A Novel Vision-Based Fall Detection Scheme Using Keypoints of Human Skeleton with Long Short-Term Memory Network

Inturi, Anitha Rani; Manikandan, V. M.; Garrapally, Vignesh

doi:10.1007/s13369-022-06684-x

Cited by 36 publications

(19 citation statements)

References 32 publications

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“…Based on this fact, it can be inferred that the proposed method is very effective. The method [ 38 ] employs CNN and LSTM on the UP-Fall dataset on the lateral camera, but compared with the proposed method, it has achieved lower sensitivity, specificity, and accuracy. Therefore, our proposed method can achieve similar or even better results than those works that use two or only one camera input to identify falls.…”

Section: Methodsmentioning

confidence: 99%

A Lightweight Subgraph-Based Deep Learning Approach for Fall Recognition

Zhao

Zhang

Shang

2022

Sensors

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Falls pose a great danger to social development, especially to the elderly population. When a fall occurs, the body’s center of gravity moves from a high position to a low position, and the magnitude of change varies among body parts. Most existing fall recognition methods based on deep learning have not yet considered the differences between the movement and the change in amplitude of each body part. Besides, some problems exist such as complicated design, slow detection speed, and lack of timeliness. To alleviate these problems, a lightweight subgraph-based deep learning method utilizing skeleton information for fall recognition is proposed in this paper. The skeleton information of the human body is extracted by OpenPose, and an end-to-end lightweight subgraph-based network is designed. Sub-graph division and sub-graph attention modules are introduced to add a larger perceptual field while maintaining its lightweight characteristics. A multi-scale temporal convolution module is also designed to extract and fuse multi-scale temporal features, which enriches the feature representation. The proposed method is evaluated on a partial fall dataset collected in NTU and on two public datasets, and outperforms existing methods. It indicates that the proposed method is accurate and lightweight, which means it is suitable for real-time detection and rapid response to falls.

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Section: Methodsmentioning

confidence: 99%

A Lightweight Subgraph-Based Deep Learning Approach for Fall Recognition

Zhao

Zhang

Shang

2022

Sensors

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“…In [32] the authors presented a solution for fall detection using vision-based methods. In this approach, they analyzed the human joint points, which are considered the main indicators of movement.…”

Section: Literature Reviewmentioning

confidence: 99%

The Effect of using Dimensionality Reduction Compared with Type of Algorithm on Detecting Patient Fall: Triage Case Study

Alshalawi,

Qadrouh,

Alhalabi

2024

Preprint

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Falling is one of the most critical outcomes of loss of consciousness during triage in emergency department (ED). It is an important sign requires an immediate medical intervention. This paper presents a computer vision-based fall detection model in ED. In this study, we hypothesis that the proposed vision-based triage fall detection model provides accuracy equal to traditional triage system (TTS) conducted by the nursing team. Thus, to build the proposed model, we use MoveNet, a pose estimation model that can identify joints related to falls, consisting of 17 key points. To test the hypothesis, we conducted two experiments: In the deep learning (DL) model we used the complete feature consisting of 17 keypoints which was passed to the triage fall detection model and was built using Artificial Neural Network (ANN). In the second model we use dimensionality reduction Feature-Reduction for Fall model (FRF), Random Forest (RF) feature selection analysis to filter the key points triage fall classifier. We tested the performance of the two models using a dataset consisting of many images for real-world scenarios classified into two classes: Fall and Not fall. We split the dataset into 80% for training and 20% for validation. The models in these experiments were trained to obtain the results and compare them with the reference model. To test the effectiveness of the model, a t-test was performed to evaluate the null hypothesis for both experiments. The results show FRF outperforms DL model, and FRF has same Accuracy of TTS.

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“…The system detects five types of falls and six types of daily living activities and achieves commendable results compared to the state-of-the-art approaches. The UP-FALL detection dataset is used for validation [ 14 ]. Baltabay et al outline a framework for gathering and analyzing sensory data from various sensors, such as ECG and inertial sensors, which are then transformed into images using unique preprocessing methods.…”

Section: Related Workmentioning

confidence: 99%

A wearable-based sports health monitoring system using CNN and LSTM with self-attentions

Wang,

Cui,

Fan

2023

PLoS ONE

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Sports performance and health monitoring are essential for athletes to maintain peak performance and avoid potential injuries. In this paper, we propose a sports health monitoring system that utilizes wearable devices, cloud computing, and deep learning to monitor the health status of sports persons. The system consists of a wearable device that collects various physiological parameters and a cloud server that contains a deep learning model to predict the sportsperson’s health status. The proposed model combines a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and self-attention mechanisms. The model is trained on a large dataset of sports persons’ physiological data and achieves an accuracy of 93%, specificity of 94%, precision of 95%, and an F1 score of 92%. The sports person can access the cloud server using their mobile phone to receive a report of their health status, which can be used to monitor their performance and make any necessary adjustments to their training or competition schedule.

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A Novel Vision-Based Fall Detection Scheme Using Keypoints of Human Skeleton with Long Short-Term Memory Network

Cited by 36 publications

References 32 publications

A Lightweight Subgraph-Based Deep Learning Approach for Fall Recognition

A Lightweight Subgraph-Based Deep Learning Approach for Fall Recognition

The Effect of using Dimensionality Reduction Compared with Type of Algorithm on Detecting Patient Fall: Triage Case Study

A wearable-based sports health monitoring system using CNN and LSTM with self-attentions

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