Classification of Upper Limb Motions from Around-Shoulder Muscle Activities: Hand Biofeedback

Abstract

Mining information from EMG signals to detect complex motion intention has attracted growing research attention, especially for upper-limb prosthetic hand applications. In most of the studies, recordings of forearm muscle activities were used as the signal sources, from which the intention of wrist and hand motions were detected using pattern recognition technology. However, most daily-life upper limb activities need coordination of the shoulder-arm-hand complex, therefore, relying only on the local information to recognize the body coordinated motion has many disadvantages because natural continuous arm-hand motions can't be realized. Also, achieving a dynamical coupling between the user and the prosthesis will not be possible. This study objective was to investigate whether it is possible to associate the around-shoulder muscles' Electromyogram (EMG) activities with the different hand grips and arm directions movements. Experiments were conducted to record the EMG of different arm and hand motions and the data were analyzed to decide the contribution of each sensor, in order to distinguish the arm-hand motions as a function of the reaching time. Results showed that it is possible to differentiate hand grips and arm position while doing a reaching and grasping task. Also, these results are of great importance as one step to achieve a close loop dynamical coupling between the user and the prosthesis.

EMG-based decoding of grasp gestures in reaching-to-grasping motions

“…As result, these devices would increase their effectiveness and usability, and consequently increase the natural transition between the reaching and grasping phase on the prostheses increasing their acceptance by patients. However, at the moment only a limited number of studies focused on the detection of different grasp movements during reaching and grasping motions [25][26][27], and no measurement were performed to assess when a good classification was achieved respect to the hand's preshape .…”

Psycho-physiological assessment of a prosthetic hand sensory feedback system based on an auditory display: a preliminary study

“…This system was designed to enhance motor-sensory performance and awareness during the manipulation of a robot hand [21,22]. In this system different types of grasp (e.g.…”

“…Our research group developed a sensory feedback system using an auditory display, which is used as a redundant source of both kinematic and somatosensory information in prosthetic applications. This system was designed to enhance motor-sensory performance and awareness during the manipulation of a robot hand [ 21 , 22 ]. In this system different types of grasp (e.g.…”

“…This situation seems to lead to a high conscious burden for the users, which generates fatigue and frustration [ 1 , 2 ]. This lack of sensory feedback is a major drawback that many researchers are trying to cope with by using indirect methods to convey information from the artificial limb to the amputee, such as electro-cutaneous stimulation [ 3 - 8 ], vibrotactile stimulation [ 8 - 12 ], force stimulation [ 13 - 18 ], or auditory cues [ 19 - 22 ]. Although the results obtained are very positive, the usability and advantages of these feedback methods were explored mainly by looking at the performance results, which do not take into account measurements of the user’s mental effort, attention, and emotions.…”

Estimation of Targeted-Reaching-Positions by Around-Shoulder Muscle Activities and Images from an Action Camera for Trans-Humeral Prosthesis Control

“…In [6,7], the EMG (electromyogram) signals from the around-shoulder area (ASA), and in [8], the EMG from the ASA, together with additional motion-related EEG were used, and machine learning methods were employed to explore the limited information.…”