Hand Motion Classification Using a Multi-Channel Surface Electromyography Sensor

The human hand has multiple degrees of freedom (DOF) for achieving high-dexterity motions. Identifying and replicating human hand motions are necessary to perform precise and delicate operations in many applications, such as haptic applications. Surface electromyography (sEMG) sensors are a low-cost method for identifying hand motions, in addition to the conventional methods that use data gloves and vision detection. The identification of multiple hand motions is challenging because the error rate typically increases significantly with the addition of more hand motions. Thus, the current study proposes two new methods for feature extraction to solve the problem above. The first method is the extraction of the energy ratio features in the time-domain, which are robust and invariant to motion forces and speeds for the same gesture. The second method is the extraction of the concordance correlation features that describe the relationship between every two channels of the multi-channel sEMG sensor system. The concordance correlation features of a multi-channel sEMG sensor system were shown to provide a vast amount of useful information for identification. Furthermore, a new cascaded-structure classifier is also proposed, in which 11 types of hand gestures can be identified accurately using the newly defined features. Experimental results show that the success rate for the identification of the 11 gestures is significantly high

Calibration of UR10 robot controller through simple auto-tuning approach

This paper presents a calibration approach of a manipulator robot controller using an auto-tuning technique. Since the industry requires machines to run with increasing speed and precision, an optimal controller is too demanding. Even though the robots make use of an internal controller, usually, this controller does not fulfill the user specification with respect to their applications. Therefore, in order to overcome the user requirements, an auto-tuning method based on a single sine test is employed to obtain the optimal parameters of the proportional-integral-derivative PID controller. This approach has been tested, validated and implemented on a UR10 robot. The experimental results revealed that the performances of the robot increased when the designed controller, using the auto-tuning technique, was employed

Combining Independent Component and Grey Relational Analysis for the Real-Time System of Hand Motion Identification Using Bend Sensors and Multichannel Surface EMG

This paper proposes a portable system for hand motion identification (HMI) using the features from data glove with bend sensors and multichannel surface electromyography (SEMG). SEMG could provide the information of muscle activities indirectly for HMI. However it is difficult to discriminate the finger motion like extension of thumb and little finger just using SEMG; the data glove with five bend sensors is designed to detect finger motions in the proposed system. Independent component analysis (ICA) and grey relational analysis (GRA) are used to data reduction and the core of identification, respectively. Six features are extracted from each SEMG channel, and three features are computed from five bend sensors in the data glove. To test the feasibility of the system, this study quantitatively compares the classification accuracies of twenty hand motions collected from 10 subjects. Compared to the performance with a back-propagation neural network and only using GRA method, the proposed method provides equivalent accuracy (>85%) with three training sets and faster processing time (20 ms). The results also demonstrate that ICA can effectively reduce the size of input features with GRA methods and, in turn, reduce the processing time with the low price of reduced identification rates

Evaluation of surface EMG-based recognition algorithms for decoding hand movements

Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins\u27 set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands

The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human-Computer Interface

http://doi.org/10.4018/978-1-4666-6090-8 ISBN 13 : 9781466660908 EISBN13: 9781466660915International audienceMuscle-computer interfaces (MCIs) based on surface electromyography (EMG) pattern recognition have been developed based on two consecutive components: feature extraction and classification algorithms. Many features and classifiers are proposed and evaluated, which yield the high classification accuracy and the high number of discriminated motions under a single-session experimental condition. However, there are many limitations to use MCIs in the real-world contexts, such as the robustness over time, noise, or low-level EMG activities. Although the selection of the suitable robust features can solve such problems, EMG pattern recognition has to design and train for a particular individual user to reach high accuracy. Due to different body compositions across users, a feasibility to use anthropometric variables to calibrate EMG recognition system automatically/semi-automatically is proposed. This chapter presents the relationships between robust features extracted from actions associated with surface EMG signals and twelve related anthropometric variables. The strong and significant associations presented in this chapter could benefit a further design of the MCIs based on EMG pattern recognition

Pattern recognition based on HD-sEMG spatial features extraction for an efficient proportional control of a robotic arm.

To enable an efficient alternative control of an assistive robotic arm using electromyographic (EMG) signals, the control method must simultaneously provide both the direction and the velocity. However, the contraction variations of the forearm muscles, used to proportionally control the device’s velocity using a regression method, can disturb the accuracy of the classification used to estimate its direction at the same time. In this paper, the original set of spatial features takes advantage of the 2D structure of an 8 × 8 high-density surface EMG (HD-sEMG) sensor to perform a high accuracy classification while improving the robustness to the contraction variations. Based on the HD-sEMG sensor, different muscular activity images are extracted by applying different spatial filters. In order to characterize their distribution specific to each movement, instead of the EMG signals’ amplitudes, these muscular images are divided in sub-images upon which the proposed spatial features, such as the centers of the gravity coordinates and the percentages of influence, are computed. These features permits to achieve average accuracies of 97% and 96.7% to detect respectively 16 forearm movements performed by a healthy subject with prior experience with the control approach and 10 movements by ten inexperienced healthy subjects. Compared with the time-domain features, the proposed method exhibits significant higher accuracies in presence of muscular contraction variations, requires less training data and is more robust against the time of use. Furthermore, two fine real-time tasks illustrate the potential of the proposed approach to efficiently control a robotic arm

Classification de mouvements fantômes du membre supérieur chez des amputés huméraux à l'aide de mesures électromyographiques et cinématiques

RÉSUMÉ La perte d’un membre supérieur engendre de nombreux déficits fonctionnels pour l’amputé dans sa vie de tous les jours. En effet, la plupart des activités de la vie quotidienne, telles qu’attacher ses souliers ou ouvrir une bouteille, sont complexes et difficiles à réaliser avec un seul bras fonctionnel. Les impacts de ces déficits augmentent à mesure que le niveau d’amputation est plus haut au niveau du bras. Pour toutes ces personnes, les nombreuses avancées dans le domaine des prothèses myoélectriques, c’est-à-dire commandées par l’activité musculaire des muscles restants après l’amputation, sont encourageantes parce qu’elles permettent d’entretenir l’espoir d’une prothèse à la commande intuitive. Un phénomène particulier, présent chez la majorité des amputés, est celui des sensations au membre fantôme. Ces sensations peuvent se manifester sous plusieurs formes : thermiques, douleurs, mobilités. Les mobilités du membre fantôme sont particulièrement intéressantes pour le développement des prothèses myoélectriques étant donné qu’il a été démontré que les mouvements fantômes produisent une activité électromyographique (EMG) au niveau du membre amputé. Cependant, les études s’intéressant à la détection des mouvements fantômes ont enregistré l’activité EMG provenant de muscles difficilement intégrables dans l’emboiture d’une prothèse myoélectriques, tels que ceux du dos, du torse et de l’épaule. La présente étude se concentre sur la classification des mouvements fantômes chez les amputés huméraux à l’aide de l’EMG dans l’optique de développer une prothèse myoélectrique commandée par reconnaissance de formes. Cinq adultes ayant subi une amputation unilatérale humérale suite à un trauma ont participé à cette étude. L’activité EMG des participants a été enregistrée exclusivement autour de leur moignon. Durant les enregistrements, il était demandé aux participants de réaliser l’un des principaux mouvements fantômes du membre supérieur : la flexion ou l’extension du coude, la pronation ou la supination de l’avant-bras, la flexion ou l’extension du poignet, l’ouverture ou la fermeture de la main et le repos. Chaque mouvement fantôme devait être réalisé symétriquement à l’aide du bras sain et la cinématique de ce dernier a été enregistrée à l’aide d’un système d’analyse du mouvement. Dix caractéristiques (ou « features » en anglais) temporels ont été extraites des signaux EMG et utilisées pour entraîner un réseau de neurones permettant de classifier les mouvements fantômes du membre supérieur.----------ABSTRACT Upper limb amputation creates substantial functional deficits for the amputee. Indeed, most activities of daily living, such as tying shoelaces or opening a bottle, are complex and hard to achieve with only one functional arm. These functional impairments increase as the level of amputation is higher up the arm. For these people, recent advances in the field of myoelectric prostheses, i.e. controlled by the activity of the remaining muscles after amputation, are encouraging because they help maintain the hope of an intuitive prosthesis. A particular phenomenon, occurring in the majority of amputees, is the presence of phantom limb sensations. Phantom limb sensations are of many types: thermal, pain, and mobility. Phantom limb mobilities are particularly interesting for the development of myoelectric prostheses since it has been shown that they produce an electromyographic (EMG) activity in the amputated limb. However, the studies focusing on the detection of phantom movements recorded EMG from muscles that are hard to integrate into the socket element of a myoelectric prosthesis, such as the back, chest and shoulder muscles. This study focuses on the classification of phantom movements in transhumeral amputees using EMG in the context of developing a myoelectric prosthesis controlled by pattern recognition. Five adults who underwent unilateral humeral amputation following a trauma participated in this study. The EMG activity of the participants was recorded exclusively around their stump. During the recordings, participants were asked to perform one of the main upper limb phantom movements: flexion or extension of the elbow, pronation or supination of the forearm, flexion or extension of the wrist, opening or closing the hand and rest. Each phantom movement was to be made symmetrical with the unaffected arm and the kinematics of the latter was recorded using a motion analysis system. Ten time-domain features were extracted from the EMG signals and used to train a neural network to classify the phantom limb movements. The performance of the classifier was evaluated based on the number of movements studied and an optimal set of four EMG features was determined. The impact of kinematic information on the classification performance was also evaluated. The accuracy of the classification varies from one amputee to another, but some trends are common: performance decreases if the number of degrees of freedom considered in the classification increases and/or if the phantom movements become more distal. Moreover, the optimal set of four EMG features provided a performance equivalent to that obtained with all ten EMG features. The addition of the kinematic information improved classification accuracy for all amputees