgado-Escaño, R., Castro, F. M., Cózar, J. R., Marín-Jiménez, M. J., Guil, N., & Casilari, E. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105265 · Cited by 57 · see citing works
Vision Based Real Time Monitoring System for Elderly Fall Event Detection Using Deep Learning
Anitha, G. S., & Priya, S. B. (2021) · Computer Systems Science and Engineering · doi:10.32604/csse.2022.020361 · Cited by 53 · see citing works
A review of wearable sensors based fall-related recognition systems
Liu, J., Li, X., Huang, S., Chao, R., Chao, R., Wang, S., Wang, S., & Liu, L. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105993 · Cited by 50 · see citing works
Embedding Recurrent Neural Networks in Wearable Systems for Real-Time Fall Detection
Torti, E., Fontanella, A., Musci, M., Blago, N., Pau, D. P., Leporati, F., & Piastra, M. (2019) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2019.102895 · Cited by 47 · see citing works
Self-citationNho, Y., Ryu, S., & Kwon, D. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3054394 · Cited by 47 · see citing works
Kraft, D., Srinivasan, K. K., & Bieber, G. (2020) · Technologies · doi:10.3390/technologies8040072 · Cited by 47 · see citing works
FallNeXt: A Deep Residual Model based on Multi-Branch Aggregation for Sensor-based Fall Detection
Mekruksavanich, S., & Jitpattanakul, A. (2022) · ECTI Transactions on Computer and Information Technology (ECTI-CIT) · doi:10.37936/ecti-cit.2022164.248156 · Cited by 43 · see citing works
Bhavani, K. D., & Ukrit, M. F. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16476-6 · Cited by 42 · see citing works
Mohammad, Z., Anwary, A. R., Mridha, M. F., Shovon, M. S. H., & Vassallo, M. (2023) · Sensors · doi:10.3390/s23104774 · Cited by 43 · see citing works
Fall Detection with CNN-Casual LSTM Network
Wu, J. Y., Wang, J., Zhan, A., & Wu, C. (2021) · Information · doi:10.3390/info12100403 · Cited by 39 · see citing works
Friedrich, B., Lau, S., Elgert, L., Bauer, J. M., & Hein, A. (2021) · Healthcare · doi:10.3390/healthcare9020149 · Cited by 37 · see citing works
Barshan, B., & Turan, M. Ş. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3280060 · Cited by 36 · see citing works
Ultra low-power, wearable, accelerated shallow-learning fall detection for elderly at-risk persons
Tian, J., Mercier, P. P., & Paolini, C. P. (2024) · Smart Health · doi:10.1016/j.smhl.2024.100498 · Cited by 36 · see citing works
Li, J., Gu, X., Qiu, S., Zhou, X., Cangelosi, A., Loo, C. K., & Liu, X. (2024) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2024.3369071 · Cited by 35 · see citing works
A Comparison of Machine Learning Algorithms for Fall Detection using Wearable Sensors
Zurbuchen, N., Bruegger, P., & Wilde, A. (2020) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic48513.2020.9065205 · Cited by 34 · see citing works
Jahanjoo, A., Naderan, M., & Rashti, M. J. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-01690-z · Cited by 33 · see citing works
Alizadeh, J., Bogdan, M., Claßen, J., & Fricke, C. (2021) · Sensors · doi:10.3390/s21217166 · Cited by 33 · see citing works
Patch-Transformer Network: A Wearable-Sensor-Based Fall Detection Method
Wang, S., & Wu, J. Y. (2023) · Sensors · doi:10.3390/s23146360 · Cited by 32 · see citing works
Syed, A. S., Sierra-Sosa, D., Kumar, A., & Elmaghraby, A. S. (2022) · Sensors · doi:10.3390/s22072547 · Cited by 31 · see citing works
Edge Computing Transformers for Fall Detection in Older Adults
Ruiz, J. F.-B., del Rincón, J. M., Dorado, J., del Toro, X., Santofimia, M. J., & López, J. C. (2024) · International Journal of Neural Systems · doi:10.1142/s0129065724500266 · Cited by 29 · see citing works
Luna-Perejón, F., Muñoz-Saavedra, L., Civit-Masot, J., Civit, A., & Domínguez-Morales, M. (2021) · Sensors · doi:10.3390/s21051889 · Cited by 29 · see citing works
A hybrid CNN-LSTM model for involuntary fall detection using wrist-worn sensors
Hu, X., Yu, S., Zheng, J., Fang, Z., Zhao, Z., & Qu, X. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103178 · Cited by 30 · see citing works
Semantic segmentation-based system for fall detection and post-fall posture classification
Mobsite, S., Alaoui, N., Boulmalf, M., & Ghogho, M. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105616 · Cited by 29 · see citing works
Unveiling Fall Origins: Leveraging Wearable Sensors to Detect Pre-Impact Fall Causes
Kiran, S., Riaz, Q., Hussain, M., Zeeshan, M., & Krüger, B. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3407835 · Cited by 26 · see citing works
Fall Detection with Supervised Machine Learning using Wearable Sensors
Giuffrida, D., Benetti, G., Martini, D. D., & Facchinetti, T. (2019) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin41052.2019.8972246 · Cited by 26 · see citing works
Self-citationSaleh, M., Abbas, M., Prud’homm, J., Somme, D., & Jeannès, R. L. B. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3133616 · Cited by 26 · see citing works
Multi-sensor-based Action Monitoring and Recognition via Hybrid Descriptors and Logistic Regression
Hafeez, S., Alotaibi, S. S., Alazeb, A., Mudawi, N. A., & Kim, W. (2023) · IEEE Access · doi:10.1109/access.2023.3275733 · Cited by 24 · see citing works
Demrozi, F., Turetta, C., Kindt, P. H., Chiarani, F., Bacchin, R. A., Valè, N., Pascucci, F., Cesari, P., Smania, N., Tamburin, S., et al. (2023) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2023.3274189 · Cited by 25 · see citing works
Fall Detector Adapted to Nursing Home Needs through an Optical-Flow based CNN
CARLIER, A., PEYRAMAURE, P., Favre, K., & Pressigout, M. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175844 · Cited by 22 · see citing works
Fall Detection for Shipboard Seafarers Based on Optimized BlazePose and LSTM
Liu, W., Liu, X., Hu, Y., Shi, J., Chen, X., Zhao, J., Wang, S., & Hu, Q. (2022) · Sensors · doi:10.3390/s22145449 · Cited by 22 · see citing works
Next-generation fall detection: harnessing human pose estimation and transformer technology
Sykes, E. R. (2024) · Health Systems · doi:10.1080/20476965.2024.2395574 · Cited by 20 · see citing works
Li, A., Bodanese, E., Poslad, S., Huang, Z., Hou, T., Wu, K., & Luo, F. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3290421 · Cited by 20 · see citing works
FallDeF5: A Fall Detection Framework Using 5G-Based Deep Gated Recurrent Unit Networks
Al-Rakhami, M. S., Gumaei, A., Altaf, M. M., Hassan, M. M., Alkhamees, B. F., Muhammad, K., & Fortino, G. (2021) · IEEE Access · doi:10.1109/access.2021.3091838 · Cited by 19 · see citing works
Yu, X., Koo, B., Jang, J., Kim, Y., & Xiong, S. (2022) · Measurement · doi:10.1016/j.measurement.2022.111785 · Cited by 20 · see citing works
A novel semi-supervised model for pre-impact fall detection with limited fall data
Yu, X., Wan, J., An, G., Yin, X., & Xiong, S. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108469 · Cited by 18 · see citing works
Falaschetti, L., Biagetti, G., Alessandrini, M., Turchetti, C., Luzzi, S., & Crippa, P. (2024) · Sensors · doi:10.3390/s24206721 · Cited by 18 · see citing works
Real-time Threshold-Based Fall Detection System Using Wearable IoT
Amir, N. I. M., Dziyauddin, R. A., Mohamed, N., Ismail, N. S. N., Zulkifli, N. S. A., & Din, N. M. (2022) · International Conference on Smart Sensors and Application (ICSSA) · doi:10.1109/icssa54161.2022.9870974 · Cited by 18 · see citing works
EEG-Based Neurodegenerative Disease Classification using LSTM Neural Networks
Alessandrini, M., Biagetti, G., Crippa, P., Falaschetti, L., Luzzi, S., & Turchetti, C. (2023) · IEEE Statistical Signal Processing Workshop (SSP) · doi:10.1109/ssp53291.2023.10208023 · Cited by 15 · see citing works
Wearable Fall Detectors Based on Low Power Transmission Systems: A Systematic Review
Villa, M., & Casilari, E. (2024) · Technologies · doi:10.3390/technologies12090166 · Cited by 15 · see citing works
Al‐qaness, M. A. A., Jiang, Z., & Shen, J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3508877 · Cited by 15 · see citing works
Research on CNN-BiLSTM Fall Detection Algorithm Based on Improved Attention Mechanism
Li, C., Liu, M., Yan, X., & Teng, G. (2022) · Applied Sciences · doi:10.3390/app12199671 · Cited by 13 · see citing works
Benoit, A., Escriba, C., Gauchard, D., Estève, A., & Rossi, C. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3364249 · Cited by 14 · see citing works
Real-Time AI-Driven Fall Detection Method for Occupational Health and Safety
Danilenka, A., Sowiński, P., Rachwał, K., Bogacka, K., Dabrowska, A. K., Kobus, M., Baszczyński, K., Okrasa, M., Olczak, W., Dymarski, P., et al. (2023) · Electronics · doi:10.3390/electronics12204257 · Cited by 13 · see citing works
Rodrigues, V. F., Antunes, R. S., Seewald, L. A., Bazo, R., dos Reis, E. S., dos Santos, U. J. L., Righi, R. D. R., da S., L. G., da Costa, C. A., Bertollo, F. L., et al. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.05.006 · Cited by 12 · see citing works
Continuous Wearable-based Fall Detection using Tiny Machine Learning
Cong, D. L., Quang, B. N., Minh, D. T., Cao, D. T., & Ngoc, M. N. (2024) · International Conference on Applying New Technology in Green Buildings (ATiGB) · doi:10.1109/atigb63471.2024.10717707 · Cited by 12 · see citing works
Evaluation of Feature Engineering on Wearable Sensor-based Fall Detection
Ramachandran, A., Ramesh, A., & Karuppiah, A. (2020) · International Conference on Information Networking · doi:10.1109/icoin48656.2020.9016479 · Cited by 12 · see citing works
Personalized Fall Detection System
Ngu, A. H. H., Metsis, V., Coyne, S., Chung, B., Pai, R., & Chang, J. (2020) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops48775.2020.9156172 · Cited by 11 · see citing works
A Smartphone Application for Fall Detection Using Accelerometer and ConvLSTM Network
Amara, M. I., Akkouche, A., Boutellaa, E., & Tayakout, H. (2021) · International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH) · doi:10.1109/ihsh51661.2021.9378743 · Cited by 10 · see citing works
Koo, B., Kim, J., Nam, Y., & Kim, Y. (2021) · Sensors · doi:10.3390/s21144638 · Cited by 9 · see citing works
Fall Detection System using Wearable Sensor Devices and Machine Learning: A ReviewPreprint
Amir, N. I. M., Dziyauddin, R. A., Mohamed, N., Ismail, N. S. N., Kaidi, H. M., Ahmad, N. B., & Izhar, M. A. M. (2024) · doi:10.36227/techrxiv.171084921.16728034/v1 · Cited by 9 · see citing works
Microcontroller Implementation of LSTM Neural Networks for Dynamic Hand Gesture Recognition
Leo, K. D., Biagetti, G., Falaschetti, L., & Crippa, P. (2025) · Sensors · doi:10.3390/s25123831 · Cited by 8 · see citing works
Change detection and convolution neural networks for fall recognition
Georgakopoulos, S. V., Tasoulis, S. K., Mallis, G. I., Vrahatis, A. G., Plagianakos, V. P., & Maglogiannis, I. G. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05208-8 · Cited by 8 · see citing works
Privacy-Enhancing Fall Detection from Remote Sensor Data Using Multi-Party Computation
Mainali, P., & Shepherd, C. (2019) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3339252.3340500 · Cited by 8 · see citing works
Detecting Falls with Recurrent Autoencoders and Body Acceleration Data
Boutellaa, E. (2019) · International Conference on Image and Signal Processing and their Applications (ISPA) · doi:10.1109/ispa48434.2019.8966875 · Cited by 8 · see citing works
Kong, V., Soeng, S., Thon, M., Cho, W.-S., Nayyar, A., & Kim, T.-K. (2025) · PLOS One · doi:10.1371/journal.pone.0325253 · Cited by 8 · see citing works
Advancing Human Fall Detection Through Motion Data from Smart Wearable Sensors and CNN-BiGRU Model
Mekruksavanich, S., Phaphan, W., & Jitpattanakul, A. (2024) · IEEE International Conference on Software Engineering and Service Science (ICSESS) · doi:10.1109/icsess62520.2024.10719052 · Cited by 7 · see citing works
Lee, H., Sohn, J., Lee, G., Jacobs, J. V., & Lee, S. H. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103413 · Cited by 7 · see citing works
Alessandrini, M., Falaschetti, L., Biagetti, G., Crippa, P., Luzzi, S., & Turchetti, C. (2023) · Sensors · doi:10.3390/s23198039 · Cited by 7 · see citing works
Using Deep Learning and Smartphone for Automatic Detection of Fall and Daily Activities
Wu, X., Cheng, L., Chu, C., & Kim, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34482-5_6 · Cited by 7 · see citing works
Fall Detection Using Transformer Model
Sharook, K. M., Premkumar, A., Aishwaryaa, R., Amrutha, J. M., & Deepthi, L. R. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-5331-6_4 · Cited by 6 · see citing works
LSTM Gate Disclosure as an Embedded AI Methodology for Wearable Fall-Detection Sensors
Correia, S. D., Roque, P. M., & Matos-Carvalho, J. P. (2024) · Symmetry · doi:10.3390/sym16101296 · Cited by 6 · see citing works
Yu, X., Jang, J., & Xiong, S. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-80713-9_36 · Cited by 6 · see citing works
Villa, M., & Casilari, E. (2025) · Sensors · doi:10.3390/s26010162 · Cited by 4 · see citing works
Combining Clustering Undersample and Ensemble Learning for Wearable Fall Detection
Zhang, M., & Gong, D. (2022) · IEEE International Conference on Real-time Computing and Robotics (RCAR) · doi:10.1109/rcar54675.2022.9872285 · Cited by 4 · see citing works
Single Camera based Real Time Framework for Automated Fall Detection
Soni, P. K., & Jain, P. (2022) · doi:10.1109/iccsea54677.2022.9936492 · Cited by 4 · see citing works
Dense & Attention Convolutional Neural Networks for Toe Walking Recognition
Chen, J., Soangra, R., Grant-Beuttler, M., Nanehkaran, Y. A., & Wen, Y. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3272362 · Cited by 4 · see citing works
Liu, X., & Zhang, H. (2025) · ChemistryOpen · doi:10.1002/open.202400394 · Cited by 4 · see citing works
de la Cal, E., Fáñez, M., Villar, M. A., Villar, J. R., & González, V. M. (2022) · Logic Journal of IGPL · doi:10.1093/jigpal/jzac025 · Cited by 4 · see citing works
Al-Majdi, K., AL-Musawi, R. S. H., Ali, A. H., Abudlmoniem, S. A., & Mezaal, Y. S. (2021) · Periodicals of Engineering and Natural Sciences (PEN) · doi:10.21533/pen.v9i3.2599 · Cited by 4 · see citing works
ProtoPLSTM: An Interpretable Deep Learning Approach for Wearable Fine-Grained Fall Detection
Gao, C., Zhang, T., Jiang, X., Huang, W., Chen, Y., & Li, J. (2022) · IEEE Smart World Congress (SWC) · doi:10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00091 · Cited by 4 · see citing works
Bhatti, F. A., Riaz, Q., & Krüger, B. (2025) · IEEE Access · doi:10.1109/access.2025.3641198 · Cited by 3 · see citing works
Deep Learning on Automatic Fall Detection
Monteiro, S., Leite, A., & Pires, E. J. S. (2021) · IEEE Latin American Conference on Computational Intelligence (LA-CCI) · doi:10.1109/la-cci48322.2021.9769783 · Cited by 3 · see citing works
Nehary, E. A., Dey, A., & Rajan, S. (2023) · International Conference on Consumer Electronics · doi:10.1109/icce56470.2023.10043463 · Cited by 3 · see citing works
Hu, X., Zhang, W., Ou, H., Mo, S., Liang, F., Liu, J., Zhao, Z., & Qu, X. (2024) · Sports Biomechanics · doi:10.1080/14763141.2024.2315243 · Cited by 3 · see citing works
MCTN: A Multi-Channel Temporal Network for Wearable Fall Prediction
Liu, J., Li, X., Liao, G., Wang, S., & Liu, L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43427-3_24 · Cited by 3 · see citing works
Optimal Wearable Sensor Placement for Human Fall Detection Using Machine Learning Models
Vishwakarma, S., Pathak, Y., & Kumar, V. (2025) · International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3) · doi:10.1109/ic2e365635.2025.11167570 · Cited by 2 · see citing works
A BiLSTM-Attention Model for Fall Event Prediction
Sun, X., Chen, X., Chen, H., & Han, Z. (2025) · International Conference on Mechatronics, Robotics, and Artificial Intelligence (MRAI) · doi:10.1109/mrai65197.2025.11135822 · Cited by 2 · see citing works
Fall Prediction by a Spatio-Temporal Multi-Channel Causal Model from Wearable Sensors Data
Liao, G., Liu, J., Liang, Y., Wang, S., & Liu, L. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447297 · Cited by 2 · see citing works
Fall-KAN: Fall impact time estimation Kolmogorov-Arnold Network
Cartocci, N., Gkikakis, A. E., Pera, F., Settino, M. T., Caldwell, D. G., & Ortiz, J. (2024) · International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) · doi:10.1109/iceccme62383.2024.10796549 · Cited by 2 · see citing works
Review on Machine learning and its application in Atmospheric science and Human Behavior Recognition
Chen, H., Jiang, M., Liu, Y., He, J., & Li, H. (2020) · doi:10.1145/3432291.3432311 · Cited by 2 · see citing works
- Authors
- Musci, M., De Martini, D., Blago, N., Facchinetti, T., & Piastra, M.
- Affiliations
- University of Pavia (IT)
- Venue
- IEEE Transactions on Emerging Topics in Computing
- Published
- 2021-07-01
- Volume
- 9
- Issue
- 3
- Pages
- 1276-1289
- ISSN
- 2168-6750 (ISSN-L)
- 2376-4562
- OpenAlex
- W2797186212
- Cited by
- 128
- References
- 56
- Citations by year
- 2026: 7 · 2025: 17 · 2024: 25 · 2023: 19 · 2022: 21 · 2021: 19 · 2020: 11 · 2019: 8 · 2018: 1
- FWCI
- 6.80
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Emerging Topics in Computing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Funders
- Regione Lombardia (139625)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
- Keywords
- fall detection, wearable sensors, recurrent neural networks, embedded deep learning, microcontroller, SisFall dataset, temporal annotation, model compression, hyperparameter optimization, software architecture
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
