  Device free activity recognition and monitoring has become a promising
research area with increasing public interest in pattern of life monitoring and
chronic health conditions. This paper proposes a novel framework for in-home
Wi-Fi signal-based activity recognition in e-healthcare applications using
passive micro-Doppler (m-D) signature classification. The framework includes
signal modeling, Doppler extraction and m-D classification. A data collection
campaign was designed to verify the framework where six m-D signatures
corresponding to typical daily activities are sucessfully detected and
classified using our software defined radio (SDR) demo system. Analysis of the
data focussed on potential discriminative characteristics, such as maximum
Doppler frequency and time duration of activity. Finally, a sparsity induced
classifier is applied for adaptting the method in healthcare application
scenarios and the results are compared with those from the well-known Support
Vector Machine (SVM) method.
