Activity recognition with cooperative radar systems at C and K band

Abstract

Remote health monitoring is a key component in the future of healthcare with predictive and fall risk estimation applications required in great need and with urgency. Radar, through the exploitation of the micro-Doppler effect, is able to generate signatures that can be classified automatically. In this work, features from two different radar systems operating at C band and K band have been used together co-operatively to classify ten indoor human activities with data from 20 subjects with a support vector machine classifier. Feature selection has been applied to remove redundancies and find a set of salient features for the radar systems, individually and in the fused scenario. Using the aforementioned methods, we show improvements in the classification accuracy for the systems from 75 and 70% for the radar systems individually, up to 89% when fused.

Keywords

• support vector machines
• doppler radar
• pattern classification
• feature extraction
• cooperative communication
• feature selection
• radar imaging
• health care
• radar computing
• cooperative radar systems
• salient features
• feature selection
• support vector machine classifier
• indoor human activities
• microdoppler effect
• risk estimation applications
• remote health monitoring
• activity recognition