2020

• Validation of an IMU Gait Analysis Algorithm for Gait Monitoring in Daily Life Situations.Zhou, Lin; Tunca, Can; Fischer, Eric; Brahms, Clemens Markus; Ersoy, Cem; Granacher, Urs; Arnrich, Bert (2020).
  [ Abstract ] [ BibTeX ] [ EndNote ] [ DOI ]
   
  Gait is an essential function for humans, and gait patterns in daily life provide meaningful information about a person’s cognitive and physical health conditions. Inertial measurement units (IMUs) have emerged as a promising tool for low-cost, unobtrusive gait analysis. However, large varieties of IMU gait analysis algorithms and the lack of consensus for their validation make it difficult for researchers to assess the reliability of the algorithms for specific use cases. In daily life,individuals adapt their gait patterns in response to changes in the environment, making it necessary for IMU gait analysis algorithms to provide accurate measurements despite these gait variations. In this paper, we reviewed common types of IMU gait analysis algorithms and appropriate analysis methods to evaluate the accuracy of gait parameters extracted from IMU measurements. We then evaluated stride lengths and stride times calculated from a comprehensive double integration based IMU gait analysis algorithm using an optoelectric walkway as gold standard. In total, 729 strides from five healthy subjects and three different walking patterns were analyzed. Correlation analyses and Bland-Altman plots showed that this method is accurate and robust against large variations in walking patterns (stride length: correlation coefficient (r) was 0.99, root mean square error (RMSE) was 3% and average limits of agreement (LoA) was 6%; stride time: r was 0.95, RMSE was 4% and average LoA was 7%), making it suitable for gait evaluation in daily life situations. Due to the small sample size, our preliminary findings should be verified in future studies.
  @conference{zhou2020validation, abstract = {Gait is an essential function for humans, and gait patterns in daily life provide meaningful information about a person’s cognitive and physical health conditions. Inertial measurement units (IMUs) have emerged as a promising tool for low-cost, unobtrusive gait analysis. However, large varieties of IMU gait analysis algorithms and the lack of consensus for their validation make it difficult for researchers to assess the reliability of the algorithms for specific use cases. In daily life,individuals adapt their gait patterns in response to changes in the environment, making it necessary for IMU gait analysis algorithms to provide accurate measurements despite these gait variations. In this paper, we reviewed common types of IMU gait analysis algorithms and appropriate analysis methods to evaluate the accuracy of gait parameters extracted from IMU measurements. We then evaluated stride lengths and stride times calculated from a comprehensive double integration based IMU gait analysis algorithm using an optoelectric walkway as gold standard. In total, 729 strides from five healthy subjects and three different walking patterns were analyzed. Correlation analyses and Bland-Altman plots showed that this method is accurate and robust against large variations in walking patterns (stride length: correlation coefficient (r) was 0.99, root mean square error (RMSE) was 3% and average limits of agreement (LoA) was 6%; stride time: r was 0.95, RMSE was 4% and average LoA was 7%), making it suitable for gait evaluation in daily life situations. Due to the small sample size, our preliminary findings should be verified in future studies.}, author = {Zhou, Lin and Tunca, Can and Fischer, Eric and Brahms, Clemens Markus and Ersoy, Cem and Granacher, Urs and Arnrich, Bert}, booktitle = {42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, keywords = {sys:relevantfor:connectedhealth arnrich bert connected health lin zhou}, month = {jul}, title = {Validation of an IMU Gait Analysis Algorithm for Gait Monitoring in Daily Life Situations}, year = 2020 }
  %0 Generic %1 zhou2020validation %A Zhou, Lin %A Tunca, Can %A Fischer, Eric %A Brahms, Clemens Markus %A Ersoy, Cem %A Granacher, Urs %A Arnrich, Bert %B 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) %D 2020 %R 10.1109/EMBC44109.2020.9176827 %T Validation of an IMU Gait Analysis Algorithm for Gait Monitoring in Daily Life Situations %X Gait is an essential function for humans, and gait patterns in daily life provide meaningful information about a person’s cognitive and physical health conditions. Inertial measurement units (IMUs) have emerged as a promising tool for low-cost, unobtrusive gait analysis. However, large varieties of IMU gait analysis algorithms and the lack of consensus for their validation make it difficult for researchers to assess the reliability of the algorithms for specific use cases. In daily life,individuals adapt their gait patterns in response to changes in the environment, making it necessary for IMU gait analysis algorithms to provide accurate measurements despite these gait variations. In this paper, we reviewed common types of IMU gait analysis algorithms and appropriate analysis methods to evaluate the accuracy of gait parameters extracted from IMU measurements. We then evaluated stride lengths and stride times calculated from a comprehensive double integration based IMU gait analysis algorithm using an optoelectric walkway as gold standard. In total, 729 strides from five healthy subjects and three different walking patterns were analyzed. Correlation analyses and Bland-Altman plots showed that this method is accurate and robust against large variations in walking patterns (stride length: correlation coefficient (r) was 0.99, root mean square error (RMSE) was 3% and average limits of agreement (LoA) was 6%; stride time: r was 0.95, RMSE was 4% and average LoA was 7%), making it suitable for gait evaluation in daily life situations. Due to the small sample size, our preliminary findings should be verified in future studies.
• How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications.Zhou, Lin; Fischer, Eric; Tunca, Can; Brahms, Clemens Markus; Ersoy, Cem; Granacher, Urs; Arnrich, Bert in Sensors (2020).
  [ BibTeX ] [ EndNote ] [ DOI ]
   
  @article{zhou2020found, author = {Zhou, Lin and Fischer, Eric and Tunca, Can and Brahms, Clemens Markus and Ersoy, Cem and Granacher, Urs and Arnrich, Bert}, journal = {Sensors}, keywords = {sys:relevantfor:connectedhealth arnrich bert connected health lin zhou}, title = {How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications}, year = 2020 }
  %0 Journal Article %1 zhou2020found %A Zhou, Lin %A Fischer, Eric %A Tunca, Can %A Brahms, Clemens Markus %A Ersoy, Cem %A Granacher, Urs %A Arnrich, Bert %D 2020 %J Sensors %R 10.3390/s20154090 %T How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications

2019

• Unobtrusive Measurement of Blood Pressure During Lifestyle Interventions.Morassi Sasso, Ariane; Datta, Suparno; Pfitzner, Bjarne; Zhou, Lin; Steckhan, Nico; Boettinger, Erwin; Arnrich, Bert (2019).
  [ BibTeX ] [ EndNote ] [ URL ] [ DOI ]
   
  @inproceedings{MorassiSasso2019, address = {Trento}, author = {Morassi Sasso, Ariane and Datta, Suparno and Pfitzner, Bjarne and Zhou, Lin and Steckhan, Nico and Boettinger, Erwin and Arnrich, Bert}, booktitle = {Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare - Demos and Posters}, keywords = {sys:relevantfor:connectedhealth arnrich bert bjarne lin nico pfitzner steckhan zhou}, publisher = {EAI}, title = {Unobtrusive Measurement of Blood Pressure During Lifestyle Interventions}, year = 2019 }
  %0 Conference Paper %1 MorassiSasso2019 %A Morassi Sasso, Ariane %A Datta, Suparno %A Pfitzner, Bjarne %A Zhou, Lin %A Steckhan, Nico %A Boettinger, Erwin %A Arnrich, Bert %B Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare - Demos and Posters %C Trento %D 2019 %I EAI %R 10.4108/eai.20-5-2019.2283509 %T Unobtrusive Measurement of Blood Pressure During Lifestyle Interventions %U http://eudl.eu/doi/10.4108/eai.20-5-2019.2283509 %@ 978-1-63190-184-3