A Novel Monitoring System for Fall Detection in Older People

Taramasco, Carla; Rodenas, Tomás; Martínez, Felipe; Fuentes, Paola; Castro-Muñoz, Roberto; Olivares, Rodrigo; Albuquerque, Victor Hugo C. de; Demongeot, Jacques

doi:10.1109/access.2018.2861331

Cited by 94 publications

(72 citation statements)

References 28 publications

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“…Motion detector-based systems identify falls based on detection of motion within an ROI. As an example of environmental sensing-based systems, Taramasco et al [4] explain one which uses very-low-resolution thermal sensors located on two horizontal planes along the floor, for classifying falls.…”

Section: Fall Detection Systems: An Overviewmentioning

confidence: 99%

A Survey on Recent Advances in Wearable Fall Detection Systems

Ramachandran

Karuppiah

2020

BioMed Research International

108

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With advances in medicine and healthcare systems, the average life expectancy of human beings has increased to more than 80 yrs. As a result, the demographic old-age dependency ratio (people aged 65 or above relative to those aged 15–64) is expected to increase, by 2060, from ∼28% to ∼50% in the European Union and from ∼33% to ∼45% in Asia (Ageing Report European Economy, 2015). Therefore, the percentage of people who need additional care is also expected to increase. For instance, per studies conducted by the National Program for Health Care of the Elderly (NPHCE), elderly population in India will increase to 12% of the national population by 2025 with 8%–10% requiring utmost care. Geriatric healthcare has gained a lot of prominence in recent years, with specific focus on fall detection systems (FDSs) because of their impact on public lives. According to a World Health Organization report, the frequency of falls increases with increase in age and frailty. Older people living in nursing homes fall more often than those living in the community and 40% of them experience recurrent falls (World Health Organization, 2007). Machine learning (ML) has found its application in geriatric healthcare systems, especially in FDSs. In this paper, we examine the requirements of a typical FDS. Then we present a survey of the recent work in the area of fall detection systems, with focus on the application of machine learning. We also analyze the challenges in FDS systems based on the literature survey.

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Section: Fall Detection Systems: An Overviewmentioning

confidence: 99%

A Survey on Recent Advances in Wearable Fall Detection Systems

Ramachandran

Karuppiah

2020

BioMed Research International

108

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“…Some researchers enhance the system based on the accelerometers by applying more wearable sensors. The systems integrate the sensors that are placed on different positions such as the waist, ankle, and chest on the subject of interest (SOI) with data mining techniques 8,27,29 . Shi et al 21 employ the wearable sensors including accelerometer, gyroscope, and magnetometer to place on 17 different positions for distinguishing the ADLs and falls.…”

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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“…And a staggering 37.3 million falls are severe enough to require medical attention each year. A wide range of factors contributes to fall risks, such as environmental hazards, illness, neurological, mental conditions, muscle weakness, poor vision, and medication [10,[20][21][22][23]. While there is no such thing as a 100% effective fall detection or prevention solution, most experts agree that a combination of available sensor technology can greatly increase system reliability.…”

Section: Related Workmentioning

confidence: 99%

“…While there is no such thing as a 100% effective fall detection or prevention solution, most experts agree that a combination of available sensor technology can greatly increase system reliability. And there is a growing acceptance by seniors as monitoring is a means to extend their independent living opportunities [21,23]. There is a system created and funded by European Agency: Ministry of industry, tourism and commerce, Spain.…”

Section: Related Workmentioning

confidence: 99%

Tele-Treatment Application Design for Disable Patients with Wireless Sensors

2020

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This paper consists of the development of a system to help patients with different disabilities, affected by rare or chronic diseases or any kind of dependence through tele assistance, virtual interaction and intelligent monitoring. The main goal is to increase the quality of life of the minorities who cannot take full advantage of the healthcare system by providing an alternative way of monitoring them with the technology embedded in this paper. The result of the paper is not intended to be a single solution, but a modular system that allows the construction of an application that is able to measure the needs of a health administration and the patients. The paper also pursues an educational training to the facultative trainees in a new way to approach patient treatments. It can improve the quality of life of the patients by saving them time and other resources in moving to the Health center and the professionals can also save time as they can take advantage of the online treatments by using the proposed system.

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A Novel Monitoring System for Fall Detection in Older People

Cited by 94 publications

References 28 publications

A Survey on Recent Advances in Wearable Fall Detection Systems

A Survey on Recent Advances in Wearable Fall Detection Systems

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

Tele-Treatment Application Design for Disable Patients with Wireless Sensors

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