@InProceedings{Ali2008,
  Title                    = {Semi-supervised segmentation for activity recognition with Multiple Eigenspaces},
  Author                   = {Ali, A. and King, R.C. and Guang-Zhong Yang},
  Booktitle                = {Medical Devices and Biosensors, 2008. ISSS-MDBS 2008. 5th International Summer School and Symposium on},
  Year                     = {2008},
  Month                    = {June},
  Pages                    = {314-317},

  Abstract                 = {Body Sensor Networks (BSNs) are increasingly being used in pervasive sensing environments including healthcare, sports, wellbeing, and gaming. Activity segmentation using BSN is challenging and the use of manual annotation is subjective and error prone. In this paper, we investigate a semi-supervised activity segmentation method using a Multiple Eigenspace (MES) technique based on Principal Components Analysis (PCA). Results show that the method can reliably perform activity segmentation and the classification results based on HMMs demonstrate the practical value of the proposed technique.},
  Doi                      = {10.1109/ISSMDBS.2008.4575082},
  Keywords                 = {biomedical equipment;body area networks;health care;medical signal processing;patient monitoring;principal component analysis;sport;activity recognition;body sensor networks;gaming;health care;multiple eigenspace technique;pervasive sensing environments;principal components analysis;semi-supervised activity segmentation;sports;wellbeing;Bayesian methods;Biomedical monitoring;Biosensors;Body sensor networks;Hidden Markov models;Machine learning;Medical services;Microelectromechanical systems;Principal component analysis;Wearable sensors;Body Sensor Networks;HMM;MES;activity recognition;activity segmentation},
  Timestamp                = {2014.12.22}
}

{"_id":"ZxJAqJXwaMM2ukNxf","bibbaseid":"ali-king-yang-semisupervisedsegmentationforactivityrecognitionwithmultipleeigenspaces-2008","downloads":0,"creationDate":"2017-09-14T16:34:36.162Z","title":"Semi-supervised segmentation for activity recognition with Multiple Eigenspaces","author_short":["Ali, A.","King, R.","Yang, G."],"year":2008,"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/jfslin/jfslin.github.io/master/jf2lin.bib","bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Semi-supervised segmentation for activity recognition with Multiple Eigenspaces","author":[{"propositions":[],"lastnames":["Ali"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["King"],"firstnames":["R.C."],"suffixes":[]},{"firstnames":["Guang-Zhong"],"propositions":[],"lastnames":["Yang"],"suffixes":[]}],"booktitle":"Medical Devices and Biosensors, 2008. ISSS-MDBS 2008. 5th International Summer School and Symposium on","year":"2008","month":"June","pages":"314-317","abstract":"Body Sensor Networks (BSNs) are increasingly being used in pervasive sensing environments including healthcare, sports, wellbeing, and gaming. Activity segmentation using BSN is challenging and the use of manual annotation is subjective and error prone. In this paper, we investigate a semi-supervised activity segmentation method using a Multiple Eigenspace (MES) technique based on Principal Components Analysis (PCA). Results show that the method can reliably perform activity segmentation and the classification results based on HMMs demonstrate the practical value of the proposed technique.","doi":"10.1109/ISSMDBS.2008.4575082","keywords":"biomedical equipment;body area networks;health care;medical signal processing;patient monitoring;principal component analysis;sport;activity recognition;body sensor networks;gaming;health care;multiple eigenspace technique;pervasive sensing environments;principal components analysis;semi-supervised activity segmentation;sports;wellbeing;Bayesian methods;Biomedical monitoring;Biosensors;Body sensor networks;Hidden Markov models;Machine learning;Medical services;Microelectromechanical systems;Principal component analysis;Wearable sensors;Body Sensor Networks;HMM;MES;activity recognition;activity segmentation","timestamp":"2014.12.22","bibtex":"@InProceedings{Ali2008,\n Title = {Semi-supervised segmentation for activity recognition with Multiple Eigenspaces},\n Author = {Ali, A. and King, R.C. and Guang-Zhong Yang},\n Booktitle = {Medical Devices and Biosensors, 2008. ISSS-MDBS 2008. 5th International Summer School and Symposium on},\n Year = {2008},\n Month = {June},\n Pages = {314-317},\n\n Abstract = {Body Sensor Networks (BSNs) are increasingly being used in pervasive sensing environments including healthcare, sports, wellbeing, and gaming. Activity segmentation using BSN is challenging and the use of manual annotation is subjective and error prone. In this paper, we investigate a semi-supervised activity segmentation method using a Multiple Eigenspace (MES) technique based on Principal Components Analysis (PCA). Results show that the method can reliably perform activity segmentation and the classification results based on HMMs demonstrate the practical value of the proposed technique.},\n Doi = {10.1109/ISSMDBS.2008.4575082},\n Keywords = {biomedical equipment;body area networks;health care;medical signal processing;patient monitoring;principal component analysis;sport;activity recognition;body sensor networks;gaming;health care;multiple eigenspace technique;pervasive sensing environments;principal components analysis;semi-supervised activity segmentation;sports;wellbeing;Bayesian methods;Biomedical monitoring;Biosensors;Body sensor networks;Hidden Markov models;Machine learning;Medical services;Microelectromechanical systems;Principal component analysis;Wearable sensors;Body Sensor Networks;HMM;MES;activity recognition;activity segmentation},\n Timestamp = {2014.12.22}\n}\n\n","author_short":["Ali, A.","King, R.","Yang, G."],"key":"Ali2008","id":"Ali2008","bibbaseid":"ali-king-yang-semisupervisedsegmentationforactivityrecognitionwithmultipleeigenspaces-2008","role":"author","urls":{},"keyword":["biomedical equipment;body area networks;health care;medical signal processing;patient monitoring;principal component analysis;sport;activity recognition;body sensor networks;gaming;health care;multiple eigenspace technique;pervasive sensing environments;principal components analysis;semi-supervised activity segmentation;sports;wellbeing;Bayesian methods;Biomedical monitoring;Biosensors;Body sensor networks;Hidden Markov models;Machine learning;Medical services;Microelectromechanical systems;Principal component analysis;Wearable sensors;Body Sensor Networks;HMM;MES;activity recognition;activity segmentation"],"downloads":0},"search_terms":["semi","supervised","segmentation","activity","recognition","multiple","eigenspaces","ali","king","yang"],"keywords":["biomedical equipment;body area networks;health care;medical signal processing;patient monitoring;principal component analysis;sport;activity recognition;body sensor networks;gaming;health care;multiple eigenspace technique;pervasive sensing environments;principal components analysis;semi-supervised activity segmentation;sports;wellbeing;bayesian methods;biomedical monitoring;biosensors;body sensor networks;hidden markov models;machine learning;medical services;microelectromechanical systems;principal component analysis;wearable sensors;body sensor networks;hmm;mes;activity recognition;activity segmentation"],"authorIDs":[],"dataSources":["iCsmKnycRmHPxmhBd"]}