Feature decision-making ant colony optimization system for an automated recognition of plant species

In the present paper, an expert system for automatic recognition of different plant species through their leaf images is investigated by employing the ant colony optimization (ACO) as a feature decision-making algorithm. The ACO algorithm is employed to investigate inside the feature search space in order to obtain the best discriminant features for the recognition of individual species. In order to establish a feature search space, a set of feasible characteristics such as shape, morphology, texture and color are extracted from the leaf images. The selected features are used by support vector machine (SVM) to classify the species. The efficiency of the system was tested on around 2050 leaf images collected from two different plant databases, FCA and Flavia. The results of the study achieved an average accuracy of 95.53% from the ACO-based approach, confirming the potentials of using the proposed system for an automatic classification of various plant species

Simple and computationally efficient movement classification approach for EMG-controlled prosthetic hand: ANFIS vs. artificial neural network

The aim of this paper is to propose an exploratory study on simple, accurate and computationally efficient movement classification technique for prosthetic hand application. The surface myoelectric signals were acquired from 2 muscles—Flexor Carpi Ulnaris and Extensor Carpi Radialis of 4 normal-limb subjects. These signals were segmented and the features extracted using a new combined time-domain method of feature extraction. The fuzzy C-mean clustering method and scatter plots were used to evaluate the performance of the proposed multi-feature versus other accurate multi-features. Finally, the movements were classified using the adaptive neuro-fuzzy inference system (ANFIS) and the artificial neural network. Comparison results indicate that ANFIS not only displays higher classification accuracy (88.90%) than the artificial neural network, but it also improves computation time significantly

Hand movements classification for myoelectric control system using adaptive resonance theory

This research proposes an exploratory study of a simple, accurate, and computationally efficient movement classification technique for prosthetic hand application. Surface myoelectric signals were acquired from the four muscles, namely, flexor carpi ulnaris, extensor carpi radialis, biceps brachii, and triceps brachii, of four normal-limb subjects. The signals were segmented, and the features were extracted with a new combined time-domain feature extraction method. Fuzzy C-means clustering method and scatter plot were used to evaluate the performance of the proposed multi-feature versus Hudgins’ multi-feature. The movements were classified with a hybrid Adaptive Resonance Theory-based neural network. Comparative results indicate that the proposed hybrid classifier not only has good classification accuracy (89.09 %) but also a significantly improved computation time

Adaptive resonance theory-based hand movement classification for myoelectric control system

Electromyography (EMG) also referred to as the Myoelectric, is a biomedical signal acquired from skeletal muscles. Skeletal muscles are attached to the bone responsible for the movements of the human body. In case of prosthetic hand, an EMG based control system known as Myoelectric Control System (MCS) has been widely attracted research in the field. Despite there has been a great development in prosthetic hand industry during the last decade, it is considerably needed to investigate an effective control algorithm for affordable prosthetic hand. This thesis investigates a pattern recognition approach for MCS that classifies hand movements accurately and computationally efficient to actuate different functions of a prosthetic hand. Five distinct hand movements are classified with an Adaptive Resonance Theory (ART) based neural network implemented, as it uses a combination of features extracted from four EMG signals. In order to prove the contribution of the proposed MCS approach, two different evaluation processes have been done. First evaluation considers the investigation of feature extraction method; where the proposed multi-feature consisting of Mean Absolute Value (MAV), Zero Crossing (ZC), Waveform Length (WL), Slope Sign Change (SSC), Root Mean Square (RMS), and Mean Frequency (MNF) has been compared to 2 well-known high accuracy and simple multi-feature methods. The second evaluation is included comparing ART-based methods versus Linear Discriminant Ananlysis (LDA) and k-Nearest neighbor (KNN) as two accurate and simple implementing classifiers. The study outcome reveals that the proposed multi-feature has better extraction performance in terms of class separability and accuracy; while the performance for the proposed multi-feature (82.51%) is at least 6% better than the other 2 methods. Classification results obtained by using the proposed multi-feature have shown better performance of ART-based methods; considering average accuracy of 89.09% for the ART method, 83.98% for the KNN and 82.52% for the LDA. Further investigation has been done on a computation time evaluation between proposed ART-based methods, LDA and KNN. Regarding training time (75.69ms), classification time (49.57 ms) and elapsed time (3.77s), evaluation showed significantly less computation time of ART-based methods than LDA : training time (153.65ms), classification time (344.2 ms) and elapsed time (7.92 s) and KNN:training time (165.42 ms), classification time (230.91 ms) and elapsed time (6.58 s). At last, an accurate and computationally efficient hand movements’ classification approach for Myoelectric Control System (MCS) has achieve

Designing a user-defined gesture vocabulary for an in-vehicle climate control system

Hand gestures are a suitable interface medium for in-vehicle interfaces. They are intuitive and natural to perform, and less visually demanding while driving. This paper aims at analysing human gestures to define a preliminary gesture vocabulary for in-vehicle climate control using a driving simulator. We conducted a user-elicitation experiment on 22 participants performing two driving scenarios with different levels of cognitive load. The participants were filmed while performing natural gestures for manipulating the air-conditioning inside the vehicle. Comparisons are drawn between the proposed approach to define a vocabulary using 9 new gestures (GestDrive) and previously suggested methods. The outcomes demonstrate that GestDrive is successful in describing the employed gestures in detail.5 page(s