Classification Of Hand Movements Research Articles

Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high dimensionality, it is commonplace to apply a processing step that transforms the data to a space of significantly lower dimensions with only a limited loss of useful information. Linear discriminant analysis (LDA) has been successfully applied as an EMG feature projection method. Recently, a number of extended LDA-based algorithms have been proposed, which are more competitive in terms of both classification accuracy and computational costs/times with classical LDA. This paper presents the findings of a comparative study of classical LDA and five extended LDA methods. From a quantitative comparison based on seven multi-feature sets, three extended LDA-based algorithms, consisting of uncorrelated LDA, orthogonal LDA and orthogonal fuzzy neighborhood discriminant analysis, produce better class separability when compared with a baseline system (without feature projection), principle component analysis (PCA), and classical LDA. Based on a 7-dimension time domain and time-scale feature vectors, these methods achieved respectively 95.2% and 93.2% classification accuracy by using a linear discriminant classifier.

Subject-specific myoelectric pattern classification of functional hand movements for stroke survivors.

In this study, we developed a robust subject-specific electromyography (EMG) pattern classification technique to discriminate intended manual tasks from muscle activation patterns of stroke survivors. These classifications will enable volitional control of assistive devices, thereby improving their functionality. Twenty subjects with chronic hemiparesis participated in the study. Subjects were instructed to perform six functional tasks while their muscle activation patterns were recorded by ten surface electrodes placed on the forearm and hand of the impaired limb. In order to identify intended functional tasks, a pattern classifier using linear discriminant analysis was applied to the EMG feature vectors. The classification accuracy was mainly affected by the impairment level of the subject. Mean classification accuracy was 71.3% for moderately impaired subjects (Chedoke Stage of Hand 4 and 5), and 37.9% for severely impaired subjects (Chedoke Stage of Hand 2 and 3). Most misclassification occurred between grip tasks of similar nature, for example, among pinch, key, and three-fingered grips, or between cylindrical and spherical grips. EMG signals from the intrinsic hand muscles significantly contributed to the inter-task variability of the feature vectors, as assessed by the inter-task squared Euclidean distance, thereby indicating the importance of intrinsic hand muscles in functional manual tasks. This study demonstrated the feasibility of the EMG pattern classification technique to discern the intent of stroke survivors. Future work should concentrate on the construction of a subject-specific EMG classification paradigm that carefully considers both functional and physiological impairment characteristics of each subject in the target task selection and electrode placement procedures.

Surface EMG pattern recognition for real-time control of a wrist exoskeleton

BackgroundSurface electromyography (sEMG) signals have been used in numerous studies for the classification of hand gestures and movements and successfully implemented in the position control of different prosthetic hands for amputees. sEMG could also potentially be used for controlling wearable devices which could assist persons with reduced muscle mass, such as those suffering from sarcopenia. While using sEMG for position control, estimation of the intended torque of the user could also provide sufficient information for an effective force control of the hand prosthesis or assistive device. This paper presents the use of pattern recognition to estimate the torque applied by a human wrist and its real-time implementation to control a novel two degree of freedom wrist exoskeleton prototype (WEP), which was specifically developed for this work.MethodsBoth sEMG data from four muscles of the forearm and wrist torque were collected from eight volunteers by using a custom-made testing rig. The features that were extracted from the sEMG signals included root mean square (rms) EMG amplitude, autoregressive (AR) model coefficients and waveform length. Support Vector Machines (SVM) was employed to extract classes of different force intensity from the sEMG signals. After assessing the off-line performance of the used classification technique, the WEP was used to validate in real-time the proposed classification scheme.ResultsThe data gathered from the volunteers were divided into two sets, one with nineteen classes and the second with thirteen classes. Each set of data was further divided into training and testing data. It was observed that the average testing accuracy in the case of nineteen classes was about 88% whereas the average accuracy in the case of thirteen classes reached about 96%. Classification and control algorithm implemented in the WEP was executed in less than 125 ms.ConclusionsThe results of this study showed that classification of EMG signals by separating different levels of torque is possible for wrist motion and the use of only four EMG channels is suitable. The study also showed that SVM classification technique is suitable for real-time classification of sEMG signals and can be effectively implemented for controlling an exoskeleton device for assisting the wrist.

Portable hand motion classifier for multi-channel surface electromyography recognition using grey relational analysis

This paper proposes the portable hand motion classifier (HMC) for multi-channel surface electromyography (SEMG) recognition using grey relational analysis (GRA). SEMG provides information on motion detection of flexion and extension of fingers, wrist, forearm, and arm. A portable HMC is developed to identify hand motion from the SEMG signals with an electrode configuration system (ECS) and GRA-based classifier. The ECS consists of seven active electrodes place around the forearm to acquire the multi-channel SEMG signals of corresponding muscle groups. Six parameters are extracted from each electrode channel and various 42 (7 Channels by 6 Parameters) parameters could be constructed as specific patterns. Sequentially, these patterns are sent to the GRA-based classifier to recognize 11 hand motions. Twelve subjects including eight males and four females participate in this study. Compared with the multi-layer neural networks (MLNNs) based classifier, GRA demonstrates the processing time, computational efficiency, and accurate recognition for recognizing SEMG signals. It takes about 0.05 s CPU time to identify each hand motion which is close to a real-time process. Therefore, the GRA-based classifier could be further recommend to implement in prosthesis control, robotic manipulator or hand motion classification applications.

Classification of the mechanomyogram signal using a wavelet packet transform and singular value decomposition for multifunction prosthesis control

Previous works have resulted in some practical achievements for mechanomyogram (MMG) to control powered prostheses. This work presents the investigation of classifying the hand motion using MMG signals for multifunctional prosthetic control. MMG is thought to reflect the intrinsic mechanical activity of muscle from the lateral oscillations of fibers during contraction. However, external mechanical noise sources such as a movement artifact are known to cause considerable interference to MMG, compromising the classification accuracy. To solve this noise problem, we proposed a new scheme to extract robust MMG features by the integration of the wavelet packet transform (WPT), singular value decomposition (SVD) and a feature selection technique based on distance evaluation criteria for the classification of hand motions. The WPT was first adopted to provide an effective time–frequency representation of non-stationary MMG signals. Then, the SVD and the distance evaluation technique were utilized to extract and select the optimal feature representing the hand motion patterns from the MMG time–frequency representation matrix. Experimental results of 12 subjects showed that four different motions of the forearm and hand could be reliably differentiated using the proposed method when two channels of MMG signals were used. Compared with three previously reported time–frequency decomposition methods, i.e. short-time Fourier transform, stationary wavelet transform and S-transform, the proposed classification system gave the highest average classification accuracy up to 89.7%. The results indicated that MMG could potentially serve as an alternative source of electromyogram for multifunctional prosthetic control using the proposed classification method.

2C1-4 筋電に基づく手の動作識別法の前腕切断者を対象とした有効性の検討(OS2-1:筋電義手I,オーガナイズドセッション)

2C1-4 筋電に基づく手の動作識別法の前腕切断者を対象とした有効性の検討(OS2-1:筋電義手I,オーガナイズドセッション)

Analysis and Systematic Classification of Human Hand Movement for Robot Hand Design

The systematic classification of hand movements, which indicates the minimum mechanism of robot hands, is suggested. The performance of existent robot hands is not as high as that of human hands because the performance of existent actuators does not come up to that of human muscles in the same volume. It is important for robot hands to accomplish targeted tasks with a minimum mechanism. Human hand movements are analyzed quantitatively considering robot hands such as associated movement of DIP and PIP joints. Based on the results of analysis, we obtain three items, i.e., fingers, joints that must be set up actuators and basic movements we define. We systematically classify human hand movement for the robot hand based on three items.

Vocabulary of grips in children with hemiplegic cerebral palsy

Children with hemiplegic cerebral palsy are affected in their motor function on one side more than the other. Our focus here is on the functions of the hand, specifically examining grasp and manipulation in children with hemiplegic cerebral palsy. We concentrate on manipulation in functional settings across a range of tasks based on the classification of hand movements by Elliott and Connolly (1984) and a selection of the tasks from Brown et al (1987). The children were filmed performing the tasks, providing both qualitative and quantitative data, from which detailed information on the functional ability of each child was obtained. The value of such assessment procedures for physiotherapists and remediation in general is discussed.