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Matching the dexterity, versatility, and robustness of the human hand is still an unachieved goal in bionics, robotics, and neural engineering. A major limitation for hand prosthetics lies in the challenges of reliably decoding user intention from muscle signals when controlling complex robotic hands. Most of the commercially available prosthetic hands use muscle-related signals to decode a finite...
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 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 behavioural biomarkers that objectively capture...
We propose a Gaussian Process-based regression framework for continuous prediction of the state of missing limbs by exclusively decoding missing limb movements from intact limbs - we achieve this as we have measured the correlation structure and synergies of natural limb kinematics in daily life. Using the example of hand neuroprosthetic, we demonstrate how our model can use non-linear regression...
This study focuses on the objective quantification of the disease progression in patients with Friedreich's Ataxia (FRDA) through the use of kinematic body sensor network technology. Currently, this quantification is performed through a series of task-oriented score-based metrics, which, although they provide an efficient way of quantifying the ataxic disease, they are dependent on the assessor's...
We propose a new framework for extracting information from extrinsic muscles in the forearm that will allow a continuous, natural and intuitive control of a neuroprosthetic devices and robotic hands. This is achieved through a continuous mapping between muscle activity and joint angles rather than prior discretisation of hand gestures. We instructed 6 able-bodied subjects, to perform everyday object...
The correlation structure of natural hand & finger movements suggests that their motion is controlled in a lower-dimensional space than would be possible given their mechanical nature. Yet, it is unclear whether this low dimensional embedding is relevant to how the brain represents motor actions and how we can decode it for Brain-Machine Interface applications. We collected large data set of natural...
Even without visual feedback, humans can accurately determine the shape of objects on the basis of haptic feedback. This feat is achievable despite large variability in sensory and motor uncertainty in estimation of hand pose and object location. In contrast, most neuroprosthetic hands still operate unaware of the shape of the object they are manipulating and can thus only provide limited intelligence...
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, we present how the simple combination of...
Replacing lost hands with prosthetic devices that offer the same functionality as natural limbs is an open challenge, as current technology is often limited to basic grasps by the low information readout. In this work, we develop a probabilistic inference-based method that allows for improved control of neuroprosthetic devices. We observe the behaviour of the undamaged limb to predict the most likely...
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