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

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

Detection of intention level in response to task difficulty from EEG signals

We present an approach that enables detecting intention levels of subjects in response to task difficulty utilizing an electroencephalogram (EEG) based brain-computer interface (BCI). In particular, we use linear discriminant analysis (LDA) to classify event-related synchronization (ERS) and desynchronization (ERD) patterns associated with right elbow flexion and extension movements, while lifting different weights. We observe that it is possible to classify tasks of varying difficulty based on EEG signals. Additionally, we also present a correlation analysis between intention levels detected from EEG and surface electromyogram (sEMG) signals. Our experimental results suggest that it is possible to extract the intention level information from EEG signals in response to task difficulty and indicate some level of correlation between EEG and EMG. With a view towards detecting patients' intention levels during rehabilitation therapies, the proposed approach has the potential to ensure active involvement of patients throughout exercise routines and increase the efficacy of robot assisted therapies

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

Review of real brain-controlled wheelchairs

This paper presents a review of the state of the art regarding wheelchairs driven by a brain-computer interface (BCI). Using a brain-controlled wheelchair (BCW), disabled users could handle a wheelchair through their brain activity, granting autonomy to move through an experimental environment. A classification is established, based on the characteristics of the BCW, such as the type of electroencephalographic (EEG) signal used, the navigation system employed by the wheelchair, the task for the participants, or the metrics used to evaluate the performance. Furthermore, these factors are compared according to the type of signal used, in order to clarify the differences among them. Finally, the trend of current research in this field is discussed, as well as the challenges that should be solved in the future

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