{'fraction_non_alphanumeric': 0.021630615640599003, 'fraction_numerical': 0.004991680532445923, 'mean_word_length': 5.840909090909091, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Individual classification through autoregressive modelling of micro-doppler signatures

This paper introduces the use of autoregressive modelling (AR) to characterize individual human gait signatures from micro-Doppler data. AR models are fitted to micro-Doppler data obtained while 6 subjects walk towards a custom-made ultrasonic transceiver module. The estimated AR coefficients capture individual movement characteristics. Such features can be used to identify different subjects quickly and with low computational cost. In the best configuration, average performance higher than 98% was obtained.