Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Andreas A. C. Thomik"'
Publikováno v:
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. 25(10)
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 mu
EthoHand: A dexterous robotic hand with ball-joint thumb enables complex in-hand object manipulation
Publikováno v:
BioRob
6th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)
6th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)
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 t
Autor:
Anastasia Sylaidi, Constantinos Gavriel, Stavros Athanasopoulos, A. Aldo Faisal, Pedro Rente Lourencco, Sathiji Nageshwaran, Richard Festenstein, Andreas A. C. Thomik
Publikováno v:
BSN
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
Autor:
Constantinos Gavriel, A. Aldo Faisal, Andreas A. C. Thomik, Pedro Rente Lourenco, Sathiji Nageshwaran, Richard Festenstein, Anastasia Sylaidi, Stavros Athanasopoulos
Publikováno v:
NER
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 tas
Publikováno v:
NER
IEEE/EMBS Neural Engineering (NER)
IEEE/EMBS Neural Engineering (NER)
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 rele
Publikováno v:
NER
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 syne
Publikováno v:
NER
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 betw
Publikováno v:
NER
7th International IEEE EMBS Conference on Neural Engineering
7th International IEEE EMBS Conference on Neural Engineering
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
Publikováno v:
BSN
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
Publikováno v:
2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).
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 infe