A5052177563- Muscle activation and electromyography studies
- Hand Gesture Recognition Systems
- EEG and Brain-Computer Interfaces
- Machine Learning in Materials Science
- Gaze Tracking and Assistive Technology
- Advanced Memory and Neural Computing
- Ferroelectric and Negative Capacitance Devices
Tsinghua University
2024
Southeast University
2021-2022
Hand gesture recognition tasks based on surface electromyography (sEMG) are vital in human-computer interaction, speech detection, robot control, and rehabilitation applications. However, existing models, whether traditional machine learnings (ML) or other state-of-the-arts, limited the number of movements. Targeting a large classes, more data features such as temporal information should be persisted much possible. In field sEMG-based recognitions, recurrent convolutional neural network...
In this paper, a low-cost wearable hand gesture detecting system based on distributed multi-node inertial measurement units (IMUs) and central node microcontroller is presented. It can obtain kinematic information transmit data to the remote processing terminal wirelessly. To have comprehensive understanding of kinematics, convolutional neural network (CNN) model proposed recognize classify gestures modified Denavit-Hartenberg notation used acquire finger spatial locations. The experiment...
Non-volatile memory-based computing-in-memory (nvCIM) is a promising candidate for accelerating deep neural networks (DNNs) at the edge. However, current nvCIMs adopt fully-pipelined (FP) or layer-serial (LS) dataflows all DNN layers, suffering poor area and energy efficiency layer-wise-varied workloads. Furthermore, their fixed macro structure results in resource under-utilization, as it unable to adapt varying weight sizes. To address these issues, this brief proposes reconfigurable nvCIM...