Paper Info
Title
Physical Activity Classification Using Time-Frequency Signatures Of Motion Artifacts In Multi-Channel Electrical Impedance Plethysmographs
Abstract
Physical activities are known to introduce motion artifacts in electrical impedance plethysmographic (EIP) sensors. Existing literature considers motion artifacts as a nuisance and generally discards the artifact containing portion of the sensor output. This paper examines the notion of exploiting motion artifacts for detecting the underlying physical activities which give rise to the artifacts in question. In particular, we investigate whether the artifact pattern associated with a physical activity is unique; and does it vary from one human-subject to another? Data was recorded from 19 adult human-subjects while conducting 5 distinct, artifact inducing, activities. A set of novel features based on the time-frequency signatures of the sensor outputs are then constructed. Our analysis demonstrates that these features enable high accuracy detection of the underlying physical activity. Using an SVM classifier we are able to differentiate between 5 distinct physical activities (coughing, reaching, walking, eating and rolling-on-bed) with an average accuracy of 85.46%. Classification is performed solely using features designed specifically to capture the time-frequency signatures of different physical activities. This enables us to measure both respiratory and motion information using only one type of sensor. This is in contrast to conventional approaches to physical activity monitoring; which rely on additional hardware such as accelerometers to capture activity information.
Year
DOI
Venue
2017
10.1109/EMBC.2017.8037474
2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Computer vision,Activity classification,Accelerometer,Computer science,Electrical impedance,Multi channel,Time–frequency analysis,Artificial intelligence,Svm classifier
Conference
2017
ISSN
Citations 
PageRank 
1094-687X
0
0.34
References 
Authors
2
4
Name
Order
Citations
PageRank
Hassan Khan112214.94
Amit Gore2478.82
jeffrey michael ashe322.55
Shantanu Chakrabartty433564.30