Mining information from EMG signals to detect complex motion intention has attracted growing research attention, especially for upper-limb prosthetic hand applications [1Akira O Otsuka, Tsuji T, Fulida O, Sakawa M. Research on upper extremity prosthesis based on human motion analysis-development of internally powered functional-cosmetics prosthetic hand J Jpn Med Instr 2002; 75(5): 220-7.]. In previous studies, it has been reported that up to 10 wrist and hand motions could be recognized from 2-3 channels of forearm electromyogram (EMG) [2Nishikawa D, Yu W, Yokoi H, Kakazu Y. On-line supervising mechanism for learning data in surface electromyogram motion classifiers IEICE Trans Inform Syst 2001; J84-D(12): 2634-43.,3Nisikawa D, Yu W, Yokoi H, Kakazu Y. On-line learning methods for emg prosthetic hand controlling Trans Inst Electron Inform Commun Eng E 1999; J82-D-II(9): 1510-9.]. Other studies have used non-stationary EMG at the beginning of motion [4Tsukamoto M, Kondo T, Ito K. A prosthetic hand control by non stationary emg at the beginning of motion Trans Inst Electron Inform Commun Eng E 2006; 105(577): 41-.] or mechanomyogram (MMG) as the signal source for the motion intention detection [5Tsuji T, Yoshihiro T, Shima K. An MMG-based control method of prosthetic manipulators using acceleration sensors Robot Soc Jpn 2007; 25(6): 874-0.]. In these studies, motion intention of the wrist and hand were detected using patter recognition techniques from recordings of forearm muscle activities. However, it is difficult to take in consideration the 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, and a dynamical coupling between the person and the prosthesis is not possible. Also, using forearm muscle activities to drive the artificial limb leaves aside the possibility for higher level amputees to use the system.

It has been shown that most daily-life upper limb activities present coordination in the shoulder-arm-hand complex. For example, it has been shown that during grasping and reaching tasks [6Lacquaniti F, Soechting JF. Coordination of arm and wrist motion during a reaching task J Neuro Sci 1982; 2(4): 399-408.-9Cothros N, Wong JD, Gribble PL. Are there distinct neural representations of object and limb dynamics Exp Brain Res 2006; 173: 689-97.], or throwing and catching a ball [10Thiel VE, Steenbergen B. Shoulder and hand displacements during hitting, reaching, and grasping movements in hemiparetic cerebral palsy Motor Control 2001; 5(2): 166-82.], the shoulder, elbow and hands’ trajectory are tightly coupled. Also, it has been discussed that this coupling is also task and situation dependent, such as reaching and grasping an object in different places and in different orientations [11Wilson FR. The Hand: How its use shapes the brain, language, and human culture. New York: Pantheon Books 1998.].

It is because of this dependency that research effort had been done to differentiate hand motions using EMG activity of proximal muscles [12Martellon Carpaneto J, Micera S. Classification of upper arm EMG signals during object-specific grasp In: 30th Annual International IEEE EMBS Conference; 2008; pp. 2061-5064.-15Koike Y, Kawato M. Trajectory formation from surface EMG signals using a neural network model Trans Inst Electron Inform Commun Eng E 1994; J77-D-II(1): 193-203.]. However, it is still complex to classify and interpret the information acquired. C. Martelloni, et al., in [12Martellon Carpaneto J, Micera S. Classification of upper arm EMG signals during object-specific grasp In: 30th Annual International IEEE EMBS Conference; 2008; pp. 2061-5064.], used a Support Vector Machine based pattern recognition algorithm as an attempt to predict different grips, trying to reproduce the finding in monkeys described in [14Brochier T, Spinks RL, Umilta MA, Lemon RN. Patterns of muscle activity underlying object-specific grasp by the macaque monkey J Neurophysiol 2004; 92: 1770-82.]. They were able to discriminated 3 different grips (palmar, lateral or pinch grip), but they used data obtained from the forearm muscles activities of the flexor carpi radialis and extensor carpi radialis, which have been used in the previous studies to control prosthetic hands [2Nishikawa D, Yu W, Yokoi H, Kakazu Y. On-line supervising mechanism for learning data in surface electromyogram motion classifiers IEICE Trans Inform Syst 2001; J84-D(12): 2634-43.-5Tsuji T, Yoshihiro T, Shima K. An MMG-based control method of prosthetic manipulators using acceleration sensors Robot Soc Jpn 2007; 25(6): 874-0.,16Kyberd PJ, Pons JL. A comparison of the oxford and manus intelligent hand prostheses In: Proceedings of the Conference on Robotics & Autom; Sep. 2003; Taiwan. 14-9.]. Therefore, this makes it difficult to determine the contribution the proximal muscles to the motion detection of the hand. Also, in [13Hu X, Nenov V. Multivariate AR modeling of electromyography for the classification of upper arm movements Clin Neurophysiol 2004; 115(6): 1276-87.], Xiao Hu, et al. compared the performance of a Scalar Autoregresive model with a Multivariate AR modeling for multichannel EMG sensors in order to classify upper arm movements. Using data obtained from the bicep, tricep, deltoid and brachioradialis. They were able to classify accurately different arm movements. Although the results are encouraging, they focus the attention on the contribution of the muscles as a whole, and insights on the contribution of individual muscles is lost. Besides they didn’t attempt to predict hand positions. Additionally, Y. Koike et al., in [15Koike Y, Kawato M. Trajectory formation from surface EMG signals using a neural network model Trans Inst Electron Inform Commun Eng E 1994; J77-D-II(1): 193-203.], developed a forward dynamics model of the human arm from EMG reading of proximal muscles using neural networks, but didn’t attempt to predict hand positions.

In this study we wanted to investigate the contribution of the around-shoulder muscle activities to the hand and arm direction movements, in order to explore the possibility of detecting the person’s intentions in a dynamical way (during the reaching process). Experiments were conducted to record the EMG signals from the around-shoulder muscles of different arm and hand motions, and to analyze the contribution of these sites to distinguish different hand and arm motions.

The rapid development of light weighted dexterous prosthetic hand its needing more research on new data mining methods in order to detect dynamical motion intention. New techniques and algorithms will allow taking full advantage of the many degrees of freedom of these artificial limbs, and hopefully will allow the user to acquire more natural and intuitive control and manipulation of the prosthesis. Different data mining methods have been used to extract and predict information from EMG sensors, being neural networks the most commonly used [1Akira O Otsuka, Tsuji T, Fulida O, Sakawa M. Research on upper extremity prosthesis based on human motion analysis-development of internally powered functional-cosmetics prosthetic hand J Jpn Med Instr 2002; 75(5): 220-7.-3Nisikawa D, Yu W, Yokoi H, Kakazu Y. On-line learning methods for emg prosthetic hand controlling Trans Inst Electron Inform Commun Eng E 1999; J82-D-II(9): 1510-9., 12Martellon Carpaneto J, Micera S. Classification of upper arm EMG signals during object-specific grasp In: 30th Annual International IEEE EMBS Conference; 2008; pp. 2061-5064.-16Kyberd PJ, Pons JL. A comparison of the oxford and manus intelligent hand prostheses In: Proceedings of the Conference on Robotics & Autom; Sep. 2003; Taiwan. 14-9.]. In [17Ahsan R, Ibrahimy MI, Khalifa OO. signal classification for human computer interaction: a review Eur J Sci Res 2009; 33(3): 480-501.], R. Ashan et al. made a review on the different types of classifiers used until this day for EMG extraction for Human Computer Interaction applications. They conclude that the use of neural networks dominates for these applications, but point out also the advantages of other methods. In [15Koike Y, Kawato M. Trajectory formation from surface EMG signals using a neural network model Trans Inst Electron Inform Commun Eng E 1994; J77-D-II(1): 193-203.] a three-layer neural network, using the Kick Out method, was applied. They were able to develop a model which relates the motor commands to movement trajectories of the arm. Also, in [13Hu X, Nenov V. Multivariate AR modeling of electromyography for the classification of upper arm movements Clin Neurophysiol 2004; 115(6): 1276-87.], a statistical approach was used in order to model the arm motions. In this case a multivariate analysis of the EMG information approach was used in order to take advantage of the correlation of the EMG activities when making arm movements. The results of these studies show that it is possible to extract arm dynamics information from activities of the proximal muscles. Therefore, this results are encouraging to go further and try to extract information that will allow prediction not only arm, but hand trajectories in reaching and grasping tasks.

T. Brochier et al., in [14Brochier T, Spinks RL, Umilta MA, Lemon RN. Patterns of muscle activity underlying object-specific grasp by the macaque monkey J Neurophysiol 2004; 92: 1770-82.], explore different muscle EMG activities in object-specific grasp in Macaque Monkeys, and J. Martellon, in [12Martellon Carpaneto J, Micera S. Classification of upper arm EMG signals during object-specific grasp In: 30th Annual International IEEE EMBS Conference; 2008; pp. 2061-5064.], reproduced the experiment for humans. They were able to classify 3 different grips using pattern recognition algorithms, but along the proximal muscles, they also used forearm muscle activities, making it difficult to determine the contribution of the proximal muscles to the recognition.

The present study aimed to explore the contribution of the EMG signals of the proximal muscles, more specifically the around-shoulder muscles, to the activities of the arm-hand dynamics. This way we can use this information to predict the type of grip and arm position, while reaching, in a dynamic way. The results showed that it is possible to distinguish the final position and the final grip with a simple classifier around 80% of the reaching time, suggesting that using more complex algorithms this rate could be improved. For example, even when reducing the amount of sensors used to discriminate the position and grips, the results were very promising. Certainly, the classification method used was not optimal, since it leaves aside several important components and it is too slow for actual applications, but it indicates the possibility of using around shoulder muscles to predict hand and arms movements in a dynamical way.

The sensor sites 1, 2, 3, 5 (refer to Fig. 4) were also employed in [12Martellon Carpaneto J, Micera S. Classification of upper arm EMG signals during object-specific grasp In: 30th Annual International IEEE EMBS Conference; 2008; pp. 2061-5064., 13Hu X, Nenov V. Multivariate AR modeling of electromyography for the classification of upper arm movements Clin Neurophysiol 2004; 115(6): 1276-87., 15Koike Y, Kawato M. Trajectory formation from surface EMG signals using a neural network model Trans Inst Electron Inform Commun Eng E 1994; J77-D-II(1): 193-203.]. However, through the comparison, it is clear that the muscles: infraspinatus muscle and infraspinatus fascia (6), teres major muscle (7), and descending fibers of the trapezius muscle (8); also play important roles in discriminating arm- hand motions.

These findings are of importance to achieve a dynamical coupling between the person and the machine. In our research group, we are currently trying to achieve this coupling by using a close loop control of the artificial limb. Therefore, not only an accurate real time intention detection of the hand and arm trajectory is needed, but a different redundant feedback channels are needed for the user to be able to notice whether the prosthetic hand is doing what he intends to achieve [18Gonzalez J, Wenwei Y. Multichannel audio aided dynamical perception for prosthetic hand biofeedback Rehabilitation Robot ICORR’09 2009 June; 240-5.], thus the user can have a dynamical correlation of the intention-action coupling of the reaching process with the prosthesis. This way we hope to achieve body-prosthesis coordination for more natural manipulation of the artificial limb when reaching and grasping in daily live activities, and to reduce the amount of consciousness awareness in the process.

Currently, we are in the processes of classifying EMG signals from the around-shoulder muscles using a back propagation neural network, with impressive results.

This study shows that it is possible to distinguish different arm direction motions and hand grips, using EMG signals from the around-shoulder muscles activities when reaching and grasping objects. Moreover, not only pure shoulder muscles contribute to the discrimination, but other proximal muscles contribute as well because the motion coordination of whole body.

As future approach, other classification algorithms will be tested (such as neural networks) in order to achieve real time discrimination of the arm-hand motions. As well, different types of sensors (such as accelerometers) will be used to improve the detection and feature extraction of the muscle activities.