Artificial Neural Networks as an alternative to traditional fall detection methods

Vallejo, Marcela; Isaza, Claudia; López, José David

doi:10.1109/embc.2013.6609833

Cited by 40 publications

(28 citation statements)

References 10 publications

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“…Our system has been shown to be generic and works only on camera images, using few image samples (10) to determine the occurrence of a fall. Those features make the system an excellent candidate to be deployed in Smart Environments, which are not only limited to home scenarios.…”

Section: Discussionmentioning

confidence: 99%

“…In the case of falls, these measures are very different compared to daily activities or confounding events (such as bending over or squatting), allowing us to discern between them. Vallejo et al [10] and Sengto and Leauhatong [11] proposed feeding a Multilayer Perceptron (MLP), the data of a 3-axis accelerometer (acceleration values in -, -, and -axis). Kwolek and Kepski [12] applied an Inertial Measurement Unit (IMU) combined with the depth maps obtained from a Kinect camera.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Vision-Based Fall Detection with Convolutional Neural Networks

Núñez-Marcos

Azkune

Arganda‐Carreras

2017

Wireless Communications and Mobile Computing

220

125

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One of the biggest challenges in modern societies is the improvement of healthy aging and the support to older persons in their daily activities. In particular, given its social and economic impact, the automatic detection of falls has attracted considerable attention in the computer vision and pattern recognition communities. Although the approaches based on wearable sensors have provided high detection rates, some of the potential users are reluctant to wear them and thus their use is not yet normalized. As a consequence, alternative approaches such as vision-based methods have emerged. We firmly believe that the irruption of the Smart Environments and the Internet of Things paradigms, together with the increasing number of cameras in our daily environment, forms an optimal context for vision-based systems. Consequently, here we propose a vision-based solution using Convolutional Neural Networks to decide if a sequence of frames contains a person falling. To model the video motion and make the system scenario independent, we use optical flow images as input to the networks followed by a novel three-step training phase. Furthermore, our method is evaluated in three public datasets achieving the state-of-the-art results in all three of them.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Vision-Based Fall Detection with Convolutional Neural Networks

Núñez-Marcos

Azkune

Arganda‐Carreras

2017

Wireless Communications and Mobile Computing

220

125

View full text Add to dashboard Cite

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“…They are small in size and cheap; they can be easily placed in any part of the human body. The pelvis of a subject is a common location because the center of mass can be easily calculated [ 40 – 42 ]. Other frequent positions are the wrist and the ankle.…”

Section: Fall Detection Systemsmentioning

confidence: 99%

Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors

DeLaHoz

Labrador

2014

Sensors

267

128

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According to nihseniorhealth.gov (a website for older adults), falling represents a great threat as people get older, and providing mechanisms to detect and prevent falls is critical to improve people's lives. Over 1.6 million U.S. adults are treated for fall-related injuries in emergency rooms every year suffering fractures, loss of independence, and even death. It is clear then, that this problem must be addressed in a prompt manner, and the use of pervasive computing plays a key role to achieve this. Fall detection (FD) and fall prevention (FP) are research areas that have been active for over a decade, and they both strive for improving people's lives through the use of pervasive computing. This paper surveys the state of the art in FD and FP systems, including qualitative comparisons among various studies. It aims to serve as a point of reference for future research on the mentioned systems. A general description of FD and FP systems is provided, including the different types of sensors used in both approaches. Challenges and current solutions are presented and described in great detail. A 3-level taxonomy associated with the risk factors of a fall is proposed. Finally, cutting edge FD and FP systems are thoroughly reviewed and qualitatively compared, in terms of design issues and other parameters.

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“…Nowadays, some well-known machine learning algorithms such as k-nearest neighbor (kNN) 13 , support vector machines (SVM) 1 , decision tree 14 and artificial neural networks (ANNs) 3 have emerged as a promising method in fall-related researches 14,15 .…”

Section: List Of Figures Vmentioning

confidence: 99%

“…Most of the fall detection systems are designed by utilizing wearable devices 1,26,27 . The wearable devices are attached to clothes or part of the body of the SOIs for detecting the falls 1,3,9 . The sensors as the main components of wearable devices measure the characteristics of the movements of SOIs.…”

Section: Literature Reviewmentioning

confidence: 99%

Wearable Computing With Distributed Deep Learning Hierarchy: A Study of Fall Detection

et al. 2020

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Chapter 1. Introduction 1 Chapter 2. Literature Review 5 Chapter 3. System Overview 8 Data Acquisition and Processing 9 Distributed Hierarchical Deep Learning Chapter 4. Experiment and Results Experimental Setup Result and Analysis Chapter 5. Discussion Why to use resources on the cloud server? What are the requirements about the incoming data during the training process? Why to use paired smart insole rather than only one smart insole? Chapter 6. Conclusion Chapter 7. Suggested Future Research References iv List of Tables 4.1 The main specs of the mobile device's hardware. 4.2 The comparison experimental results of using smart insoles and wearable sensors placed on SOIs for fall detection,W-waist, H-hand, T-thigh, I-smart insole, with k-nearest neighbor (knn), support vector machines (svm), decision tree (dt), discriminant (dc),Multi-Layer Perceptron (mlp) and convolutional neural network (cnn). 4.3 The best results through the six algorithm studies. The combination represents the combination of smart Insole, smartwatch and smartphone. 4.4 Performance of two different neural networks CNN and MLP under two different parameter exchange protocols. 4.5 Running time and Running Memory usage. 5.1 The confusion matrix using CNN with only one smart insole as inputs. CM-confusion matrix, Fa-Falling, SD-Sit Down, Wa-Walking, BP-Bend to Pick up, St-Standing, Be-Bending, Si-Sitting, Ly-Lying, Sq-Squatting and SP-Squat to pick up 5.2 The confusion matrix using CNN with two smart insole as inputs. v List of Figures 3.1 This figure shows the fall detection system flowchart. The sensor data collected from the smartwatch, smartphone and smart insoles. All the data are sent to the smartphone simultaneously. The smartphone will combine all three sensor data and perform deep learning through the system. During the training process, each smartphone transmits the intermediate output and label to the cloud server which will calculate corresponding gradient-outputs ∇ n j L(w, b; x, y) and release it back to the smartphone. After finishing the training process, the smartphone will deliver the accumulated gradients to the cloud server, and the cloud server will update the consensus model according to these gradients. The updated weights will distribute to one or more smartphones which send the requests. 3.2 Overview of DHNN architecture. (a) is the traditional DNN architecture. (b) introduces HNN architecture including a single smartphone and the cloud server. And (c) illustrates DHNN architecture in which the system further extends to multiple mobile deices. 4.1 (i), (ii), (iii), (iv), (v) representing the changes of acceleration, angular velocity, and orientation on smartwatch and smartphone, as well as the moving trend of pressure center of the smart insoles when presenting five different kinds of fall movements. Plot (vi), (vii), (viii), (ix), (x) showing the changes when presenting five different ADLs. vi 4.2 The distributed hierarchical deep learning architecture used in the system evaluation. The blocks in orange and blue represen...

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Artificial Neural Networks as an alternative to traditional fall detection methods

Cited by 40 publications

References 10 publications

Vision-Based Fall Detection with Convolutional Neural Networks

Vision-Based Fall Detection with Convolutional Neural Networks

Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors

Wearable Computing With Distributed Deep Learning Hierarchy: A Study of Fall Detection

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