Andreas A. C. Thomik

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The vast amounts of data which can be collected using body-sensor networks with high temporal and spatial resolution require a novel analysis approach. In this context, state-of-the-art Bayesian approaches based on variational, non-parametric or MCMC derived methods often become computationally intractable when faced with several million data points. Here,(More)
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(More)
Our dexterous hand is a fundmanetal human feature that distinguishes us from other animals by enabling us to go beyond grasping to support sophisticated in-hand object manipulation. Our aim was the design of a dexterous anthropomorphic robotic hand that matches the human hand's 24 degrees of freedom, under-actuated by seven motors. With the ability to(More)
The brain is a dynamical system, mapping sensory inputs to motor actions. This relationship has been widely characterised by reductionist controlled lab experiments. However, with the emergence of mobile eye-tracking, increasing emphasis has been placed on the ecological validity of gaze studies, taking them out of the lab and into the "wild" (Hayhoe &(More)
We have deployed body sensor network (BSN) technology in clinical trials and developed behavioural analytics to quantify and monitor longitudinally the progression of Friedreich's Ataxia (FRDA) outside the lab. Patients and their carers administered themselves our ETHO1 wireless BSN and we captured motion time-series from patient sleep. We extracted(More)
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