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Correlations of Gait Phase Kinematics and Cortical EEG: Modelling Human Gait with Data…
DOI: http://dx.doi.org/10.5772/intechopen.88465
Author details
Chaitanya Nutakki, Sandeep Bodda and Shyam Diwakar*
Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri
Campus, Kollam, Kerala, India
*Address all correspondence to: shyam@amrita.edu
and multi-configuration data relating gait and EEG may be needed, this step helps
to propose a pre-clinical assessment tool for rural communities, especially when
multi-specialty hospitals may consider outreach or where specialists may need
more time to understand movement related conditions prior to an actual diagnosis.
With useful preliminary results that supports gait as a BCI technology, it further
warrants the need to investigate the utility of mobile phone sensors for extracting
accelerometer-based data and its use in a patient population.
Acknowledgements
This work derives direction and ideas from the Chancellor of Amrita University,
Sri Mata Amritanandamayi Devi. This work was partially supported by Grants
DST/CSRI/2017/31 of Department of Science and Technology, Visvesvaraya
Fellowship, Ministry of Electronics and IT, Government of India and by Embracing
The World, Research-for-a-Cause-Initiative.
Conflict of interest
The authors declare no conflict of interest.
© 2020 The Author(s). Licensee IntechOpen. Distributed under the terms of the Creative
Commons Attribution - NonCommercial 4.0 License (https://creativecommons.org/
licenses/by-nc/4.0/), which permits use, distribution and reproduction for
non-commercial purposes, provided the original is properly cited.
Correlations of Gait Phase Kinematics and Cortical EEG: Modelling Human Gait with Data…
DOI: http://dx.doi.org/10.5772/intechopen.88465
Author details
Chaitanya Nutakki, Sandeep Bodda and Shyam Diwakar*
Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri
Campus, Kollam, Kerala, India
*Address all correspondence to: shyam@amrita.edu
and multi-configuration data relating gait and EEG may be needed, this step helps
to propose a pre-clinical assessment tool for rural communities, especially when
multi-specialty hospitals may consider outreach or where specialists may need
more time to understand movement related conditions prior to an actual diagnosis.
With useful preliminary results that supports gait as a BCI technology, it further
warrants the need to investigate the utility of mobile phone sensors for extracting
accelerometer-based data and its use in a patient population.
Acknowledgements
This work derives direction and ideas from the Chancellor of Amrita University,
Sri Mata Amritanandamayi Devi. This work was partially supported by Grants
DST/CSRI/2017/31 of Department of Science and Technology, Visvesvaraya
Fellowship, Ministry of Electronics and IT, Government of India and by Embracing
The World, Research-for-a-Cause-Initiative.
Conflict of interest
The authors declare no conflict of interest.
© 2020 The Author(s). Licensee IntechOpen. Distributed under the terms of the Creative
Commons Attribution - NonCommercial 4.0 License (https://creativecommons.org/
licenses/by-nc/4.0/), which permits use, distribution and reproduction for
non-commercial purposes, provided the original is properly cited.