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3 results on '"Topic model"'

1. Hierarchical Complex Activity Representation and Recognition Using Topic Model and Classifier Level Fusion.

Author

Peng, Liangying, Chen, Ling, Wu, Xiaojie, Guo, Haodong, and Chen, Gencai

Subjects
*HUMAN activity recognition, *UBIQUITOUS computing, *RUNNING, *WALKING, *PHYSIOLOGY, *DIRICHLET forms
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. [ABSTRACT FROM AUTHOR]

Published
2017
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3. Investigating recognition accuracy improvement by adding user's acceleration data to location and power consumption-based in-home activity recognition system

Author

Hirohiko Suwa, Shotaro Miwa, Manato Fujimoto, Hatta Toshiyuki, Eri Nakagawa, Yutaka Arakawa, Kazuki Moriya, and Keiichi Yasumoto

Subjects
Topic model, Service (systems architecture), business.industry, Computer science, Real-time computing, 02 engineering and technology, Construct (python library), computer.software_genre, Smartwatch, Activity recognition, Acceleration, Home automation, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Data mining, business, computer, Wearable technology
Abstract

Recently, there are many studies on automatic recognition of activities of daily living (ADL) to provide various services such as elderly monitoring, intelligent concierge, and health support. In particular, real-time ADL recognition is essential to realize an intelligent concierge service since the service needs to know user's current or next activity for supporting it. We have been studying real-time ADL recognition using only user's position data and appliances' power consumption data which are considered to include less privacy information than audio and visual data. In the study, we found that some activities such as reading and operating smartphone that happen in similar conditions cannot be classified with only position and power data. In this paper, we propose a new method that adds the acceleration data from wearable devices for classifying activities happening in similar conditions with higher accuracy. In the proposed method, we use the acceleration data from a smart watch and a smartphone worn by user's arm and waist, respectively, in addition to user's position data and appliances' power consumption data, and construct a machine learning model for recognizing 15 types of target activities. We evaluated the recognition accuracy of 3 methods: our previous method (using only position data and power consumption data); the proposed method using the mean value and the standard deviation of the acceleration norm; and the proposed method using the ratio of the activity topics. We collected the sensor data in our smart home facility for 12 days, and applied the proposed method to these sensor data. As a result, the proposed method could recognize the activities with 57% which is 12 % improvement from our previous method without acceleration data.

Published
2016

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