Paper Info
Title
Human Daily Activity Recognition for Healthcare Using Wearable and Visual Sensing Data
Abstract
Wearable digital self-tracking technologies for monitoring individuals' health condition have become more accessible to the public in recent years with the development of connected portable devices, such as smart phones, smart watches, smart bands, and other personal biometric monitoring devices. Mining behavioural patterns from such wearable data along with other available sensory data, has the potential to offer an objective, insightful service in clinical professionals and healthcare. For example, accurate identification of human activities could help us provide a better patient recovery training guidance, or an early alarm of emergency that may happen to elder people, such as stroke, falls, etc. In this paper, we introduce an activity recognition system, which learns a nonlinear SVM algorithm to identify 20 different human activities from accelerometer and RGB-D camera data. Our early experimental results show that the proposed approach is promising and effective.
Year
DOI
Venue
2016
10.1109/ICHI.2016.100
2016 IEEE International Conference on Healthcare Informatics (ICHI)
Keywords
Field
DocType
human daily activity recognition,healthcare,visual sensing data,wearable digital self-tracking technologies,health condition,connected portable devices,smart phones,smart watches,smart bands,personal biometric monitoring devices,wearable data,patient recovery training guidance,nonlinear SVM algorithm,accelerometer,RGB-D camera data
Health care,Activity recognition,Wearable computer,ALARM,Accelerometer,Computer science,Support vector machine,Human–computer interaction,Biometrics,Smartwatch,Embedded system
Conference
ISBN
Citations 
PageRank 
978-1-5090-6118-1
3
0.41
References 
Authors
0
4
Name
Order
Citations
PageRank
Xi Liu1113.45
Lei Liu2508.33
Steven J. Simske339749.71
Jerry Liu481.25