6,584 research outputs found
A first approach to a taxonomy-based classification framework for hand grasps
Many solutions have been proposed to help amputated subjects regain the lost functionality. In order to interact with the outer world and objects that populate it, it is crucial for these subjects to being able to perform essential grasps. In this paper we propose a preliminary solution for the online classification of 8 basics hand grasps by considering physiological signals, namely Surface Electromyography (sEMG), exploiting a quantitative taxonomy of the considered movement. The hierarchical organization of the taxonomy allows a decomposition of the classification phase between couples of movement groups. The idea is that the closest to the roots the more hard is the classification, but on the meantime the miss-classification error is less problematic, since the two movements will be close to each other. The proposed solution is subject-independent, which means that signals from many different subjects are considered by the probabilistic framework to modelize the input signals. The information has been modeled offline by using a Gaussian Mixture Model (GMM), and then testen online on a unseen subject, by using a Gaussian-based classification. In order to be able to process the signal online, an accurate preprocessing phase is needed, in particular, we apply the Wavelet Transform (Wavelet Transform) to the Electromyography (EMG) signal. Thanks to this approach we are able to develop a robust and general solution, which can adapt quickly to new subjects, with no need of long and draining training phase. In this preliminary study we were able to reach a mean accuracy of 76.5%, reaching
up to 97.29% in the higher levels
Fast human motion prediction for human-robot collaboration with wearable interfaces
In this paper, we aim at improving human motion prediction during human-robot
collaboration in industrial facilities by exploiting contributions from both
physical and physiological signals. Improved human-machine collaboration could
prove useful in several areas, while it is crucial for interacting robots to
understand human movement as soon as possible to avoid accidents and injuries.
In this perspective, we propose a novel human-robot interface capable to
anticipate the user intention while performing reaching movements on a working
bench in order to plan the action of a collaborative robot. The proposed
interface can find many applications in the Industry 4.0 framework, where
autonomous and collaborative robots will be an essential part of innovative
facilities. A motion intention prediction and a motion direction prediction
levels have been developed to improve detection speed and accuracy. A Gaussian
Mixture Model (GMM) has been trained with IMU and EMG data following an
evidence accumulation approach to predict reaching direction. Novel dynamic
stopping criteria have been proposed to flexibly adjust the trade-off between
early anticipation and accuracy according to the application. The output of the
two predictors has been used as external inputs to a Finite State Machine (FSM)
to control the behaviour of a physical robot according to user's action or
inaction. Results show that our system outperforms previous methods, achieving
a real-time classification accuracy of after
from movement onset
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
Prediction of isometric motor tasks and effort levels based on high-density EMG in patients with incomplete spinal cord injury
Objective. The development of modern assistive and rehabilitation devices requires reliable and easy-to-use methods to extract neural information for control of devices. Group-specific pattern recognition identifiers are influenced by inter-subject variability. Based on high-density EMG (HD-EMG) maps, our research group has already shown that inter-subject muscle activation patterns exist in a population of healthy subjects. The aim of this paper is to analyze muscle activation patterns associated with four tasks (flexion/extension of the elbow, and supination/pronation of the forearm) at three different effort levels in a group of patients with incomplete Spinal Cord Injury (iSCI). Approach. Muscle activation patterns were evaluated by the automatic identification of these four isometric tasks along with the identification of levels of voluntary contractions. Two types of classifiers were considered in the identification: linear discriminant analysis and support vector machine. Main results. Results show that performance of classification increases when combining features extracted from intensity and spatial information of HD-EMG maps (accuracy = 97.5%). Moreover, when compared to a population with injuries at different levels, a lower variability between activation maps was obtained within a group of patients with similar injury suggesting stronger task-specific and effort-level-specific co-activation patterns, which enable better prediction results. Significance. Despite the challenge of identifying both the four tasks and the three effort levels in patients with iSCI, promising results were obtained which support the use of HD-EMG features for providing useful information regarding motion and force intentionPeer ReviewedPostprint (author's final draft
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