{'fraction_non_alphanumeric': 0.022263450834879406, 'fraction_numerical': 0.00463821892393321, 'mean_word_length': 5.130681818181818, '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}}
Human fall detection using CHLAC features with skeletal image sequences

This paper describes a human fall detection approach using CHLAC features with skeletal image sequences obtained from Kinect on a mobile robot. In the proposed approach, the spatio-temporal local geometric features of moving-image sequences are extracted with CHLAC (the cubic higher-order local auto-correlation) method firstly. Then the 251-dimensional CHLAC feature vectors are projected to a low-dimensional eigenspace by principal components analysis method. The skeletal image sequences of 3 kinds of motions (walking and lying down as normal motion, fall as abnormal motion) are learned in the low-dimensional eigenspace and the basis of the eigenspace can be obtained off-line. The CHLAC feature vectors of skeleton image sequences obtained in real time are projected to the eigenspace obtained off-line. The degree of similarity to the 3 kinds of motions is evaluated and the motion is classified by k-Nearest Neighbor algorithm. The performance of the classification is evaluated with F-measure.