@inproceedings{discovery10052434,
          volume = {2017},
           title = {Bistatic human micro-Doppler signatures for classification of indoor activities},
           month = {June},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
           pages = {610--615},
          series = {IEEE Radar Conference (RadarConf)},
         journal = {2017 IEEE RADAR CONFERENCE (RADARCONF)},
       booktitle = {Proceedings of the 2017 IEEE Radar Conference (RadarConf)},
       publisher = {IEEE},
         address = {Seattle, WA, USA},
            year = {2017},
             url = {https://doi.org/10.1049/iet-rsn.2016.0503},
        keywords = {Radar, Feature extraction, Doppler effect, Legged locomotion, Spectrogram, Radar antennas, Receivers, bistatic radar, micro-Doppler, feature extraction and lassification, machine learning},
          author = {Fioranelli, F and Ritchie, M and Griffiths, H},
            issn = {1097-5764},
        abstract = {This paper presents the analysis of human micro- Doppler signatures collected by a bistatic radar system to classify different indoor activities. Tools for automatic classification of different activities will enable the implementation and deployment of systems for monitoring life patterns of people and identifying fall events or anomalies which may be related to early signs of deteriorating physical health or cognitive capabilities. The preliminary results presented here show that the information within the micro-Doppler signatures can be successfully exploited for automatic classification, with accuracy up to 98\%, and that the multi-perspective view on the target provided by bistatic data can contribute to enhance the overall system performance.}
}