Paper Info
Title
Elderly Assistance Using Wearable Sensors by Detecting Fall and Recognizing Fall Patterns.
Abstract
Falling is a serious threat to the elderly people. One severe fall can cause hazardous problems like bone fracture or may lead to some permanent disability or even death. Thus, it has become the need of the hour to continuously monitor the activities of the elderly people so that in case of fall incident they may get rescued timely. For this purpose, many fall monitoring systems have been proposed for the ubiquitous personal assistance of the elderly people but most of those systems focus on the detection of fall incident only. However, if a fall monitoring system is made capable of recognizing the way in which the fall occurs, it can better assist people in preventing or reducing future falls. Therefore, in this study, we proposed a fall monitoring system that not only detects a fall but also recognizes the pattern of the fall for elderly assistance using supervised machine learning. The proposed system effectively distinguishes between falling and non-falling activities to recognize the fall pattern with a high accuracy.
Year
DOI
Venue
2018
10.1145/3267305.3274129
UbiComp '18: The 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing Singapore Singapore October, 2018
Keywords
Field
DocType
Fall Detection, Fall Monitoring, Fall Pattern Recognition, Fall Prevention, Machine Learning, Ubiquitous Computing, Wearable Sensors
Monitoring system,Computer science,Computer security,Wearable computer,Fall prevention,Human–computer interaction,Ubiquitous computing
Conference
ISBN
Citations 
PageRank 
978-1-4503-5966-5
1
0.35
References 
Authors
10
4
Name
Order
Citations
PageRank
Faisal B. Hussain1357.06
Muhammad Ehatisham-ul-Haq2276.73
Muhammad Awais Azam317824.45
Asra Khalid4343.54