Evaluating a Biosensor-Based Interface to Recognize Hand-Finger Gestures Using a Myo Armband

Gesture recognition is a convenient and natural Human Computer Interaction (HCI) technique. Recent advances in bioengineering have seen the use of biosensor technologies in HCI, since various hiosensors provide real-time feedback from biological activities. This has enabled User Interface (UI) designers to design more natural UN, including the use of a Muscle -Computer Interface (MCI). This paper presents an evaluation of an MCI designed to recognize four -finger pinching, fist action and five -hand-fingers spread gestures using Electromyography (EMG) signals from a Myo armband from Thalmic Labs Inc An experimental research strategy was used and a Feedforward Neural Network was implemented to classify the gestures and each of the gestures was trained for 3 seconds. An average 95% success rate for completing gesture -posing tasks among 6 participants was achieved with an average predicting error value of 11.48, expressed by the Root Mean Square Error (RMST). The results illustrate the application of hiosensors in gesture recognition as a modern and reliable approach which benefits the IICI. Biosensor-based gesture recognition provided a greater level of accessibility and encumbered the users less wIwn this approach was compared with vision- and sensor -based approaches. The Myo armband showed that it is an economical and standard bio-sensing wearable device that can he successhilly used in hand -finger gesture recognition.