scholarly journals Preimpact Fall Detection for Elderly Based on Fractional Domain

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Ning Liu ◽  
Dedi Zhang ◽  
Zhong Su ◽  
Tianrun Wang
Keyword(s):  
Social Cost ◽  
Detection System ◽  
Fall Detection ◽  
The Elderly ◽  
Selection Method ◽  
Excellent Performance ◽  
Motion Data ◽  
Algorithm Framework ◽  
Warning Effect ◽  
Mean Filtering

The aging population has become a growing worldwide problem. Every year, deaths and injuries caused by elderly people's falls bring huge social costs. To reduce the rate of injury and death caused by falls among the elderly and the following social cost, the elderly must be monitored. In this context, falls detecting has become a hotspot for many research institutions and enterprises at home and abroad. This paper proposes an algorithm framework to prealarm the fall based on fractional domain, using inertial data sensor as motion data collection devices, preprocessing the data by axis synthesis and mean filtering, and using fractional-order Fourier transform to convert the collected data from time domain to fractional domain. Based on the above, a multilayer dichotomy classifier is designed, and each node parameter selection method is given, which constructed a preimpact fall detection system with excellent performance. The experiment result demonstrates that the algorithm proposed in this paper can guarantee better warning effect and classification accuracy with fewer features.

scholarly journals Machine Learning based Human Fall Detection System

2021 ◽  
Vol 9 (VI) ◽  
pp. 2677-2682
Author(s):  
Nishanth P
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Wearable Sensors ◽  
Fall Detection ◽  
The Elderly ◽  
World Health ◽  
Support Vector ◽  
Data Output ◽  
Health Organization ◽  
Context Features

Falls have become one of the reasons for death. It is common among the elderly. According to World Health Organization (WHO), 3 out of 10 living alone elderly people of age 65 and more tend to fall. This rate may get higher in the upcoming years. In recent years, the safety of elderly residents alone has received increased attention in a number of countries. The fall detection system based on the wearable sensors has made its debut in response to the early indicator of detecting the fall and the usage of the IoT technology, but it has some drawbacks, including high infiltration, low accuracy, poor reliability. This work describes a fall detection that does not reliant on wearable sensors and is related on machine learning and image analysing in Python. The camera's high-frequency pictures are sent to the network, which uses the Convolutional Neural Network technique to identify the main points of the human. The Support Vector Machine technique uses the data output from the feature extraction to classify the fall. Relatives will be notified via mobile message. Rather than modelling individual activities, we use both motion and context information to recognize activities in a scene. This is based on the notion that actions that are spatially and temporally connected rarely occur alone and might serve as background for one another. We propose a hierarchical representation of action segments and activities using a two-layer random field model. The model allows for the simultaneous integration of motion and a variety of context features at multiple levels, as well as the automatic learning of statistics that represent the patterns of the features.

scholarly journals Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data

2017 ◽  
Vol 23 (3) ◽  
pp. 147 ◽  
Author(s):  
Moiz Ahmed ◽  
Nadeem Mehmood ◽  
Adnan Nadeem ◽  
Amir Mehmood ◽  
Kashif Rizwan
Keyword(s):  
Detection System ◽  
Fall Detection ◽  
The Elderly

scholarly journals IR-UWB Sensor Based Fall Detection Method Using CNN Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5948
Author(s):  
Taekjin Han ◽  
Wonho Kang ◽  
Gyunghyun Choi
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Privacy Preservation ◽  
Detection System ◽  
Fall Detection ◽  
The Elderly ◽  
Ultra Wideband ◽  
Detection Accuracy ◽  
Secondary Damage ◽  
Behavior Types

Falls are the leading cause of fatal injuries in the elderly such as fractures, and secondary damage from falls can lead to death. As such, fall detection is a crucial topic. However, due to the trade-off relationship between privacy preservation, user convenience, and fall detection performance, it is generally difficult to develop a fall detection system that simultaneously satisfies all conditions. The main goal of this study is to build a practical fall detection framework that can effectively classify the various behavior types into “Fall” and “Activities of daily living (ADL)” while securing privacy preservation and user convenience. For this purpose, signal data containing the motion information of objects was collected using a non-contact, unobtrusive, and non-restraint impulse-radio ultra wideband (IR-UWB) radar. These data were then applied to a convolutional neural network (CNN) algorithm to create an object behavior type classifier that can classify the behavior types of objects into “Fall” and “ADL.” The data were collected by actually performing various activities of daily living, including falling. The performance of the classifier yielded satisfactory results. By combining an IR-UWB and CNN algorithm, this study demonstrates the feasibility of building a practical fall detection system that exceeds a certain level of detection accuracy while also ensuring privacy preservation and user convenience.

Application of Deep Learning to Develop a Safety Confirmation System for the Elderly in a Nursing Home

2017 ◽  
Vol 29 (2) ◽  
pp. 338-345 ◽  
Author(s):  
Masaru Kawakami ◽  
◽  
Shogo Toba ◽  
Kohei Fukuda ◽  
Shinya Hori ◽  
...  
Keyword(s):  
Nursing Home ◽  
Detection System ◽  
Elderly Care ◽  
Elderly Person ◽  
Bed Rest ◽  
The Elderly ◽  
The Body ◽  
Body Motion ◽  
Motion Data ◽  
System A

[abstFig src='/00290002/07.jpg' width='210' text='The motion detection system' ] Fall accident prevention is one of the most important issues in elderly care settings. To prevent an accident, it is necessary to notify caregivers if the elderly person is getting out of bed. We have previously developed a posture discrimination system based on body motions. Herein, we propose a discrimination method by using machine learning to improve the performance of the system. A purpose of this study is to evaluate the proposed method. Elderly people in a nursing home were chosen as subjects in this study. We analyzed the body motion data during bed rest and bed exit of the subjects using the proposed method. These results suggest that it is effective.

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