8,335 research outputs found
Myoelectric signal recognition using artificial neural networks in real time
Application of EMG-controlled functional neuromuscular stimulation to a denervated muscle depends largely on the successful discrimination of the EMG signal by which the subject desires to execute control over the impeded movement. This can be achieved by an adaptive and flexible interface regardless of electrodes location, strength of remaining muscle activity or even personal conditions. Adaptability is a natural and important characteristic of artificial neural networks. This research work is restricted to the development of a real-time application of artificial neural network to the EMG signature recognition. Through this new approach, EMG features extracted by Fourier analysis are presented to a multilayer perceptron type neural network. The neural network learns the most relevant features of the control signal. For real-time operation, a digital signal processor operates over the resulting set of weights from the learning process, and maps the incoming signal to the stimulus control domain. Results showed a highly accurate discrimination of the EMG signal over interference patterns
Myoelectric forearm prostheses: State of the art from a user-centered perspective
User acceptance of myoelectric forearm prostheses is currently low. Awkward control, lack of feedback, and difficult training are cited as primary reasons. Recently, researchers have focused on exploiting the new possibilities offered by advancements in prosthetic technology. Alternatively, researchers could focus on prosthesis acceptance by developing functional requirements based on activities users are likely to perform. In this article, we describe the process of determining such requirements and then the application of these requirements to evaluating the state of the art in myoelectric forearm prosthesis research. As part of a needs assessment, a workshop was organized involving clinicians (representing end users), academics, and engineers. The resulting needs included an increased number of functions, lower reaction and execution times, and intuitiveness of both control and feedback systems. Reviewing the state of the art of research in the main prosthetic subsystems (electromyographic [EMG] sensing, control, and feedback) showed that modern research prototypes only partly fulfill the requirements. We found that focus should be on validating EMG-sensing results with patients, improving simultaneous control of wrist movements and grasps, deriving optimal parameters for force and position feedback, and taking into account the psychophysical aspects of feedback, such as intensity perception and spatial acuity
Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks
Transient muscle movements influence the temporal structure of myoelectric
signal patterns, often leading to unstable prediction behavior from
movement-pattern classification methods. We show that temporal convolutional
network sequential models leverage the myoelectric signal's history to discover
contextual temporal features that aid in correctly predicting movement
intentions, especially during interclass transitions. We demonstrate
myoelectric classification using temporal convolutional networks to effect 3
simultaneous hand and wrist degrees-of-freedom in an experiment involving nine
human-subjects. Temporal convolutional networks yield significant
performance improvements over other state-of-the-art methods in terms of both
classification accuracy and stability.Comment: 4 pages, 5 figures, accepted for Neural Engineering (NER) 2019
Conferenc
Feature Analysis for Classification of Physical Actions using surface EMG Data
Based on recent health statistics, there are several thousands of people with
limb disability and gait disorders that require a medical assistance. A robot
assisted rehabilitation therapy can help them recover and return to a normal
life. In this scenario, a successful methodology is to use the EMG signal based
information to control the support robotics. For this mechanism to function
properly, the EMG signal from the muscles has to be sensed and then the
biological motor intention has to be decoded and finally the resulting
information has to be communicated to the controller of the robot. An accurate
detection of the motor intention requires a pattern recognition based
categorical identification. Hence in this paper, we propose an improved
classification framework by identification of the relevant features that drive
the pattern recognition algorithm. Major contributions include a set of
modified spectral moment based features and another relevant inter-channel
correlation feature that contribute to an improved classification performance.
Next, we conducted a sensitivity analysis of the classification algorithm to
different EMG channels. Finally, the classifier performance is compared to that
of the other state-of the art algorithm
Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring
Sleep studies are important for diagnosing sleep disorders such as insomnia,
narcolepsy or sleep apnea. They rely on manual scoring of sleep stages from raw
polisomnography signals, which is a tedious visual task requiring the workload
of highly trained professionals. Consequently, research efforts to purse for an
automatic stage scoring based on machine learning techniques have been carried
out over the last years. In this work, we resort to multitaper spectral
analysis to create visually interpretable images of sleep patterns from EEG
signals as inputs to a deep convolutional network trained to solve visual
recognition tasks. As a working example of transfer learning, a system able to
accurately classify sleep stages in new unseen patients is presented.
Evaluations in a widely-used publicly available dataset favourably compare to
state-of-the-art results, while providing a framework for visual interpretation
of outcomes.Comment: 8 pages, 1 figure, 2 tables, IEEE 2017 International Workshop on
Machine Learning for Signal Processin
PULP-HD: Accelerating Brain-Inspired High-Dimensional Computing on a Parallel Ultra-Low Power Platform
Computing with high-dimensional (HD) vectors, also referred to as
, is a brain-inspired alternative to computing with
scalars. Key properties of HD computing include a well-defined set of
arithmetic operations on hypervectors, generality, scalability, robustness,
fast learning, and ubiquitous parallel operations. HD computing is about
manipulating and comparing large patterns-binary hypervectors with 10,000
dimensions-making its efficient realization on minimalistic ultra-low-power
platforms challenging. This paper describes HD computing's acceleration and its
optimization of memory accesses and operations on a silicon prototype of the
PULPv3 4-core platform (1.5mm, 2mW), surpassing the state-of-the-art
classification accuracy (on average 92.4%) with simultaneous 3.7
end-to-end speed-up and 2 energy saving compared to its single-core
execution. We further explore the scalability of our accelerator by increasing
the number of inputs and classification window on a new generation of the PULP
architecture featuring bit-manipulation instruction extensions and larger
number of 8 cores. These together enable a near ideal speed-up of 18.4
compared to the single-core PULPv3
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