5,388 research outputs found
Pattern classification of hand movements using time domain features of electromyography
Myoelectric control of prostheses is a long-established technique, using surface electromyography (sEMG) to detect the electrical signals of muscle activity and perform subsequent mechanical actions. Despite several decades’ research, robust, responsive and intuitive control schemes remain elusive. Current commercial hardware advances
offer a variety of movements but the control systems are unnatural, using sequential switching methods triggered by specific
sEMG signals. However, recent research with pattern recognition and simultaneous and proportional control shows good promise for
natural myoelectric control. This paper investigates several sEMG time domain features using a series of hand movements performed by 11 subjects, taken from a benchmark database, to determine if optimal classification accuracy is dependent on feature set size. The features were extracted from the data using a sliding window process and applied to five machine learning classifiers, of which Random Forest consistently performed best. Results suggest a few simple features such as Root Mean Square and Waveform Length achieve comparable performance to using the entire feature set, when identifying the hand movements, although further work is
required for feature optimisation
A quantitative taxonomy of human hand grasps
Background: A proper modeling of human grasping and of hand movements is fundamental for robotics,
prosthetics, physiology and rehabilitation. The taxonomies of hand grasps that have been proposed in scientific
literature so far are based on qualitative analyses of the movements and thus they are usually not quantitatively
justified.
Methods: This paper presents to the best of our knowledge the first quantitative taxonomy of hand grasps based on
biomedical data measurements. The taxonomy is based on electromyography and kinematic data recorded from 40
healthy subjects performing 20 unique hand grasps. For each subject, a set of hierarchical trees are computed for
several signal features. Afterwards, the trees are combined, first into modality-specific (i.e. muscular and kinematic)
taxonomies of hand grasps and then into a general quantitative taxonomy of hand movements. The modality-specific
taxonomies provide similar results despite describing different parameters of hand movements, one being muscular
and the other kinematic.
Results: The general taxonomy merges the kinematic and muscular description into a comprehensive hierarchical
structure. The obtained results clarify what has been proposed in the literature so far and they partially confirm the
qualitative parameters used to create previous taxonomies of hand grasps. According to the results, hand movements
can be divided into five movement categories defined based on the overall grasp shape, finger positioning and
muscular activation. Part of the results appears qualitatively in accordance with previous results describing kinematic
hand grasping synergies.
Conclusions: The taxonomy of hand grasps proposed in this paper clarifies with quantitative measurements what
has been proposed in the field on a qualitative basis, thus having a potential impact on several scientific fields
Near Real-Time Data Labeling Using a Depth Sensor for EMG Based Prosthetic Arms
Recognizing sEMG (Surface Electromyography) signals belonging to a particular
action (e.g., lateral arm raise) automatically is a challenging task as EMG
signals themselves have a lot of variation even for the same action due to
several factors. To overcome this issue, there should be a proper separation
which indicates similar patterns repetitively for a particular action in raw
signals. A repetitive pattern is not always matched because the same action can
be carried out with different time duration. Thus, a depth sensor (Kinect) was
used for pattern identification where three joint angles were recording
continuously which is clearly separable for a particular action while recording
sEMG signals. To Segment out a repetitive pattern in angle data, MDTW (Moving
Dynamic Time Warping) approach is introduced. This technique is allowed to
retrieve suspected motion of interest from raw signals. MDTW based on DTW
algorithm, but it will be moving through the whole dataset in a pre-defined
manner which is capable of picking up almost all the suspected segments inside
a given dataset an optimal way. Elevated bicep curl and lateral arm raise
movements are taken as motions of interest to show how the proposed technique
can be employed to achieve auto identification and labelling. The full
implementation is available at https://github.com/GPrathap/OpenBCIPytho
Enhancing Upper Limb Prosthetic Control in Amputees Using Non-invasive EEG and EMG Signals with Machine Learning Techniques
Amputation of the upper limb significantly hinders the ability of patients to perform activities of daily living. To address this challenge, this paper introduces a novel approach that combines non-invasive methods, specifically Electroencephalography (EEG) and Electromyography (EMG) signals, with advanced machine learning techniques to recognize upper limb movements. The objective is to improve the control and functionality of prosthetic upper limbs through effective pattern recognition. The proposed methodology involves the fusion of EMG and EEG signals, which are processed using time-frequency domain feature extraction techniques. This enables the classification of seven distinct hand and wrist movements. The experiments conducted in this study utilized the Binary Grey Wolf Optimization (BGWO) algorithm to select optimal features for the proposed classification model. The results demonstrate promising outcomes, with an average classification accuracy of 93.6% for three amputees and five individuals with intact limbs. The accuracy achieved in classifying the seven types of hand and wrist movements further validates the effectiveness of the proposed approach. By offering a non-invasive and reliable means of recognizing upper limb movements, this research represents a significant step forward in biotechnical engineering for upper limb amputees. The findings hold considerable potential for enhancing the control and usability of prosthetic devices, ultimately contributing to the overall quality of life for individuals with upper limb amputations
Adaptive learning to speed-up control of prosthetic hands: A few things everybody should know
Domain adaptation methods have been proposed to reduce the training efforts needed to control an upper-limb prosthesis by adapting well performing models from previous subjects to the new subject. These studies generally reported impressive reductions in the required number of training samples to achieve a certain level of accuracy for intact subjects. We further investigate two popular methods in this field to verify whether this result also applies to amputees. Our findings show instead that this improvement can largely be attributed to a suboptimal hyperparameter configuration. When hyperparameters are appropriately tuned, the standard approach that does not exploit prior information performs on par with the more complicated transfer learning algorithms. Additionally, earlier studies erroneously assumed that the number of training samples relates proportionally to the efforts required from the subject. However, a repetition of a movement is the atomic unit for subjects and the total number of repetitions should therefore be used as reliable measure for training efforts. Also when correcting for this mistake, we do not find any performance increase due to the use of prior models
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
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