Finger Motion Detection Based On SEMG

Fingers and palm could concert to make lots of motion which is skillful and complex, different fingers had different functions according to the variety of the motion. Finger force was an important parameter to reflect finger synergetic motion and evaluate hand movement function. The restriction of environment and situation was the defects of the conventional sensors which had been used to gather the force parameter. Finger motion was controlled by the muscles of forearm, while the sEMG signal reflected the status of the never and muscle, so there might be some relationship between sEMG signal and finger motion, the sEMG (surface electromyography) signal could be used to estimate the motion mode of fingers, and it would play an important role in the research of mechanics, prosthetic hand, and rehabilitation training.First, an experiment system using for detecting finger force had been established. The LabVIEW-based finger force feedback software was designed, which could display the results of real-time acquisition of finger force and force track. Then, three experiments were conducted: (1) the experiment of single finger pressing at 4N, 6N, 8N; (2) the experiment of index and middle finger combined pressing at 10%, 20%, 30% MVC (maximal voluntary contraction); (3) the experiment of index and middle finger combined pressing force at 30%MVC, and index, middle finger force rate at 1:1 and 5:2. And the sEMG signal of FDS (flex digitorum superficials) and ED (extensor digitorum) was recorded. Next, the sEMG signal was filtered, and RMS (root mean square), AR model coefficient, C0 complexity as the characteristic of sEMG signal was calculated. After that, the RMS value of sEMG signal at different force had been compared, so did the C0 complexity in different force proportion. The motion of index finger or middle finger which pressed at a same force had been classified by the use of PNN (probabilistic neural networks) and LVQ (learning vector quantization) neural network. Some result could be obtained by analyzing the sEMG signal: (1) RMS value of both FDS and ED's sEMG signal increased with index or middle finger force increased. When index finger pressed, the C0 value of FDS and ED's sEMG signal was larger than middle finger pressed. (2). Using C0 value or AR coefficient as the input of the LVQ or PNN neural network could classify the motion between index finger and middle finger, and the correct rate was above 80%. (3). Using C0 value or AR coefficient as the input of the PNN neural network could classify the motion between index finger and middle finger, and the correct rate was above 80% except the motion at 4N. (4). For a single subject, The maximum correct recognition rate of all finger force level movement by using RMS and C0 value, or RMS and AR coefficient as the input parameter of PNN neural network could be 95%. (5). RMS value of both FDS and ED increased with index and middle finger combined force increased. (6). The RMS and C0 value didn't showed differential when index and middle finger combined force at 30%MVC, and index, middle finger force rate at 1:1 and 5:2.The results of experiment showed that, a correlation between finger motion and sEMG signal had been represented. As the characteristic of sEMG signal, the RMS value, AR coefficient, and C0 complexity could reflect muscle active level, and could estimate finger motion. When single finger force increased or multi-finger combined force increased, the RMS of sEMG signal would raise. Using C0 value or AR coefficient as the input of the LVQ or PNN neural network could classify the motion between index finger and middle finger. The difference of sEMG signal didn't represent when index and middle finger combined force at 30%MVC, and index, middle finger force rate at 1:1 and 5:2.