Fall detection using body geometry and human pose estimation in video sequences

Beddiar, Djamila Romaissa; Oussalah, Mourad; Nini, Brahim

doi:10.1016/j.jvcir.2021.103407

Cited by 28 publications

(3 citation statements)

References 33 publications

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“…The authors of [30] presented a solution, PRF-PIR, which integrates multimodal sensor fusion with passive and interpretable monitoring for long-term monitoring. The system comprised a software-defined radio (SDR) device and a novel passive infrared (PIR) sensor system.…”

Section: Explainable and Interpretable Fall Detectionmentioning

confidence: 99%

An Interpretable Modular Deep Learning Framework for Video-Based Fall Detection

Dutt,

Gupta,

Goodwin

et al. 2024

Applied Sciences

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Falls are a major risk factor for older adults, increasing morbidity and healthcare costs. Video-based fall-detection systems offer crucial real-time monitoring and assistance. Yet, their deployment faces challenges such as maintaining privacy, reducing false alarms, and providing understandable outputs for healthcare providers. This paper introduces an innovative automated fall-detection framework that includes a Gaussian blur module for privacy preservation, an OpenPose module for precise pose estimation, a short-time Fourier transform (STFT) module to capture frames with significant motion selectively, and a computationally efficient one-dimensional convolutional neural network (1D-CNN) classification module designed to classify these frames. Additionally, integrating a gradient-weighted class activation mapping (GradCAM) module enhances the system’s explainability by visually highlighting the movement of the key points, resulting in classification decisions. Modular flexibility in our system allows customization to meet specific privacy and monitoring needs, enabling the activation or deactivation of modules according to the operational requirements of different healthcare settings. This combination of STFT and 1D-CNN ensures fast and efficient processing, which is essential in healthcare environments where real-time response and accuracy are vital. We validated our approach across multiple datasets, including the Multiple Cameras Fall Dataset (MCFD), the UR fall dataset, and the NTU RGB+D Dataset, which demonstrates high accuracy in detecting falls and provides the interpretability of results.

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Section: Explainable and Interpretable Fall Detectionmentioning

confidence: 99%

An Interpretable Modular Deep Learning Framework for Video-Based Fall Detection

Dutt,

Gupta,

Goodwin

et al. 2024

Applied Sciences

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“…4) Computer vision: [43] produced the dataset by utilizing infrared array sensor temperature data. [44] made use of the Le2i FD, the URFD dataset, and the crossdataset.Using classified movies, a bespoke data set was created [45]. [46] created SimgFall, a signal-based picture dataset.…”

Section: Comparisonmentioning

confidence: 99%

Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review

Ishtiaq

Saeed

Khan

et al. 2022

JAREE

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Falls are a major public health concern among the elderly and the number of gadgets designed to detect them has increased significantly in recent years. This document provides a detailed summary of research done on fall detection systems, with comparisons across different types of studies. Its purpose is to be a resource for doctors and engineers who are planning or conducting field research. Following the examination, datasets, limitations, and future imperatives in fall detection were discussed in detail. The quantity of research using context-aware approaches continues to rise, but there is a new trend toward integrating fall detection into smartphones, as well as the use of artificial intelligence in the detection algorithm. Concerns with real-world performance, usability, and reliability are also highlighted.

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“…Working together in these conditions involves coordination that requires reasoning about how people pose and move (Maeda et al 2014;Mainprice, Rafi, and Berenson 2015;Mörtl et al 2012;Unhelkar et al 2015). Even without direct physical proximity, robots often need to think about a person's physical posture in applications such as sports coaching (Ross, Broz, and Baillie 2019) and elder care, which spans tasks from exercise engagement (Fasola and Matarić 2013) to fall detection (Faria et al 2015;Di et al 2013;Beddiar, Oussalah, and Nini 2022). In motion planning, robots need to further think about their own embodiment and posture to accomplish tasks; learning from human demonstrations via observation (Inamura, Nakamura, and Shimozaki 2002;Inamura, Toshima, and Nakamura 2002) has to map human motion into the robot's analogous configurations.…”

Section: Introductionmentioning

confidence: 99%

A Symbolic Representation of Human Posture for Interpretable Learning and Reasoning

Freedman¹,

Mueller²,

Ladwig³

et al. 2022

Preprint

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Robots that interact with humans in a physical space or application need to think about the person's posture, which typically comes from visual sensors like cameras and infra-red. Artificial intelligence and machine learning algorithms use information from these sensors either directly or after some level of symbolic abstraction, and the latter usually partitions the range of observed values to discretize the continuous signal data. Although these representations have been effective in a variety of algorithms with respect to accuracy and task completion, the underlying models are rarely interpretable, which also makes their outputs more difficult to explain to people who request them. Instead of focusing on the possible sensor values that are familiar to a machine, we introduce a qualitative spatial reasoning approach that describes the human posture in terms that are more familiar to people. This paper explores the derivation of our symbolic representation at two levels of detail and its preliminary use as features for interpretable activity recognition.

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Fall detection using body geometry and human pose estimation in video sequences

Cited by 28 publications

References 33 publications

An Interpretable Modular Deep Learning Framework for Video-Based Fall Detection

An Interpretable Modular Deep Learning Framework for Video-Based Fall Detection

Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review

A Symbolic Representation of Human Posture for Interpretable Learning and Reasoning

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