@InProceedings{8081244,
  author = {T. K. M. Lee and K. Leo and S. Sanei and E. Chew and L. Zhao},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Triaxial rehabilitative data analysis incorporating matching pursuit},
  year = {2017},
  pages = {434-438},
  abstract = {The continuing drive for better rehabilitative healthcare hinges on the availability of sensor data which can be shared and analysed. This leverages on the widespread communications network to provide an integrated health management environment. For this paper, we delineate our current work in sensorizing rehabilitative tests of upper limb movements. Where previously we applied data driven analysis, we now employ time-frequency methods to provide a better analytical basis for our derivations. The use of Matching Pursuit algorithm in biological signals has concentrated on brain signals and much less on human motion. Thus we contribute to efficacy of the algorithm by employing it on rehabilitative data collected from widely available sensors. We describe how we obtained the parameters based on pre-analysing an available data set. By selecting the most useful signal constituents and applying this to signal denoising, we are able to better classify the condition of a patient automatically - which shows encouraging promise in the quest for integrative healthcare.},
  keywords = {brain;data analysis;feature extraction;health care;iterative methods;medical signal processing;patient rehabilitation;signal denoising;time-frequency analysis;sensor data;widespread communications network;integrated health management environment;rehabilitative tests;upper limb movements;data driven analysis;time-frequency methods;Matching Pursuit algorithm;biological signals;brain signals;integrative healthcare;continuing drive;rehabilitative healthcare hinges;signal constituents;triaxial rehabilitative data analysis;Matching pursuit algorithms;Instruments;Accelerometers;Force sensors;Monitoring;Force;Dictionaries;Matching pursuit;rehabilitation;accelerometer;instrumented objects},
  doi = {10.23919/EUSIPCO.2017.8081244},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347474.pdf},
}

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M.","Leo, K.","Sanei, S.","Chew, E.","Zhao, L."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["T.","K.","M."],"propositions":[],"lastnames":["Lee"],"suffixes":[]},{"firstnames":["K."],"propositions":[],"lastnames":["Leo"],"suffixes":[]},{"firstnames":["S."],"propositions":[],"lastnames":["Sanei"],"suffixes":[]},{"firstnames":["E."],"propositions":[],"lastnames":["Chew"],"suffixes":[]},{"firstnames":["L."],"propositions":[],"lastnames":["Zhao"],"suffixes":[]}],"booktitle":"2017 25th European Signal Processing Conference (EUSIPCO)","title":"Triaxial rehabilitative data analysis incorporating matching pursuit","year":"2017","pages":"434-438","abstract":"The continuing drive for better rehabilitative healthcare hinges on the availability of sensor data which can be shared and analysed. This leverages on the widespread communications network to provide an integrated health management environment. For this paper, we delineate our current work in sensorizing rehabilitative tests of upper limb movements. Where previously we applied data driven analysis, we now employ time-frequency methods to provide a better analytical basis for our derivations. The use of Matching Pursuit algorithm in biological signals has concentrated on brain signals and much less on human motion. Thus we contribute to efficacy of the algorithm by employing it on rehabilitative data collected from widely available sensors. We describe how we obtained the parameters based on pre-analysing an available data set. By selecting the most useful signal constituents and applying this to signal denoising, we are able to better classify the condition of a patient automatically - which shows encouraging promise in the quest for integrative healthcare.","keywords":"brain;data analysis;feature extraction;health care;iterative methods;medical signal processing;patient rehabilitation;signal denoising;time-frequency analysis;sensor data;widespread communications network;integrated health management environment;rehabilitative tests;upper limb movements;data driven analysis;time-frequency methods;Matching Pursuit algorithm;biological signals;brain signals;integrative healthcare;continuing drive;rehabilitative healthcare hinges;signal constituents;triaxial rehabilitative data analysis;Matching pursuit algorithms;Instruments;Accelerometers;Force sensors;Monitoring;Force;Dictionaries;Matching pursuit;rehabilitation;accelerometer;instrumented objects","doi":"10.23919/EUSIPCO.2017.8081244","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347474.pdf","bibtex":"@InProceedings{8081244,\n author = {T. K. M. Lee and K. Leo and S. Sanei and E. Chew and L. Zhao},\n booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},\n title = {Triaxial rehabilitative data analysis incorporating matching pursuit},\n year = {2017},\n pages = {434-438},\n abstract = {The continuing drive for better rehabilitative healthcare hinges on the availability of sensor data which can be shared and analysed. This leverages on the widespread communications network to provide an integrated health management environment. For this paper, we delineate our current work in sensorizing rehabilitative tests of upper limb movements. Where previously we applied data driven analysis, we now employ time-frequency methods to provide a better analytical basis for our derivations. The use of Matching Pursuit algorithm in biological signals has concentrated on brain signals and much less on human motion. Thus we contribute to efficacy of the algorithm by employing it on rehabilitative data collected from widely available sensors. We describe how we obtained the parameters based on pre-analysing an available data set. By selecting the most useful signal constituents and applying this to signal denoising, we are able to better classify the condition of a patient automatically - which shows encouraging promise in the quest for integrative healthcare.},\n keywords = {brain;data analysis;feature extraction;health care;iterative methods;medical signal processing;patient rehabilitation;signal denoising;time-frequency analysis;sensor data;widespread communications network;integrated health management environment;rehabilitative tests;upper limb movements;data driven analysis;time-frequency methods;Matching Pursuit algorithm;biological signals;brain signals;integrative healthcare;continuing drive;rehabilitative healthcare hinges;signal constituents;triaxial rehabilitative data analysis;Matching pursuit algorithms;Instruments;Accelerometers;Force sensors;Monitoring;Force;Dictionaries;Matching pursuit;rehabilitation;accelerometer;instrumented objects},\n doi = {10.23919/EUSIPCO.2017.8081244},\n issn = {2076-1465},\n month = {Aug},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347474.pdf},\n}\n\n","author_short":["Lee, T. 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