Preliminary results of online classification of upper limb motions from around-shoulder muscle activities.

Recently, detecting upper-limb motion intention for prosthetic control purpose attracted growing research attention. 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. The disadvantage of relying only on the local information to recognize a whole body coordinated motion is that misrecognition could easily happen, so that steady and reliable continuous motions could not be realized. Moreover, using forearm muscle activities would limit the use of the system for higher level amputation patients. Therefore, in this study we aimed to explore the feasibility of using an online classification algorithm to test the intention detection in real time. Experiments were conducted to record around-shoulder muscle activity using EMG and acceleration sensors. Then, a neural network was trained using these data, and finally tested online in a set of tests. Results showed that, from 5 channels of Electromyogram (EMG) and 4 channels of accelerometers, it is possible to discriminate 3 different grips and 5 reaching direction of arm.

A study on the forearm muscular reflexes during grasping for prosthetic applications.

Recently, there has been an increasing interest in upper-limb prosthetic hand control, but most of these studies focus on the detection of exact motion intentions. Therefore, the responses to unexpected disturbance are not taken into consideration. On the other hand, unimpaired people respond to external disturbances by reflexive responses, hence, it is important to explore how this kind of reactive responses could be applied into prosthetic hand applications, and whether it will improve the human-machine interaction in a dynamical way. Our objective for the present study was to examine the responses of the human reflexes on different conditions in order to apply them to our prosthetic hand. Electromyograph (EMG) signals were recorded from the forearm muscles of unimpaired people during grasping of a cylinder. Results showed that the reflexes have different tendencies depending on the direction on which the disturbance is applied.

Finger motion classification by forearm skin surface vibration signals.

The development of prosthetic hand systems with both decoration and motion functionality for hand amputees has attracted wide research interests. Motion-related myoelectric potentials measured from the surface of upper part of forearms were mostly employed to construct the interface between amputees and prosthesis.However, finger motions, which play a major role in dexterous hand activities, could not be recognized from surface EMG (Electromyogram) signals.The basic idea of this study is to use motion-related surface vibration, to detect independent finger motions without using EMG signals. In this research, accelerometers were used in a finger tapping experiment to collect the finger motion related mechanical vibration patterns. Since the basic properties of the signals are unknown, a norm based, a correlation coefficient based, and a power spectrum based method were applied to the signals for feature extraction. The extracted features were then fed to back-propagation neural networks to classify for different finger motions.The results showed that, the finger motion identification is possible by using the neural networks to recognize vibration patterns.

Classification of upper limb motions from around-shoulder muscle activities: hand biofeedback.

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.