Evaluation of Optical Myography Sensor as Predictor of Hand Postures

Optical myography stands as one of many techniques to assess hand postures. As a combination of computer vision and muscular activity analysis, it differs from conventional gesture recognition techniques, such as instrumented gloves or optical tracking, by not relying on the existence of a healthy hand, so it is able to detect the hand motion as well as the motion intent. In this aspect, optical myography is like well-established myographic approaches, such as surface electromyography or force myography, but it is simpler, more comfortable, and inexpensive. This recent technology is hereby evaluated upon the construction of a feasible and low-cost sensor that monitors both the front and the back of the forearm. The results are organized into two sections: the first validates the sensor and the second evaluates its performance as a predictor of eight static postures, including the thumb and the fingers motion. In the end, the sensor proved to be comparable to more mature techniques with an F-score of ~92.2% and ~71.5% for front- and back-side analysis, respectively.