Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands

Abstract

The term ‘synergy’ – from the Greek synergia – means ‘working together’. The concept of multiple elements working together towards a common goal has been extensively used in neuroscience to develop theoretical frameworks, experimental approaches, and analytical techniques to understand neural control of movement, and for applications for neuro-rehabilitation. In the past decade, roboticists have successfully applied the framework of synergies to create novel design and control concepts for artificial hands, i.e., robotic hands and prostheses. At the same time, robotic research on the sensorimotor integration underlying the control and sensing of artificial hands has inspired new research approaches in neuroscience, and has provided useful instruments for novel experiments. The ambitious goal of integrating expertise and research approaches in robotics and neuroscience to study the properties and applications of the concept of synergies is generating a number of multidisciplinary cooperative projects, among which the recently finished 4-year European project “The Hand Embodied” (THE). This paper reviews the main insights provided by this framework. Specifically, we provide an overview of neuroscientific bases of hand synergies and introduce how robotics has leveraged the insights from neuroscience for innovative design in hardware and controllers for biomedical engineering applications, including myoelectric hand prostheses, devices for haptics research, and wearable sensing of human hand kinematics. The review also emphasizes how this multidisciplinary collaboration has generated new ways to conceptualize a synergy-based approach for robotics, and provides guidelines and principles for analyzing human behavior and synthesizing artificial robotic systems based on a theory of synergies.

Unvealing the Principal Modes of Human Upper Limb Movements through Functional Analysis

“…Not surprisingly, a lot of studies have been devoted to model how the nervous system can cope with the complexity of hand sensory-motor architecture (Mason et al, 2001;Todorov and Ghahramani, 2004;Zatsiorsky and Latash, 2004;Thakur et al, 2008;Gabiccini et al, 2013;Santello, 2014). These studies have led to the definition of the so-called synergies, broadly intended as covariation patterns that can be represented at different levels (Santello et al, 2016). More specifically, at the level of motor units, neural activation shows a synergistic control in the time and/or frequency domain (Santello, 2014).…”

“…Other papers studied muscular synergies in upper limb activities, as in d ' Avella and Tresch (2002), where the authors introduced a model based on combinations of muscle time-varying synergies, and in d ' Avella et al (2006), where authors recorded electromyographic activity from shoulder and arm muscles during point-to-point movements. As for hand synergies, whose robotic applications are reviewed in Santello et al (2016), synergies have also been applied to movement generation for virtual arms (Fu et al, 2013) as well as myocontrol of a multi-DoF planar robotic arm using muscle synergies (Lunardini et al, 2015). However, none of the previous studies considered the dynamic aspects of human upper limb motion, i.e., that different temporal evolutions and shapes of upper limb joints trajectories would result in different final hand poses.…”

Combining Electromyography and Tactile Myography to Improve Hand and Wrist Activity Detection in Prostheses

“…Surface electromyography (sEMG) sensors allow for capturing the electrical activity of the muscles of the forearm, which is used to estimate the user intent using machine learning methods [1]. Most research efforts in this area have focused on myoelectric prostheses, by exploring the control of one, two or multiple degrees of freedom (DOFs) [2][3][4] or by exploiting hand synergies [5,6]. Even by means of this technology, simultaneous and proportional control of multiple DOFs remains a major challenge [7]; the problem is that even when state-of-the-art machine learning algorithms are used to interpret sEMG data, robust (reliable) hand activity detection is not yet possible in daily living activities with a small number of sEMG sensors.…”

Sparse Eigenmotions derived from daily life kinematics implemented on a dextrous robotic hand

“…of Computing, Imperial College London, South Kensington Campus, SW7 2AZ, London, UK, 3 MRC Clinical Sciences Centre. Address for correspondence: aldo.faisal@imperial.ac.uk taken towards exploiting a lower dimensional manifold to control artificial hands [8], [9].…”

“…Much of the focus of hand control is on grasping [4], [5], [9], yet hand control involves also more complex behaviours such as inhand object manipulation (e.g. using chop sticks), compound grasps (e.g.…”