Paper Info
Title
Toward Early Severity Assessment Of Obstructive Lung Disease Using Multi-Modal Wearable Sensor Data Fusion During Walking
Abstract
Early detection of chronic diseases helps to minimize the disease impact on patient's health and reduce the economic burden. Continuous monitoring of such diseases helps in the evaluation of rehabilitation program effectiveness as well as in the detection of exacerbation. The use of everyday wearables i.e. chest band, smartwatch and smart band equipped with good quality sensor and light weight machine learning algorithm for the early detection of diseases is very promising and holds tremendous potential as they are widely used. In this study, we have investigated the use of acceleration, electrocardiogram, and respiration sensor data from a chest band for the evaluation of obstructive lung disease severity. Recursive feature elimination technique has been used to identify top 15 features from a set of 62 features including gait characteristics, respiration pattern and heart rate variability. A precision of 0.93, recall of 0.91 and F-1 score of 0.92 have been achieved with a support vector machine for the classification of severe patients from the non-severe patients in a data set of 60 patients. In addition, the selected features showed significant correlation with the percentage of predicted FEV1.
Year
DOI
Venue
2020
10.1109/EMBC44109.2020.9176559
42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20
DocType
Volume
ISSN
Conference
2020
1557-170X
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Md Juber Rahman100.68
Ebrahim Nemati28415.30
Mahbubur Rahman300.34
Korosh Vatanparvar413416.20
Viswam Nathan55014.09
Jilong Kuang63817.00