Book ChapterDOI
Human activity recognition from wireless sensor network data: benchmark and software
T. L. M. van Kasteren,Gwenn Englebienne,Ben Kröse +2 more
- Iss: 4, pp 165-186
TLDR
This chapter presents the state of the art probabilistic models used in activity recognition and shows their performance on several real world datasets so that they can be used as a baseline for comparing the performance of other pattern recognition methods.Abstract:
Although activity recognition is an active area of research no common benchmark for evaluating the performance of activity recognition methods exists. In this chapter we present the state of the art probabilistic models used in activity recognition and show their performance on several real world datasets. Our results can be used as a baseline for comparing the performance of other pattern recognition methods (both probabilistic and non-probabilistic). The datasets used in this chapter are made public, together with the source code of the probabilistic models used.read more
Citations
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Posted Content
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
TL;DR: This study presents a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition and proposes a new taxonomy to structure the deep methods by challenges.
Journal ArticleDOI
A review on applications of activity recognition systems with regard to performance and evaluation
TL;DR: An overview of the applications of activity recognition systems is provided and a comparison of the existing methodologies which, when applied to real-world scenarios, allow to formulate research questions for future approaches are compared.
Journal ArticleDOI
A review of smart homes in healthcare
TL;DR: The present survey will address technologies and analysis methods and bring examples of the state of the art research studies in order to provide background for the research community.
Book ChapterDOI
Human Activity Recognition Using Recurrent Neural Networks
Deepika Singh,Erinc Merdivan,Ismini Psychoula,Johannes Kropf,Sten Hanke,Matthieu Geist,Andreas Holzinger +6 more
TL;DR: A deep learning model that learns to classify human activities without using any prior knowledge is introduced and it is shown that the proposed approach outperforms the existing ones in terms of accuracy and performance.
References
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Book
Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Book
Pattern Recognition and Machine Learning (Information Science and Statistics)
TL;DR: Looking for competent reading resources?
Journal ArticleDOI
On the limited memory BFGS method for large scale optimization
Dong C. Liu,Jorge Nocedal +1 more
TL;DR: The numerical tests indicate that the L-BFGS method is faster than the method of Buckley and LeNir, and is better able to use additional storage to accelerate convergence, and the convergence properties are studied to prove global convergence on uniformly convex problems.