• 1
  • 2
  • 3
  • 4
  • 5




Our research is geared toward improving quality of life for each amputee. Currently, our laboratory is working to develop bionic articial legs, design smart prostheses, and to create aerent stimulation for improved walking stability.


1.Design of neural-machine interface (NMI) based on neuromuscular information to accurately and reliably decipher the user's movement intents


Surface Electromyographic (EMG) signals are one of the major neural control sources for powered prostheses, experimental motorized orthotics, rehabilitation robots, and other assistive devices. Our research goal is to develop a robust neural-machine interface based on EMG signals that can accurately identify user intent. The designed technology can allow patients with various motor deficits to intuitively control external devices, which can compensate their lost motor function, assist their activities of daily living, or retrain their motor skills.


Testing a Real-time Neural-Machine Interface Using Virtual Reality


Description: This video shows the real-time prototype testing of a novel neural machine interface (NMI) on one transfemoral amputee who wore a prosthesis when performing level-ground walking, stair ascent and stair descent tasks. The NMI system senses to drive cyber virtual reality (VR) for the purpose of evaluating a neural-machine interface (NMI) for artificial legs. When walking on different terrains, the subject's locomotion intent was recognized by a neuromuscular-mechanical fusion algorithm and fed into the virtual reality cyber system. The real-time motion of the avatar was displayed on a TV monitor to animate exactly the movement intents of the subject. The system demonstrates high accuracy and reasonable latency, which can be an effective evaluation and training tool for leg amputees to neurally control their artificial legs.



NMI recognizing simple task transitions between sitting and standing


Description: This video shows the real-time recognition of task transitions between sitting and standing based on the EMG signals of gluteal and thigh muscles in one side of leg. An embedded system based on Freescale's MPC5566 was designed for real time operation. When the subject was in the sitting position, the output indicator on the monitor showed value "1"; when the subject was in the standing position, the output value was "0". The system presents high accuracy for identifying user intent. Although the subject moved the instrumented side of leg and shifted the body weight in the sitting or standing position, no erroneous task switch was presented.





2.Implementation of neural-machine interface (NMI) on powerful embedded computer systems


Implementing the neural interfacing algorithms on an embedded computer system is essential to make the EMG based NMIs practical and available to patients with leg amputations. The speed of the embedded system must be adequate because any delayed decision-making from the NMI also introduces instability and unsafe use of prostheses. Streaming and storing multiple sensor data, deciphering user intent, and running sensor monitoring algorithms at the same time superimpose a great challenge to the design of an embedded system for the NMI of artificial legs.


Real-time Implementation of Neural-Machine Interface on an Embedded System

Description: This video shows the real-time implementation of our developed neural machine interface (NMI) on a high-performance embedded system. The embedded system consists of two parts: a microcontroller unit for sensing the neuromuscular signals from human body and mechanical measurements from the prosthesis, and an FPGA device as the computing engine for fast decoding and pattern recognition. The designed NMI prototype was tested on an able-bodied subject for accurately classifying multiple movement tasks (level-ground walking, stair ascent, and standing) in real-time. The results demonstrated the feasibility of a self-contained and high performance real-time NMI for artificial legs.





3.Development of trust sensor interface for improving reliability of neural-machine interface (NMI)


To achieve natural and smooth control of prostheses, EMG signals have been investigated for decoding user intent. However, environmental uncertainty, such as perspiration, temperature change, and movement between the residual limb and prosthetic socket can cause unexpected sensor failure, influence the recorded EMG signals, and reduce the trustworthiness of the NMI. Therefore, it is critical to develop a reliable and trustworthy NMI for safe use of prosthetic legs.


Real-time Performance of a Trust Sensor Interface


Description: It is critical to develop a reliable and trustworthy NMI for safe use of prosthetic legs. To achieve this goal, a trust sensor interface (TSI) was designed to handle diverse disturbances originated in uncertain environment, which may distort EMG signals, cause errors in the NMI, and threaten the safety of amputees. Preliminary implementation of designed trust sensor interface was conducted on an able-bodied human subject when he was performing sitting and standing. The result shows the accurate and fast detection of disturbances and demonstrates a great potential in improving the reliability of the neural machine interface.