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

Our hands are considered one of the most complex to control actuated systems, thus, emulating the manipulative skills of real hands is still an open challenge even in anthropomorphic robotic hand. While the action of the 4 long fingers and simple grasp motions through opposable thumbs have been successfully implemented in robotic designs, complex in-hand manipulation of objects was difficult to achieve. We take an approach grounded in data-driven extraction of control primitives from natural human behaviour to develop novel ways to understand the dexterity of hands. We collected hand kinematics datasets from natural, unconstrained human behaviour of daily life in 8 healthy in a studio flat environment. We then applied our Sparse Motion Decomposition approach to extract spatio-temporally localised modes of hand motion that are both time-scale and amplitude-scale invariant. These Sparse EigenMotions (SEMs)[1] form a sparse symbolic code that encodes continuous hand motions. We mechanically implemented the common SEMs on our novel dexterous robotic hand [2] in open-loop control. We report that without processing any feedback during grasp control, several of the SEMs resulted in stable grasps of different daily life objects. The finding that SEMs extracted from daily life implement stable grasps in open-loop control of dexterous hands, lends further support for our hypothesis the brain controls the hand using sparse control strategies

Hand use predicts the structure of representations in sensorimotor cortex.

Fine finger movements are controlled by the population activity of neurons in the hand area of primary motor cortex. Experiments using microstimulation and single-neuron electrophysiology suggest that this area represents coordinated multi-joint, rather than single-finger movements. However, the principle by which these representations are organized remains unclear. We analyzed activity patterns during individuated finger movements using functional magnetic resonance imaging (fMRI). Although the spatial layout of finger-specific activity patterns was variable across participants, the relative similarity between any pair of activity patterns was well preserved. This invariant organization was better explained by the correlation structure of everyday hand movements than by correlated muscle activity. This also generalized to an experiment using complex multi-finger movements. Finally, the organizational structure correlated with patterns of involuntary co-contracted finger movements for high-force presses. Together, our results suggest that hand use shapes the relative arrangement of finger-specific activity patterns in sensory-motor cortex

Wearable full-body motion tracking of activities of daily living predicts disease trajectory in Duchenne muscular dystrophy

Artificial intelligence has the potential to revolutionize healthcare, yet clinical trials in neurological diseases continue to rely on subjective, semiquantitative and motivation-dependent endpoints for drug development. To overcome this limitation, we collected a digital readout of whole-body movement behavior of patients with Duchenne muscular dystrophy (DMD) (n = 21) and age-matched controls (n = 17). Movement behavior was assessed while the participant engaged in everyday activities using a 17-sensor bodysuit during three clinical visits over the course of 12 months. We first defined new movement behavioral fingerprints capable of distinguishing DMD from controls. Then, we used machine learning algorithms that combined the behavioral fingerprints to make cross-sectional and longitudinal disease course predictions, which outperformed predictions derived from currently used clinical assessments. Finally, using Bayesian optimization, we constructed a behavioral biomarker, termed the KineDMD ethomic biomarker, which is derived from daily-life behavioral data and whose value progresses with age in an S-shaped sigmoid curve form. The biomarker developed in this study, derived from digital readouts of daily-life movement behavior, can predict disease progression in patients with muscular dystrophy and can potentially track the response to therapy