Mohammed Almulla

A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue

Muscle fatigue is an established area of research and various types of muscle fatigue have been investigated in order to fully understand the condition. This paper gives an overview of the various non-invasive techniques available for use in automated fatigue detection, such as mechanomyography, electromyography, near-infrared spectroscopy and ultrasound for both isometric and non-isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who wish to select the most appropriate methodology for research on muscle fatigue detection or prediction, or for the development of devices that can be used in, e.g., sports scenarios to improve performance or prevent injury. To date, research on localised muscle fatigue focuses mainly on the clinical side. There is very little research carried out on the implementation of detecting/predicting fatigue using an autonomous system, although recent research on automating the process of localised muscle fatigue detection/prediction shows promising results.

Keyed hash function based on a chaotic map

Secure hash functions play a fundamental role in cryptographic and Web applications. They are mainly used, within digital signature schemes, to verify the integrity and authenticity of information. In this paper, we propose a simple and efficient keyed hash function based on a single chaotic map. Theoretical and simulation results demonstrate that the suggested scheme satisfies all cryptographic requirements of secure keyed hash functions such as strong confusion and diffusion capability, good collision resistance, high sensitivity to message and secret key, etc. Furthermore, it is fast and can be easily implemented through software or hardware. Moreover, the length of the hash value is flexible without any impact on the algorithm. This function is shown to have better statistical performance than many existing hash functions. Thus, the suggested hash function seems to be a good candidate as a secure keyed hash function for use in cryptographic applications.

Statistical Class Separation Using sEMG Features Towards Automated Muscle Fatigue Detection and Prediction

Surface Electromyography (sEMG) activity of the biceps muscle was recorded from ten subjects. Data were recorded while subjects performed isometric contraction until fatigue. The signals were segmented into three parts (Non-Fatigue, Transition-to-Fatigue and Fatigue), assisted by a fuzzy classifier using arm angle and arm oscillation as inputs. Nine features were extracted from each of the three classes to quantify the potential performance of each feature, also aiding towards the differentiation of the three classes of muscle fatigue within the sEMG signal. Percent change was calculated between Non-Fatigue and Transition-to-Fatigue and also between Transition-to-Fatigue and Fatigue classes. Estimation of relative class overlap using Partition Index approach was used to show features that can best distinguish between the three classes and quantifying class separability. Results show that the selected dominant frequency best discriminate between the classes, giving the highest average percent change of 159.37% and 64.75%. Partition Index showed small values confirming the percent change calculations. ©2009 IEEE.

A geometry-based coverage strategy over urban VANETs

Vehicular ad hoc networks have emerged as a promising field in wireless networking research. Unlike traditional wireless sensor networks, vehicular networks demand more consideration due to their assorted road topology, the high mobility of vehicles and the irregularly placed feasible region of deployment. As one of the most complex issues in vehicular networks, coverage strategy has been researched extensively, especially in complex urban scenarios. However, most existing coverage approaches are based on an ideal traffic map consisting of straight lines and nodes. These simplifications misrepresent the road networks. In order to provide more realistic vehicular networks deployment, this paper proposes a geometry-based coverage strategy to handle the deployment problem over urban scenarios. By taking the shape and area of road segments into account, our scheme suits different kinds of road topology and effectively solves the maximum coverage problem. To evaluate the effectiveness of our scheme, we compare this coverage strategy with α-coverage algorithm. The simulation result verifies that geometry-based coverage strategy culminates in a higher coverage ratio and a lower drop rate than α-coverage under the same constraints. The results also show that the deployment of Road Side Units (RSUs) in regions with high traffic flow is able to cover the majority of communication, so that less RSUs are able to provide better communication performance.

Classification of localized muscle fatigue with genetic programming on sEMG during isometric contraction

Genetic Programming is used to generate a solution that can classify localized muscle fatigue from filtered and rectified surface electromyography (sEMG). The GP has two classification phases, the GP training phase and a GP testing phase. In the training phase, the program evolved with multiple components. One component analyzes statistical features extracted from sEMG to chop the signal into blocks and label them using a fuzzy classifier into three classes: Non-Fatigue, Transition-to-Fatigue and Fatigue. The blocks are then projected onto a two-dimensional Euclidean space via two further (evolved) program components. K-means clustering is then applied to group similar data blocks. Each cluster is then labeled according to its dominant members. The programs that achieve good classification are evolved. In the testing phase, it tests the signal using the evolved components, however without the use of a fuzzy classifier. As the results show the evolved program achieves good classification and it can be used on any unseen isometric sEMG signals to classify fatigue without requiring any further evolution. The GP was able to classify the signal into a meaningful sequence of Non-Fatigue→Transition-to-Fatigue→Fatigue. By identifying a Transition-to Fatigue state the GP can give a prediction of an oncoming fatigue. The genetic classifier gave promising results 83.17% correct classification on average of all signals in the test set, especially considering that the GP is classifying muscle fatigue for ten different individuals.

Novel feature modelling the prediction and detection of sEMG muscle fatigue towards an automated wearable system.

Surface Electromyography (sEMG) activity of the biceps muscle was recorded from ten subjects performing isometric contraction until fatigue. A novel feature (1D spectro_std) was used to extract the feature that modeled three classes of fatigue, which enabled the prediction and detection of fatigue. Initial results of class separation were encouraging, discriminating between the three classes of fatigue, a longitudinal classification on Non-Fatigue and Transition-to-Fatigue shows 81.58% correct classification with accuracy 0.74 of correct predictions while the longitudinal classification on Transition-to-Fatigue and Fatigue showed lower average correct classification of 66.51% with a positive classification accuracy 0.73 of correct prediction. Comparison of the 1D spectro_std with other sEMG fatigue features on the same dataset show a significant improvement in classification, where results show a significant 20.58% (p < 0.01) improvement when using the 1D spectro_std to classify Non-Fatigue and Transition-to-Fatigue. In classifying Transition-to-Fatigue and Fatigue results also show a significant improvement over the other features giving 8.14% (p < 0.05) on average of all compared features.

Design of a Fast Location-Based Handoff Scheme for IEEE 802.11 Vehicular Networks

IEEE 802.11 is an economical and efficient standard that has been applied to vehicular networks. However, the long handoff latency of the standard handoff scheme for IEEE 802.11 has become an important issue for seamless roaming in vehicular environments, because more handoffs may be triggered due to the higher mobility of vehicles. This paper presents a new and fast location-based handoff scheme particularly designed for vehicular environments. With the position and movement direction of a vehicle and the location information of the surrounding access points (APs), our protocol is able to accurately predict several APs that the vehicle may possibly visit in the future and to assign these APs different priority levels. APs on higher priority levels will be scanned first. A blacklist scheme is also used to exclude those APs that showed no response to the scanning during previous handoffs. Thus, time spent on scanning APs is supposed to be significantly reduced. The simulation results show that the proposed scheme attains not only a lower prediction error rate but also lower link layer handoff latency and that it has a smaller influence on jitter and throughput. Moreover, these results show that the proposed scheme has a smaller total number of handoffs than other handoff schemes.

An efficient animal detection system for smart cars using cascaded classifiers

Animal-Vehicle Collisions (AVCs) have been a challenging problem since the creation of cars. Consequently, such collisions cause hundreds of human and animal deaths, thousands of injuries, and billions of dollars in property damage every year. To cope with this challenge, vehicles have to be equipped with smart systems able to detect animals (e.g., moose), which cross roadways, and warn drivers about the imminent danger. In this paper, we develop a new animal detection system following two criteria: detection accuracy and detection speed. To achieve these requirements, a two-stage strategy system is investigated. In the first stage, we use the LBP-Adaboost algorithm which supplies the second stage by a set of ROIs containing moose and other similar-objects. Whereas the second stage is based on an adapted version of HOG-SVM classifier. In this stage, the non-moose ROIs are rejected. To train and test our system, we create our own dataset, which is frequently updated by adding new images. Through an extensive set of simulations, we show that our system is able to detect more than 83% of moose.

On the number of candidates in opportunistic routing for multi-hop wireless networks

Opportunistic Routing (OR) is a new paradigm that has been investigated as a new way to improve the performance of multihop wireless networks by exploiting the broadcast nature of the wireless medium. In contrast to traditional routing, in OR an ordered set of nodes is selected as potential next-hop forwarders (candidates). Using more number of candidates in OR decreases the number of transmissions in the network, but this comes at the cost of increasing the signaling overhead and also the possibility of having duplicated transmissions which in turn reduces the performance of the OR protocol. The number of candidates that each node can select is an issue which is not well investigated in the literature. In this paper, we propose a Distance-based MAximum number of Candidate Estimation (D-MACE) as an approach to find the number of candidates in each node. In contrast to the traditional approaches in OR which consider an identical number of candidates for all nodes, D-MACE reduces the number of candidates in each node according to the distance between the node and the destination. We evaluate the performance of our proposal, using two relevant candidate selection algorithms. Our results show that D-MACE reduces the number of selected candidates effectively in the network, which improves the network performance compared to the case with the same number of candidates in all nodes.

A new fuzzy hybrid technique for ranking real world Web services

We propose in this article a new fuzzy hybrid ranking technique, which is based on a linear combination of two new ranking techniques we devised: an objective Fuzzy Distance Correlation Ranking Technique (FDCRT) and a subjective Fuzzy Interval-based Ranking Technique (FSIRT). The objective technique leverages the distance correlation metric to derive weights of quality attributes directly from the available data. The subjective technique computes weights from opinions of domain experts, which are specified via two ingredients: intervals representing acceptable ranges of values for quality attributes and importance values of a quality attribute with respect to the other attributes. We show that the linear combination of these two techniques allows to overcome the shortcomings of objective and subjective techniques. Our experiments are performed on a dataset of real world Web services. The empirical results show that a tuning of the proposed linear combination gives better ranking results than Entropy and Fuzzy AHP separately and even than a linear combination of these two well-known techniques.