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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 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 replicate human hand movements in a naturalistic...
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...
In this preliminary study, we investigate the potential use of smartphones as portable heart-monitoring devices that can capture and analyse heart activity in real time. We have developed a smartphone application called “Medical Tricorder” that can exploit smartphone;s inertial sensors and when placed on a subject;s chest, it can efficiently capture the motion patterns caused by the mechanical activity...
EEG-based Brain Computer Interfaces (BCIs) are quite noisy brain signals recorded from the scalp (electroencephalography, EEG) to translate the user's intent into action. This is usually achieved by looking at the pattern of brain activity across many trials while the subject is imagining the performance of an instructed action - the process known as motor imagery. Nevertheless, existing motor imagery...
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...
Modern Brain Computer Interfaces (BCIs) use EEG signals recorded from the scalp to transduce a users intent into action. However, achieving an optimal control requires a physically and mentally demanding series of long-lasting training sessions based on the use of common neurofeedback. In this study we propose a framework that bypasses the training phase (unsupervised personalisation), where the BCI...
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...
We investigate the effectiveness of a dual-channel MMG signal recorded from the biceps and triceps brachii as a way to predict the isometric forces produced by flexion and extension of the elbow. We asked 8 subjects to apply a range of isometric force levels for both flexion and extension of the elbow while the activity of the two muscles was captured using custom-built MMG sensors. By extracting...
Day-long continuous monitoring requires stable sensors that can minimise the effects of drift and maintain high accuracy and precision over time. We have recently shown that our inertial motion tracking system can capture stable kinematic data, calibrated against ground-truth over a long period of time. However, for many clinical and daily life activities, it is also essential to monitor the muscle-activity...
We present an ultra-portable and low-cost body sensor network (BSN), which enables wireless recording of human motor movement kinematics and neurological signals in unconstrained, daily-life environments. This is crucial as activities of daily living (ADL) and thus metrics of everyday movement enable us to diagnose motor and neurological disorders in the patients context, and not artificial laboratory...
We present a wearable head-tracking device using inexpensive inertial sensors as an alternative head movement tracking system. This can be used as indicator of human movement intentions for Brain-Machine Interface (BMI) applications. Our system is capable of tracking head movements at high rates (100 Hz) and achieves R2 = 0.99 with a 2.5° RMSE against a ground-truth motion tracking system. The system...
Muscle activity is the basis of many brain-machine interface (BMI) applications, but the mainstream EMG-based technology to decode muscle activity has significant constraints when long-term BMI usage “in-the-wild” is required (e.g. controlling neuroprosthetics throughout the day). We use the surface mechanomyogram (MMG), the mechano-acoustic signal generated by lateral oscillations of the muscle fibres...
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