Biomedical subjects

K Y Tong

Publications and source records attributed to K Y Tong.

4 recordsLinked to original sources

PubMed2001

Development of computer-based environment for simulating the voluntary upper-limb movements of persons with disability.

Upper-limb orthotic systems have been designed for restoring the upper-limb functions of individuals with disabilities resulting from spinal cord injury (SCI), stroke and muscular dystrophy. These systems employ either functional electrical stimulation or external power. It is proposed that, instead of time-consuming and complicated monitoring using sensors and motion analysis, a software simulator with both angular displacement and acceleration parameters can facilitate the design of a control strategy for an orthosis. Reaching movements of three cervical SCI subjects are used to verify the simulator. A motion analysis system is used to measure the range of motion and joint angles during hand reaching. Results indicate that quaternion and spline curve techniques are suitable for interpolation of the hand reaching movements. The information needed for good simulation only compress the shoulder and elbow joint angles in a few key postures. Stimulated acceleration signals on the upper-arm segment have a high correlation coefficient (> 0.9) and a small root mean squared error (< 0.11 g) with a real bi-axial accelerometer.

Arm↗

PubMed1999

Gait control system for functional electrical stimulation using neural networks.

In functional electrical stimulation (FES) systems for restoring walking in spinal cord injured (SCI) individuals, hand switches are the preferred method for controlling stimulation timing. Through practice the user becomes an 'expert' in determining when stimulation should be applied. Neural networks have been used to 'clone' this expertise but these applications have used small numbers of sensors, and their structure has used a binary output, giving rise to possible controller oscillations. It was proposed that a three-layer structure neural network with continuous function, using a larger number of sensors, including 'virtual' sensors, can be used to 'clone' this expertise to produce good controllers. Using a sensor set of ten force sensors and another of 13 'virtual' kinematic sensors, a good FES control system was constructed using a three-layer neural network with five hidden nodes. The sensor set comprising three sensors showed the best performance. The accuracy of the optimum three-sensor set for the force sensors and the virtual kinematic sensors was 90% and 93%, respectively, compared with 81% and 77% for a heel switch. With 32 synchronised sensors, binary neural networks and continuous neural networks were constructed and compared. The networks using continuous function had significantly fewer oscillations. Continuous neural networks offer the ability to generate good FES controllers.

Electric Stimulation↗

PubMed1999

Reliability of neural-network functional electrical stimulation gait-control system.

Functional electrical stimulation (FES) has been used for restoring walking in spinal-cord injured (SCI) persons. Using artificial intelligence (AI), FES controllers have been developed that allow the automatic phasing of stimulation, to replace the function of hand or heel switches. However, there has been no study to evaluate the reliability of these AI systems. Neural networks were used to construct FES controllers to control the timing of stimulation. Different numbers of sensors in the sensor set and different numbers of data points from each sensor were used. Two incomplete-SCI subjects were recruited, and each was tested on three separate occasions. The results show the neural-network controllers can maintain a high accuracy (around 90% for the two- and three-sensor groups and 80% for the one-sensor group) over a period of six months. Two or three sensors were sufficient to provide enough information to construct a reliable FES control system, and the number of data points did not have any effect on the reliability of the system.

Adult↗

PubMed1998

Virtual artificial sensor technique for functional electrical stimulation.

In the control of Functional Electrical Stimulation (FES) gait systems artificial sensors are used to provide the controller with feedback information. The sensors used range in complexity from simple heel or hand switches to tri-axial accelerometers. There are three basic problems connected with the selection of sensors: the type(s) of sensor(s) to be used, the number of sensors required and the optimum location of the sensor set. In general the choice of the sensor sets has been based on the availability of actual sensors and the experts understanding of where these sensors should be located. Using motion analysis data it is possible to construct an almost unlimited number of virtual sensors on any location of the body surface. Our aim was to develop this technique for construction of virtual sensors and compare these virtual sensors with their physical counterparts. Virtual goniometers, inclinometers, accelerometers and foot switches were constructed and compared with their physical counterparts. In addition visualisation tools were developed to aid in the choice of sensor location. There was a very good correlation between all the virtual and physical sensors. This technique gives flexibility to place virtual sensors almost anywhere on the body surface and also allows the construction of novel sensors.

Acceleration↗