{'fraction_non_alphanumeric': 0.02688953488372093, 'fraction_numerical': 0.00436046511627907, 'mean_word_length': 5.404651162790698, '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}}
A Heterogeneous Ensemble Approach for Activity Recognition with Integration of Change Point-Based Data Segmentation

One of the main topics of Smart Home (SH) research is the recognition of activities performed by its inhabitants, which is considered to be one of the bases to foster new technological solutions inside the home, including services to prolong independent living of the elderly. However, current activity recognition proposals still find problems when considering all the different types of activities that can be performed at home, namely static, dynamic, and transitional activities. In this paper, we consider recognition of transitional activities, which is often ignored in most studies. In addition, we propose a novel dynamic segmentation method based on change points in data stream and construct an ensemble of heterogeneous classifiers to recognize twelve activities (of all types). The experiment is conducted on the dataset collected over ten hours by a wearable accelerometer placed on the person’s wrist. The base classifiers selected to form this ensemble are support vector machine (SVM), decision tree (DT) and k-nearest neighbors (KNN). As a result, the proposed approach has achieved an overall classification accuracy equal to 96.87% with 10-fold cross-validation. Moreover, all activity types considered have been similarly well identified.