Download

• Paper (IEEE Xplore)

Abstract

This paper addresses the critical need for privacy preservation in EEG data captured by commercial headwear. EEG signals contain sensitive information regarding mental states, cognitive processes, and health conditions, making privacy protection a key challenge for wearable brain-computer interfaces. We propose a hardware-assisted privacy-preserving approach for multi-channel EEG computational headwear.

Citation

Radmehr, Amirmohammad. 2024. “Hardware-Assisted Privacy-Preserving Multi-Channel EEG Computational Headwear.” In 2024 IEEE 20th International Conference on Body Sensor Networks (BSN). IEEE. https://doi.org/10.1109/BSN63547.2024.10780473.

@inproceedings{radmehr2024eeg,
  author = {Radmehr, Amirmohammad},
  title = {Hardware-Assisted Privacy-Preserving Multi-Channel EEG Computational Headwear},
  booktitle = {2024 IEEE 20th International Conference on Body Sensor Networks (BSN)},
  year = {2024},
  doi = {10.1109/BSN63547.2024.10780473}
}

Download

• Paper (ACM Digital Library)

Abstract

IOTeeth is a cost-effective and automated intra-oral sensing system for continuous and fine-grained monitoring of occlusal diseases. It incorporates an intra-oral piezoelectric-based sensing array integrated into a dental retainer platform. The system monitors and analyzes teeth contact-induced vibration signals during oral activities to recognize the presence of occlusal diseases, focusing on biting and grinding activities from canines and front teeth. A deep learning model called Physio-aware Attention Network (PAN Net) is used for occlusal disease recognition.

Results

• F1 score of 0.97 for activity recognition with leave-one-out validation
• Average F1 score of 0.92 for dental disease recognition across different activities

Citation

Hu, Zhizhang, Amirmohammad Radmehr, Yue Zhang, Shijia Pan, and Phuc Nguyen. 2024. “IOTeeth: Intra-Oral Teeth Sensing System for Dental Occlusal Diseases Recognition.” Proc. ACM Interact. Mob. Wearable Ubiquittic Technol. 8 (1): Article 7. https://doi.org/10.1145/3643516.

@article{hu2024ioteeth,
  author = {Hu, Zhizhang and Radmehr, Amirmohammad and Zhang, Yue and Pan, Shijia and Nguyen, Phuc},
  title = {IOTeeth: Intra-Oral Teeth Sensing System for Dental Occlusal Diseases Recognition},
  journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.},
  volume = {8},
  number = {1},
  articleno = {7},
  year = {2024},
  doi = {10.1145/3643516}
}

Download

• Paper (IEEE Xplore)

Abstract

This paper proposes a method to mimic human face and eye movements by combining computer vision techniques and neural network concepts. A 3-DOF spherical parallel robot (Agile Eye) is used for head movement imitation, with a 2-DOF mechanism attached to its end-effector for eye movement. The study uses the Mediapipe library to extract face mesh data and obtain the roll, yaw, and pitch angles. A linear regression model was trained to calculate face angles, and experimental tests conducted on a ROS platform demonstrated the effectiveness of the proposed methods in tracking and imitating human head and eye movements with high accuracy.

Citation

Radmehr, Amir, Milad Asgari, and Mehdi Tale Masouleh. 2021. “Experimental Study on the Imitation of the Human Head-and-Eye Pose Using the 3-DOF Agile Eye Parallel Robot with ROS and Mediapipe Framework.” In 2021 9th RSI International Conference on Robotics and Mechatronics (ICRoM), 579–584. IEEE. https://doi.org/10.1109/ICRoM54204.2021.9663445.

@inproceedings{radmehr2021agile,
  author = {Radmehr, Amir and Asgari, Milad and Masouleh, Mehdi Tale},
  title = {Experimental Study on the Imitation of the Human Head-and-Eye Pose Using the 3-DOF Agile Eye Parallel Robot with ROS and Mediapipe Framework},
  booktitle = {2021 9th RSI International Conference on Robotics and Mechatronics (ICRoM)},
  pages = {579--584},
  year = {2021},
  doi = {10.1109/ICRoM54204.2021.9663445}
}