title: Hierarchical Complex Activity Representation and Recognition Using Topic Model and Classifier Level Fusion.
abstract: Human activity recognition is an important area of ubiquitous computing. Most current researches in activity recognition mainly focus on simple activities, e.g., sitting, running, walking, and standing. Compared with simple activities, complex activities are more complicated with high-level semantics, e.g., working, commuting, and having a meal. This paper presents a hierarchical model to recognize complex activities as mixtures of simple activities and multiple actions. We generate the components of complex activities using a clustering algorithm, represent and recognize complex activities by applying a topic model on these components. It is a data-driven method that can retain effective information for representing and recognizing complex activities. In addition, acceleration and physiological signals are fused in classifier level to ensure the overall performance of complex activity recognition. The results of experiments show that our method has ability to represent and recognize complex activities effectively.
authors.lastname: Liangying Peng
authors.lastname: Ling Chen
authors.lastname: Xiaojie Wu
authors.lastname: Haodong Guo
authors.lastname: Gencai Chen
mesh_terms: D056044:Actigraphy / Q000379:methods*; D000328:Adult; D000465:Algorithms; D003198:Computer Simulation; D015444:Exercise / Q000502:physiology*; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D015233:Models, Statistical; D018670:Monitoring, Ambulatory / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055815:Young Adult