Science

Vision

Freer Industries’s scientific vision for the future is one in which humans and robots can harmoniously work together for the benefit of society and the universe. One of the main applications for this is in assistive robotics, which can be robots that assist the elderly or disabled in their daily lives, but can additionally extend to assisting healthy individuals in carrying out dangerous, difficult, or dull tasks. In my research, I have investigated how to train robots to understand humans through advanced processing of sensed physiological data, and have additionally explored how humans can understand robots through better training protocols which bring humans more smoothly into the technological loop.

For more information, see the figures and read the publications below.

Tutorials

Figures

Selected Publications Google Scholar

  1. Daniel Freer, Guang-Zhong Yang. “MIndGrasp: A New Training and Testing Framework for Motor Imagery Based 3-Dimensional Assistive Robotic Control”, 2020 Conference on Intelligent Robots and Systems (IROS). (under review) Link Code
  2. Daniel Freer, Yao Guo, Fani Deligianni, Guang-Zhong Yang. “On-Orbit Operations Simulator for Workload Measurement During Telerobotic Training”, IEEE Robotics and Automation Letters. (under review) Link Code
  3. Daniel Freer, Guang-Zhong Yang. “Data Augmentation for Self-Paced Motor Imagery Classification with C-LSTM”, Journal of Neural Engineering, 2020, pp.16-41. Link Code
  4. Chen Wang*, Daniel Freer*, Jindong Liu, Guang-Zhong Yang. “Vision-Based Automatic Control of a 5-Fingered Assistive Robotic Manipulator for Activities of Daily Living”, 2019 Conference on Intelligent Robots and Systems (IROS). Link
  5. Daniel Freer, Fani Deligianni, Guang-Zhong Yang. “Adaptive Riemannian BCI for Enhanced Motor Imagery Training Protocols”, 2019 IEEE 16th Conference on Wearable and Implantable Body Sensor Networks (BSN). Link Code
  6. Daniel Freer, Jindong Liu, Guang-Zhong Yang. “Optimization of EMG Movement Recognition for Use in an Upper-Limb Wearable Robot”, 2017 IEEE 14th Conference on Wearable and Implantable Body Sensor Networks (BSN). Link