Principal components analysis based control of a multi-dof underactuated prosthetic hand

Journal of NeuroEngineering and Rehabilitation Principal components analysis based control of a multi-dof underactuated prosthetic hand Giulia C Matrone 0 Christian Cipriani 2 Emanuele L Secco 1 Giovanni Magenes 0 Maria Chiara Carrozza 2 0 Department of Computer Engineering and Systems Science, University of Pavia , Via Ferrata 1, 27100 Pavia , Italy 1 EUCENTRE Foundation , Via Ferrata 1, 27100 Pavia , Italy 2 ARTS Lab, Scuola Superiore Sant'Anna , V.le Piaggio 34, 56025 Pontedera (PI) , Italy Background: Functionality, controllability and cosmetics are the key issues to be addressed in order to accomplish a successful functional substitution of the human hand by means of a prosthesis. Not only the prosthesis should duplicate the human hand in shape, functionality, sensorization, perception and sense of body-belonging, but it should also be controlled as the natural one, in the most intuitive and undemanding way. At present, prosthetic hands are controlled by means of non-invasive interfaces based on electromyography (EMG). Driving a multi degrees of freedom (DoF) hand for achieving hand dexterity implies to selectively modulate many different EMG signals in order to make each joint move independently, and this could require significant cognitive effort to the user. Methods: A Principal Components Analysis (PCA) based algorithm is used to drive a 16 DoFs underactuated prosthetic hand prototype (called CyberHand) with a two dimensional control input, in order to perform the three prehensile forms mostly used in Activities of Daily Living (ADLs). Such Principal Components set has been derived directly from the artificial hand by collecting its sensory data while performing 50 different grasps, and subsequently used for control. Results: Trials have shown that two independent input signals can be successfully used to control the posture of a real robotic hand and that correct grasps (in terms of involved fingers, stability and posture) may be achieved. Conclusions: This work demonstrates the effectiveness of a bio-inspired system successfully conjugating the advantages of an underactuated, anthropomorphic hand with a PCA-based control strategy, and opens up promising possibilities for the development of an intuitively controllable hand prosthesis. - Background In the last thirty years several examples of robotic hands have been developed by research or industry, some designed to mimic the human hand in its manipulation dexterity and functionality, some aimed at achieving better anthropomorphism and cosmetic appearance [1]. Great research effort has been focused on the design of both articulated articulated end-effectors and smart dexterous anthropomorphic hands, for humanoid robotics and prosthetics. An exhaustive summary of the various approaches and solutions is given in [2] and [1]. An advanced neuro-controlled prosthetic hand bi-directionally interfaced with a human being should address both functional and cosmetic issues; it should be dexterous enough to allow the execution of Activities of Daily Living (ADLs), and include proprioceptive and exteroceptive sensors for the delivery of consciously perceived sensory feedback [3]. Market available myoelectric hand prostheses [4-6] are instead similar to rough pincers [7], having just one (open/close the hand) or two (prono/supinate the wrist) degrees of freedom (DoFs), therefore poor manipulation capabilities. They are controlled by means of electromyographic (EMG) signals picked up from the residual muscles by surface electrodes, amplified and processed to functionally operate the hand [8-10]. Also the recently commercialized multi-fingered I-Limb prosthesis (Touch EMAS Ltd., Edinburgh, UK) [11] is controlled using a traditional two-input EMG scheme where all fingers open/close simultaneously. The communication interface between the user and the machine is the technological bottle-neck [12] which explains why current hand prostheses are very simple from a biomechanical point of view, even if more sophisticated solutions would be possible. Still nowadays there is no way to easily interface the amputee with the multi-DoF dexterous prostheses developed in the past decades (e.g. the Southampton-REMEDI [13], the RTR II [14], the MANUS [15], the Karlsruhe hands [16], the SmartHand [17], the IOWA hand [18]), since it requires either too many independent control signals or a controller able to compensate for the limited bandwidth of the source signal. As a matter of fact, increasing the number of DoFs (i. e. dexterity) means either that the system should take care of carrying out the grasp with some level of automatism, as in the SAMS [10,13,19], or that the user should learn how to correctly and selectively modulate different muscular contractions so as to move each prosthesis joint independently (as in [20,21]). In all cases, a certain level of shared-control between the users intention and the automatic controller is required, as formally introduced by [22]. If the control relies on the automatic controller of the prosthesis, this must include a high number of sensors and intelligent control algorithms to achieve the grasp; on the other hand, if the control system is based on users intentions decoded from bio-signals extracted by an appropriate interface, (possibly) complex EMG processing algorithms and a high level of training for the user may be required, which could cause fatiguing burden [23]. This could potentially induce the subject to reject the prosthesis, particularly when the amputation is mono-lateral and he/she can supply with the healthy limb to his/her motor deficiency. An innovative shared-control strategy could be achieved by observing and mimicking the natural biomechanical behaviour. As several studies in the neurophysiology literature report, low-dimensional modules formed by muscles activated in synchrony - also called muscular synergies - are used by the human nervous system to build complex motor output patterns during motor tasks [24,25]. In 1997/8 Santello and Soechting reported a series of interesting experimental results on the analysis of human hand grasping postures [26,27], demonstrating that such synergies exist also in hand postural data, which can thus be described in a reduced dimensionality space [26-30]. This concept has been exploited with the aim of controlling robotic grippers and dexterous hands by means of a lower-dimension input space, in a limited number of works. Brown and Asada explored the concept of biomechanical synergies and how they can be applied to a 17 DoFs robot anthropomorphic hand, by mechanically implementing Principal Components Analysis (PCA) and using common patterns of actuation called eigenpostures [31]. Ciocarlie et al. [32] used PCA to design an automatic grasp planning system for integration into the control system of a prosthetic arm and hand driven by cortical activity. Ciocarlie, Goldfeder and Allen [33,34] applied the eigengrasp concept to 5 dexte (...truncated)