Synergistic Integration of Skeletal Kinematic Features for Vision-Based Fall Detection

Inturi, Anitha Rani; Manikandan, V. M.; Kumar, Mithilesh; Wang, Shuihua; Zhang, Yudong

doi:10.3390/s23146283

Cited by 6 publications

(2 citation statements)

References 42 publications

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“…The authors of [26] developed a vision-based fall-detection system focusing on skeletal kinematic features. Their proposed feature descriptor analyzes body geometry and dynamic patterns to distinguish falls from non-fall activities effectively.…”

Section: Fall Detection Based On Video Datasetsmentioning

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: Fall Detection Based On Video Datasetsmentioning

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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“…RF classifier is an integrated approach consisting of multiple decision trees that are independent of each other. Each decision tree processes samples and predicts output labels, and the final output of the model is determined by the class that receives the most votes from the individual trees ( 14 ). As RFs overcomes the common problem of over-fitting through the use of bootstrap aggregation, it appears to be more accurate in prediction than other algorithms ( 15 ).…”

Section: Introductionmentioning

confidence: 99%

Construction and validation of a joint diagnosis model based on random forest and artificial intelligence network for hepatitis B-related hepatocellular carcinoma

Jiang,

Hu,

Xie

2024

Transl Cancer Res

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Background Hepatitis B virus (HBV) is the dominant pathogenic factor of hepatocellular carcinoma (HCC) in Asia and Africa. Early identification and clinical diagnosis are crucial for HBV-related HCC. Random forest (RF) and artificial neural network (ANN) were an innovative and highly effective supervised machine learning (ML) algorithm for the early diagnosis and screening of HBV-related HCC. This study aims to identify significant biomarkers and develop a novel genetic model for the efficient diagnosis of HBV-related HCC. Methods Gene Expression Omnibus (GEO) Series (GSE)19665, GSE55092, and GSE121248 were used to identify significant differentially expressed genes (DEGs). The enrichment analysis was performed on Metascape online tool. The RF algorithm and ANN were used to select the potential predictive gene panels and construct an HBV-related HCC diagnostic model. Subsequently, GSE17548, GSE104310, GSE44074, and GSE136247 were used to test the accuracy of the ANN model. Finally, the CIBERSORT algorithm was used to assess the abundance of immune infiltrates in all samples. Results First, 116 genes were identified as DEGs, and the DEGs were particularly enriched in cellular hormone metabolic process, monocarboxylic acid metabolic process, NABA extracellular matrix (ECM) AFFILIATED steroid metabolic process and metabolism of bile acid and bile salt. DNA topoisomerase II alpha ( TOP2A ), C-type lectin domain family 1 member B ( CLEC1B ), BUB1 mitotic checkpoint serine/threonine kinase B ( BUB1B ), ficolin 2 ( FCN2 ), C-X-C motif chemokine ligand 14 ( CXCL14 ), cyclase associated actin cytoskeleton regulatory protein 2 ( CAP2 ), ficolin 3 ( FCN3 ), kynurenine 3-monooxygenase ( KMO ) and cadherin related family member 2 ( CDHR2 ) were available to develop an HBV-related HCC diagnostic model. After validation, the diagnostic model showed high sensitivity (88.5%, 90%, 88.5%, 76.5%) and specificity (100%, 81.8%, 89.5%, 72.2%), and the areas under the receiver operating characteristic (ROC) curves showed excellent efficiency (1, 0.927, 0.921, 0.833). Finally, the percentage of infiltrating immune cell types [B cells naïve, B cells memory, plasma cells, T cells CD8, T cells CD4 memory resting, T cells regulatory (Tregs), T cells gamma delta, natural killer (NK) cells resting, NK cells activated, Macrophages M0, Dendritic cells activated, Mast cells activated] for hepatitis B-related HCC were significantly different from that of non-cancerous liver tissue with HBV. Conclusions A novel early diagnostic model of HBV-related HCC was established, and the model showed better efficiency in distinguishing HBV-related HCC from other non-cancerous with HBV individuals.

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Deep learning for computer vision based activity recognition and fall detection of the elderly: a systematic review

Gaya-Morey,

Manresa-Yee,

Buades-Rubio

2024

Appl Intell

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As the proportion of elderly individuals in developed countries continues to rise globally, addressing their healthcare needs, particularly in preserving their autonomy, is of paramount concern. A growing body of research focuses on Ambient Assisted Living (AAL) systems, aimed at alleviating concerns related to the independent living of the elderly. This systematic review examines the literature pertaining to fall detection and Human Activity Recognition (HAR) for the elderly, two critical tasks for ensuring their safety when living alone. Specifically, this review emphasizes the utilization of Deep Learning (DL) approaches on computer vision data, reflecting current trends in the field. A comprehensive search yielded 2,616 works from five distinct sources, spanning the years 2019 to 2023 (inclusive). From this pool, 151 relevant works were selected for detailed analysis. The review scrutinizes the employed DL models, datasets, and hardware configurations, with particular emphasis on aspects such as privacy preservation and real-world deployment. The main contribution of this study lies in the synthesis of recent advancements in DL-based fall detection and HAR for the elderly, providing insights into the state-of-the-art techniques and identifying areas for further improvement. Given the increasing importance of AAL systems in enhancing the quality of life for the elderly, this review serves as a valuable resource for researchers, practitioners, and policymakers involved in developing and implementing such technologies. Graphical abstract

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Synergistic Integration of Skeletal Kinematic Features for Vision-Based Fall Detection

Cited by 6 publications

References 42 publications

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

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

Construction and validation of a joint diagnosis model based on random forest and artificial intelligence network for hepatitis B-related hepatocellular carcinoma

Deep learning for computer vision based activity recognition and fall detection of the elderly: a systematic review

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