Md. Nazmus Sahadat
Orcid: 0000-0001-6319-156XAffiliations:
- Georgia Institute of Technology, Atlanta, GA, USA
According to our database1,
Md. Nazmus Sahadat
authored at least 13 papers
between 2014 and 2022.
Collaborative distances:
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Bibliography
2022
Evaluation of a Head-Tongue Controller for Power Wheelchair Driving by People With Quadriplegia.
IEEE Trans. Biomed. Eng., 2022
2019
Design and evaluation of a multimodal assistive technology using tongue commands, head movements, and speech recognition for people with tetraplegia.
PhD thesis, 2019
A Stand-Alone Intraoral Tongue-Controlled Computer Interface for People With Tetraplegia.
IEEE Trans. Biomed. Circuits Syst., 2019
2018
Simultaneous Multimodal PC Access for People With Disabilities by Integrating Head Tracking, Speech Recognition, and Tongue Motion.
IEEE Trans. Biomed. Circuits Syst., 2018
Simultaneous Multimodal Access to Wheelchair and Computer for People with Tetraplegia.
Proceedings of the 2018 on International Conference on Multimodal Interaction, 2018
Standalone Assistive System to Employ Multiple Remaining Abilities in People with Tetraplegia.
Proceedings of the 2018 IEEE Biomedical Circuits and Systems Conference, 2018
Development and Preliminary Assessment of an Arch-Shaped Stand-Alone Intraoral Tongue Drive System for People with Tetraplegia.
Proceedings of the 2018 IEEE Biomedical Circuits and Systems Conference, 2018
2015
Proceedings of the IEEE Biomedical Circuits and Systems Conference, 2015
Live demonstration: A tongue-operated multimodal human computer interface and robotic rehabilitation system.
Proceedings of the IEEE Biomedical Circuits and Systems Conference, 2015
A multimodal human computer interface combining head movement, speech and tongue motion for people with severe disabilities.
Proceedings of the IEEE Biomedical Circuits and Systems Conference, 2015
Live demonstration: Towards an ultra low power on-board processor for Tongue Drive System.
Proceedings of the IEEE Biomedical Circuits and Systems Conference, 2015
2014
Hardware-efficient robust biometric identification from 0.58 second template and 12 features of limb (Lead I) ECG signal using logistic regression classifier.
Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014
Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014