Paper Info
Title
Performance Analysis of Machine Learning Algorithms for Fall Detection
Abstract
Intelligent IoT-based ambient assisted living systems (AALS) have been a major research focus area in recent times. Application of machine learning in areas of AALS such as fall detection has the potential to have huge social impact. There has been active research in the application of machine learning in fall detection, using data generated by various means such as wearable devices, environment sensors and vision based systems. The main challenge is to create a model that detects falls accurately, while keeping the design of the fall detection system minimal and non-intrusive. Wearable devices equipped with inertial motion unit (IMU) sensors and vital signs sensors are commonly used to enable analysis around performance of machine learning (ML) models. In this paper, we analyze the impact of using IMU sensor parameters in combination with vital signs parameters, on the performance of ML algorithms for fall detection. We present details on the data set we have generated for this purpose, and compare the performance of various ML algorithms on the collected dataset, with features from IMU sensors vis-à-vis those from IMU sensors in combination with vital signs sensors. We also apply machine learning algorithms on two public datasets, one with only IMU sensor parameter values and the second with only vital signs parameter values, and summarize their performance.
Year
DOI
Venue
2019
10.1109/HealthCom46333.2019.9009442
2019 IEEE International Conference on E-health Networking, Application & Services (HealthCom)
Keywords
Field
DocType
Fall detection,machine learning,wearable systems
Computer science,Artificial intelligence,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-7281-0403-4
0
0.34
References 
Authors
10
6
Name
Order
Citations
PageRank
Anita Ramachandran110.73
Adarsh Ramesh200.34
Piyush Pahwa300.34
A. Prahalad Atreyaa400.34
Shivam Murari500.34
Anupama, K.R.6137.76