Classification of Upper Limb Motions from Around-Shoulder Muscle Activities

Abstract

In recent years, detecting upper-limb motion intention has attracted growing research attention in order to improve the manipulation of a prosthetic hand. Recording of forearm muscle activity has been used as a signal source to detect wrist and hand motions using different pattern recognition techniques. However, it is difficult to take in consideration body coordinated motions from these signals alone; therefore the movement of the artificial limb can be unnatural, if consider as a part of the whole body coordination, and a dynamical coupling between the user and the prosthesis is impossible. Also, using only forearm muscle activities to drive the artificial limb leaves aside the possibility for higher level amputees to use these systems. It is well known that most daily-life upper limb activities present a coordination of the shoulder-arm-hand complex. For example, the shoulder, elbow and hand's trajectories are tightly coupled when reaching and grasping an object, or when throwing and catching a ball. It is because of this dependency that research effort had been done to differentiate hand motions using EMG activity of proximal muscles. For example, C. Martelloni et al were able to discriminate different grip types from EMG signals of proximal and distal muscles by statistical means. Also, Xiao Hu et al compared the performance of a Scalar Autoregresive model with a Multivariate AR modeling using EMG data obtained from the bicep, tricep, deltoid, and brachioradialis, successfully classifying different arm movements. Although the results obtained are encouraging, relying only on EMG information is not accurate enough for a robust dynamical control of a prosthetic hand since it is still complex to classify and interpret the information acquired in real time. In a previous study, we showed the possibility of improving the discrimination rate by using accelerometers, to detect kinematical information from the around-shoulder muscles, along with the EMG signals. In that study we obtained EMG and accelerometer information from only proximal muscles and used an off-line neural network classifier to discriminate different grips and arm positions. Therefore, the objective of this study was to investigate the possibility of associating around-shoulder muscle activity with different grasps and arm positions while reaching for an object using an on-line recognition method. This way we might be able to deduce the user's motion intention and coordinate the prosthetic arm position and movements to his body in a dynamical way.