Shaopeng Liu

Summary

Affiliation: University of Connecticut
Country: USA

Publications

  1. doi Multisensor data fusion for physical activity assessment
    Shaopeng Liu
    Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
    IEEE Trans Biomed Eng 59:687-96. 2012
  2. doi Wireless design of a multisensor system for physical activity monitoring
    Lingfei Mo
    Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
    IEEE Trans Biomed Eng 59:3230-7. 2012
  3. doi Classification of physical activities based on sparse representation
    Shaopeng Liu
    Electromechanical Systems Laboratory, Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
    Conf Proc IEEE Eng Med Biol Soc 2012:6200-3. 2012
  4. doi SVM-based multi-sensor fusion for free-living physical activity assessment
    Shaopeng Liu
    Electromechanical Systems Laboratory, Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
    Conf Proc IEEE Eng Med Biol Soc 2011:3188-91. 2011
  5. doi ZigBee-based wireless multi-sensor system for physical activity assessment
    Lingfei Mo
    Electromechanical Systems Laboratory, Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
    Conf Proc IEEE Eng Med Biol Soc 2011:846-9. 2011
  6. pmc Computational methods for estimating energy expenditure in human physical activities
    Shaopeng Liu
    Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
    Med Sci Sports Exerc 44:2138-46. 2012
  7. pmc Tissue artifact removal from respiratory signals based on empirical mode decomposition
    Shaopeng Liu
    Department of Mechanical Engineering, University of Connecticut, 191 Auditorium Rd, Unit 3139, Storrs, CT 06269, USA
    Ann Biomed Eng 41:1003-15. 2013
  8. pmc Improved regression models for ventilation estimation based on chest and abdomen movements
    Shaopeng Liu
    Department of Mechanical Engineering, University of Connecticut, Storrs, CT, USA
    Physiol Meas 33:79-93. 2012
  9. doi Development of statistical regression models for ventilation estimation
    Shaopeng Liu
    Electromechanical Systems Laboratory, University of Connecticut, Storrs, CT 06269, USA
    Conf Proc IEEE Eng Med Biol Soc 2009:1266-9. 2009

Detail Information

Publications9

  1. doi Multisensor data fusion for physical activity assessment
    Shaopeng Liu
    Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
    IEEE Trans Biomed Eng 59:687-96. 2012
    ..These results demonstrate that the multisensor fusion technique presented is more effective in identifying activity type and energy expenditure than the traditional accelerometer-alone-based methods...
  2. doi Wireless design of a multisensor system for physical activity monitoring
    Lingfei Mo
    Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
    IEEE Trans Biomed Eng 59:3230-7. 2012
    ..The results demonstrate the effectiveness of the energy-efficient wireless design for human PA monitoring...
  3. doi Classification of physical activities based on sparse representation
    Shaopeng Liu
    Electromechanical Systems Laboratory, Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
    Conf Proc IEEE Eng Med Biol Soc 2012:6200-3. 2012
    ..Higher discriminative power than that from the conventional k-nearest neighbor algorithm has been demonstrated through experiments performed on 105 subjects...
  4. doi SVM-based multi-sensor fusion for free-living physical activity assessment
    Shaopeng Liu
    Electromechanical Systems Laboratory, Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
    Conf Proc IEEE Eng Med Biol Soc 2011:3188-91. 2011
    ..These results demonstrate that the multi-sensor fusion technique presented is more effective in assessing activities of varying intensities than the traditional accelerometer-alone based methods...
  5. doi ZigBee-based wireless multi-sensor system for physical activity assessment
    Lingfei Mo
    Electromechanical Systems Laboratory, Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
    Conf Proc IEEE Eng Med Biol Soc 2011:846-9. 2011
    ..Preliminary testing of the WIMS has demonstrated the functionality of the design, while performance comparison of the WIMS with a wired version on an electromagnetic shaker has demonstrated the signal validity...
  6. pmc Computational methods for estimating energy expenditure in human physical activities
    Shaopeng Liu
    Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
    Med Sci Sports Exerc 44:2138-46. 2012
    ..The review illustrates three directions in the PAEE studies and provides recommendations for future research, with the aim to produce valid, reliable, and accurate assessment of PAEE from wearable sensors...
  7. pmc Tissue artifact removal from respiratory signals based on empirical mode decomposition
    Shaopeng Liu
    Department of Mechanical Engineering, University of Connecticut, 191 Auditorium Rd, Unit 3139, Storrs, CT 06269, USA
    Ann Biomed Eng 41:1003-15. 2013
    ..Comparison with low-pass filtering that has been conventionally applied confirmed the effectiveness of the technique in tissue artifacts removal...
  8. pmc Improved regression models for ventilation estimation based on chest and abdomen movements
    Shaopeng Liu
    Department of Mechanical Engineering, University of Connecticut, Storrs, CT, USA
    Physiol Meas 33:79-93. 2012
    ..5%, verifying reasonably good performance of the models and the applicability of the wearable sensing system for minute ventilation estimation during physical activity...
  9. doi Development of statistical regression models for ventilation estimation
    Shaopeng Liu
    Electromechanical Systems Laboratory, University of Connecticut, Storrs, CT 06269, USA
    Conf Proc IEEE Eng Med Biol Soc 2009:1266-9. 2009
    ..The results indicate that Model 2, combining respiratory features and breathing frequency, with a longer time intervals will lead to a higher accuracy...