Fall Detection

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Overview

Fall detection studies how to recognize falls, distinguish them from routine activities such as lying down, and sometimes estimate impact or fall risk. Approaches include wearable IMU classifiers, camera-based pose and skeleton models, thermal and radar sensing, and multimodal systems, using methods such as CNNs, recurrent networks, graph models, and state-space models. Evaluation spans staged datasets, cross-dataset tests, and real-world deployments, with growing attention to edge-device speed, energy use, privacy, and personalized monitoring. A central challenge is that strong results on simulated or in-domain data may not transfer to genuine falls, scarce clinical data, or changing environments.

Latest papers

CardsList

20 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

  1. Asking a Multimodal LLM Whether Someone Fell: Zero-Shot Screening of Low-Resolution Thermal Video in a Laboratory and an Occupied Home

    Oct 21, 2026Marcos Junca, Mauro JuncaFall DetectionHealthcare

    Senecta Inc.

  2. FALLNEST: Dual-Domain Dataset Condensation of Imbalanced IMU Data for Wearable Fall Detection

    Oct 13, 2026Rashi Niyas P, Ritika Jain, Hitika TiwariFall DetectionDataset Condensation

    Indian Institute of Technology Madras · Indian Institute of Technology Guwahati

  3. A new design of a fall detection system integrating landmark identification and deep learning techniques

    Oct 5, 2026Tri Nhut Do, Thi Thuy LeFall DetectionHuman Activity Recognition

    University of Information Technology, Vietnam National University in Ho Chi Minh City, Quarter 34, Linh Xuan Ward, Ho Chi Minh City, Vietnam · Thu Dau Mot University, 6 Tran Van On Street, Phu Loi Ward, Ho Chi Minh City, Vietnam

  4. SAFER-Activities: A Dataset for Smart Assessment of Fall Events and Routine Activities

    Sep 7, 2026Diwas Lamsal, Pramod Wickramatilake, Jednipat Moonrinta +2Skeleton-Based Action RecognitionFall Detection

    KU Leuven, Leuven 3000, Belgium · Asian Institute of Technology, Khlong Luang, Pathum Thani 12120, Thailand

  5. Beyond Simulated Benchmarks: Evaluating Motion Representations for Fall Detection Under Real-World Data Scarcity

    Aug 13, 2026Timilehin B. Aderinola, Ilaria D'Ascanio, Luca Palmerini +5Fall DetectionRepresentation Learning

    School of Computer Science, University College Dublin (UCD), Ireland · Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Italy · Department of Clinical Gerontology, Robert Bosch Hospital, Stuttgart, Germany +2

  6. FLASH: Efficient Impact Fall Detection with Unified Hypergraph State-Space Model

    Jul 28, 2026Tresor Y. Koffi, Youssef Mourchid, Yohan DupuisFall DetectionHypergraph Neural Networks

    CESI, CESI LINEACT, France

  7. Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data

    Jul 28, 2026Tresor Y. Koffi, Youssef Mourchid, Mohammed Hindawi +1Fall Detection

    CESI, CESI LINEACT, Dijon, France. · ED 432, ENSAM, Paris, France. · CESI, CESI LINEACT, Lyon, France. +1

  8. Real-time fall detection based on vision for low-power edge platforms

    Jul 14, 2026Wenjun Xia, Zhicheng Peng, Haopeng Li +1Fall DetectionPhysics-Informed ML

    Jiangsu University, Zhenjiang, China

  9. From General Actions to Domain-Specific Monitoring: Prior-Adaptive Transfer for Skeleton-Based Action Recognition

    Jul 3, 2026Hao Wang, Di Yang, Jiangtao WangSkeleton-Based Action RecognitionDataset Pruning

    University of Science and Technology of China

  10. Stereo Vision-Based Fall Prediction and Detection using Human Pose Estimation on the AMD Kria K26 SOM

    Jun 10, 2026Shreyas Narasimhiah Ramesh, P. D. Rathika, Mahasweta Sarkar +3Fall DetectionOn-Device Inference

    Electrical and Computer Engineering, San Diego State University, 5500 Campanile Drive, San Diego, 92182-1309, California, USA · Department of Robotics and Automation Engineering, PSG College of Technology, Peelamedu, Coimbatore, 641004, Tamil Nadu, India · Psychology, San Diego State University, 5500 Campanile Drive, San Diego, 92182-4611, California, USA +1

  11. EM-Fall: Embodied mmWave Sensing for Day-and-Night Fall Detection on Humanoid Robots

    Jun 9, 2026Yanshuo Lu, Yuxuan Hu, Shenghai Yuan +5Fall DetectionmmWave Radar Sensing

    MARS Lab · NTU · IOT Lab

  12. You Don't Need Attention: Gated Convolutional Modeling for Watch-Based Fall Detection

    May 19, 2026Sana Alamgeer, Ronish Kumar, Awatif Yasmin +2Fall DetectionWearable Sensing

    1Texas State University, San Marcos, USA

  13. Integrating Anomaly Detection into Agentic AI for Proactive Risk Management in Human Activity

    Apr 21, 2026Farbod Zorriassatine, Ahmad LotfiFall DetectionAI Risk Management

    Department of Computer Science · Nottingham Trent University · Nottingham, UK

  14. TaFall: Balance-Informed Fall Detection via Passive Thermal Sensing

    Apr 6, 2026Chengxiao Li, Xie Zhang, Wei Zhu +2Fall Detection

    University of Hong Kong, China · West China Hospital, Sichuan University, China

  15. A Multi-Modal CNN-LSTM Framework with Multi-Head Attention and Focal Loss for Real-Time Elderly Fall Detection

    Mar 20, 2026Lijie Zhou, Luran WangFall DetectionWearable Sensing

    School of Computer Science, University of Nottingham Ningbo China, Ningbo, China · School of Mathematical Sciences, University of Nottingham Ningbo China, Ningbo, China

  16. Personalized Fall Detection by Balancing Data with Selective Feedback Using Contrastive Learning

    Mar 17, 2026Awatif Yasmin, Tarek Mahmud, Sana Alamgeer +1Fall DetectionClass-Imbalanced Learning

    Texas State University · Texas A&M University - Kingsville

  17. Edge-Efficient Two-Stream Multimodal Architecture for Non-Intrusive Bathroom Fall Detection

    Mar 17, 2026Haitian Wang, Yiren Wang, Xinyu Wang +2Fall DetectionMultimodal Sensor Fusion

    The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia

  18. Bridging the Visual-to-Physical Gap: Physically Aligned Representations for Fall Risk Analysis

    Mar 12, 2026Xianqi ZhangMotion Representation LearningClinical Risk Prediction

    Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China · Suzhou Research Institute, Harbin Institute of Technology, Suzhou 215104, China

  19. OmniFall: From Staged Through Synthetic to Wild, A Unified Multi-Domain Dataset for Robust Fall Detection

    May 26, 2025David Schneider, Zdravko Marinov, Moritz Mistol +6Synthetic-to-Real Domain AdaptationFall Detection

    Karlsruhe Institute of Technology · Mercedes-Benz Tech Innovation

  20. Pose-Based Fall Detection System: Efficient Monitoring on Standard CPUs

    Mar 25, 2025Vinayak Mali, Saurabh JaiswalFall DetectionHuman Activity Recognition

    Department of Artificial Intelligence Indian Institue of Technology Kharagpur