An Elderly Fall Detection Method Based on Federated Learning and Extreme Learning Machine (Fed-ELM) by Zhigang Yu, Jiahui Liu, Mingchuan Yang, Yanmin Cheng, Jie Hu, Xinchi Li

Subjects: “…Fall detection…”
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Identify Subtle Fall Hazards Using Transfer Learning by Wen-Ta Hsiao, Wen-Der Yu, Chi-Yung Tang, Alexey Bulgakov

“…Computer vision is increasingly used for fall safety monitoring, but it struggles in subtle hazard scenarios, causing delays in hazard detection. …”
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Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique by Moritz Schneider, Kevin Seeser-Reich, Armin Fiedler, Udo Frese

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Deep learning-based fall detection using commodity Wi-Fi by Tingwei Chen, Xiaoyang Li, Hang Li, Guangxu Zhu

“…In this study, within practical experimental environments, we collect dataset and utilize deep learning method to classify the falling events.…”
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Automating tephra fall building damage assessment using deep learning by E. Tennant, S. F. Jenkins, V. Miller, R. Robertson, B. Wen, S.-H. Yun, B. Taisne

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Lessons learned from the rise and the fall of floral hemp production in Utah by Tina Sullivan, Matt Yost, Jessica Schad, Marion Murray

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Deep Transfer Learning Approach in Smartwatch-Based Fall Detection Systems by Alessandro Leone, Andrea Manni, Gabriele Rescio, Pietro Siciliano, Andrea Caroppo

Subjects: “…transfer learning…”
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Scenario-adaptive wireless fall detection system based on few-shot learning by Yuting ZENG, Suzhi BI, Lili ZHENG, Xiaohui LIN, Hui WANG

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A Multimodal Approach of Machine and Deep Learnings to Enhance the Fall of Elderly People by Saleh Al meraikhi, Murad Al-Rajab

Subjects: “…machine learning…”
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Using Machine Learning to Shorten and Adapt Fall Risk Assessments for Older Adults by Lilyana Khatib, Adi Toledano-Shubi, Hilla Sarig Bahat, Hagit Hel-Or

Subjects: “…fall risk assessment…”
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Federated Learning for Fall Detection With Multimodal Residual Fusion and Pareto-Optimized Client Selection by Bao-Quan Wang, Fan Yang, Yi Wang, Fan Zhao, Yun-Fei Han, Yu-Peng Ma

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Myoelectric signal and machine learning computing in gait pattern recognition for flat fall prediction by Shuo Zhang, Biao Chen, Chaoyang Chen, Maximillian Hovorka, Jin Qi, Jie Hu, Gui Yin, Marie Acosta, Ruby Bautista, Hussein F. Darwiche, Bryan E. Little, Carlos Palacio, John Hovorka

“…Abnormal gaits including pelvic obliquity gait and knee hyperextension gait are common clinical symptoms related to flat-ground fall among elder adults. This study aimed to determine the feasibility of using lower limb myoelectrical signals (electromyographic signals, EMG) for gait pattern recognition and to identify the optimal machine learning (ML) algorithms for EMG signal processing. …”
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Fall prediction in a quiet standing balance test via machine learning: Is it possible? by Juliana Pennone, Natasha Fioretto Aguero, Daniel Marczuk Martini, Luis Mochizuki, Alexandre Alarcon do Passo Suaide

“…They are considered the gold standard to indicate optimal mobility and performance individually, but their sensitivity and specificity are not good enough to predict who is at higher risk of falling. An innovative approach for fall prediction is the machine learning. …”
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Constructing a fall risk prediction model for hospitalized patients using machine learning by Cheng-Wei Kang, Zhao-Kui Yan, Jia-Liang Tian, Xiao-Bing Pu, Li-Xue Wu

Subjects: “…Accidental falls…”
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Advances in Skeleton-Based Fall Detection in RGB Videos: From Handcrafted to Deep Learning Approaches by Van-Ha Hoang, Jong Weon Lee, Md. Jalil Piran, Chun-Su Park

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Automatic Fall Risk Detection Based on Imbalanced Data by Yen-Hung Liu, Patrick C. K. Hung, Farkhund Iqbal, Benjamin C. M. Fung

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Detecting Falls and Slips of Wheelchair Users Using Low-Resolution Thermal Image Analysis by Shisei Nakamura, Masaaki Yamauchi, Miwa Sugita, Yoshihiro Aso, Yuichi Ohsita, Hideyuki Shimonishi

Subjects: “…Fall detection…”
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Smart Belay Device for Sport Climbing—An Analysis about Falling by Heiko Oppel, Michael Munz

“…Using an instrumented belay device, we recorded a climber’s fall into the rope with varying configurations. By combining this data with machine learning algorithms, we were able to categorize the recordings and extract information about the severity of the fall for the climber.…”
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Wearable sensor-based fall detection for elderly care using ensemble machine learning techniques by Ch Gangadhar, P Pavithra Roy, R. Dinesh Kumar, Janjhyam Venkata Naga Ramesh, S. Ravikanth, N. Akhila

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An Optical Flow- and Machine Learning-Based Fall Recognition Model for Stair Accessing Service Robots by Jun Hua Ong, Abdullah Aamir Hayat, Mohan Rajesh Elara, Kristin Lee Wood

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