Finger Movement Classification
using a neural net
Eduard Heindl (IAP Tübingen, 1995)
We used a CMSTM
ultrasound based coordinate measure system with 8 markers. (TM zebris Germany)
The makers send short ultrasonic impuls signals,
which were collected by 3 micro-phones. The position (x,y,z) of the markers
was then determined from the receiving time at the micros by a computer.
The repetition rate was 20 Hz.
The Angle of the Joints
After the collection of the raw data (x,y,z), we
calculated the angle between the segments from the measured coordinates of
the 8 markers using trigonometric equations.
Generating a Standard Pattern
To generate a standard pattern, we collected the significant part of the
opening and closing of the fist.
Training of the Neuronal Net
1. Finding the two patterns xk,xl
with the longest distance Dmax
2. Initializing the Self Organizing Map (SOM) with arbitrary patterns.
3. Training of the SOM using 145 training patterns and the patterns xk,xl in the corner of the
The Resulting SOM Net
The net contains
100 patterns, which represent the most significant patterns in topological
order. If it is similar to a movement of a patients with ulnar nerve palsy it
is plotted in red, if there was response to each groups - grey, no response
light and healthy in green color.