Yacine Amirat

Physical Human Activity Recognition Using Wearable Sensors.

This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors’ placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.

Modeling and Control of a Hybrid Continuum Active Catheter for Aortic Aneurysm Treatment

Endovascular aortic aneurysm treatment is a minimally invasive surgery (MIS) which requires high dexterity for stentgraft placement. This procedure requires technological improvements. For this purpose we have developed a new tool called MALICA (Multi Active LInk CAtheter). MALICA is an active catheter with a multi continuum micro-robots stack inside its external sheath. In this paper we present a new direct static model formulation of MALICA along with an orientation control scheme using the redundancy property of this robot.

Lower-Limb Movement Assistance through Wearable Robots: State of the Art and Challenges

Recent technological advances made necessary the use of robots in various types of applications. Unlike traditional robot-like scenarios dedicated to industrial applications with repetitive tasks, the current focus of attention is on applications that require close human interactions. One of the main fields of such applications concerns assisting and rehabilitating of dependent/elderly persons. In this study, the state-of-the-art of the main research advances in lower-limbs human assistance is presented. This includes a review on research covering mainly lower-limb actuated exoskeletons. Some case studies related to full-limb exoskeletons are presented as well. Lower-limb movement restoration using functional electrical stimulation and treadmill-based rehabilitation devices is also investigated. In addition, the seamless integration of wearable robots in ambient intelligence spaces appears as one of the major challenges for ubiquitous environments and ambient assisted living. Some open issues related to this integration are discussed in this paper.

Analysis and design of a six-DOF parallel manipulator, modeling, singular configurations, and workspace

In this paper, a new architecture of a parallel robot with six degrees of freedom is presented. This device is well adapted to perform force feedback control, and under some conditions, can be fitted with a center of compliance. This robot has been designed in order to obtain a symmetric and compact structure. The particular properties of its geometric and kinematic models with respect to that of a classical parallel robot are addressed. Due to the fact that each actuator keeps a constant orientation with respect to the static part, we show that the direct model has a single analytical solution. This result leads us to characterize the robot singularities and the reachable workspace. To demonstrate the capability of the proposed structure, an application of the C5 parallel robot acting as a force controlled active wrist in an assembly task is described. Furthermore, the hardware and software control system is presented.

Towards intelligent lower limb wearable robots: Challenges and perspectives - State of the art

Recent technological advances made necessary the use of the robots in various types of applications. Currently, the traditional robot-like scenarios dedicated to industrial applications with repetitive tasks, were replaced by applications which require human interaction. The main field of such applications concerns the rehabilitation and aid of elderly persons. In this study, we present a state-of-the-art of the main research advances in lower limbs actuated orthosis/wearable robots in the literature. This will include a review on researches covering full limb exoskeletons, lower limb exoskeletons and particularly the knee joint orthosis. Rehabilitation using treadmill based device and use of Functional Electrical Stimulation (FES) are also investigated. We discuss finally the challenges not yet solved such as issues related to portability, energy consumption, social constraints and high costs of theses devices.

Control of Upper-Limb Power-Assist Exoskeleton Using a Human-Robot Interface Based on Motion Intention Recognition

Recognition of the wearers motion intention plays an important role in the study of power-assist robots. In this paper, an intention-guided control strategy is proposed and applied to an upper-limb power-assist exoskeleton. Meanwhile, a human-robot interface comprised of force-sensing resistors (FSRs) is designed to estimate the motion intention of the wearers upper limb in real time. Moreover, a new concept called the “intentional reaching direction (IRD)” is proposed to quantitatively describe this intention. Both the state model and the observation model of IRD are obtained by studying the upper limb behavior modes and analyzing the relationship between the measured force signals and the motion intention. Based on these two models, the IRD can be inferred online using an adapted filtering technique. Guided by the inferred IRD, an admittance control strategy is deployed to control the motions of three DC motors placed at the corresponding joints of the robotic arm. The effectiveness of the proposed approaches is finally confirmed by experiments on a 3 degree-of-freedom (DOF) upper-limb robotic exoskeleton.

Design and control of a new six DOF parallel robot: Application to equestrian gait simulation

A new six DOF parallel robot which is derived from a classic Stewart platform is presented. The spatial arrangement of the links and the type of actuators, which are hydraulic jacks, induce interesting geometrical and dynamical performances. After the description of the mechanical architecture of this robot, the inverse geometrical and kinematical models are presented, along with the control system. The robot is used as an equestrian simulator and the corresponding control laws have been deduced from data which have been experimentally measured on an actual horse. The gait playback is discussed in the case of the trot, and the interest of the robot so designed is confirmed by the results evaluated by experienced horse riders.

Optimization of fault diagnosis based on the combination of Bayesian Networks and Case-Based Reasoning

Fault diagnosis is one of the most important tasks in fault management. The main objective of the fault management system is to detect and localize failures as soon as they occur to minimize their effects on the network performance and therefore on the service quality perceived by users. In this paper, we present a new hybrid approach that combines Bayesian Networks and Case-Based Reasoning to overcome the usual limits of fault diagnosis techniques and reduce human intervention in this process. The proposed mechanism allows identifying the root cause failure with a finer precision and high reliability while reducing the process computation time and taking into account the network dynamicity.

Nonlinear disturbance observer based sliding mode control of a human-driven knee joint orthosis

The present paper deals with the control of a knee joint orthosis intended to be used for rehabilitation and assistive purposes. A model, integrating human shank and orthosis, is presented. To reduce the influence of the uncertainties in muscular torque modeling on the system control, a nonlinear observer is proposed to estimate the muscular torque developed by the wearer. Additionally, a robust terminal sliding mode control approach combined with the nonlinear observer is presented. To illustrate the effectiveness of the proposed control method, a comparison with two control methods, basic sliding mode and sliding mode with nonlinear observer, are also given. The asymptotic stability of the presented approaches and observer convergence are proved by means of a Lyapunov analysis. Furthermore, the proof of advantage of the robust terminal sliding mode control method with the nonlinear observer (improving the tracking precision and reducing the required time for eliminating external disturbances) is proposed as well. The experiment results show that the robust terminal sliding mode control approach combined with the nonlinear observer has a significant advantage with respect to the position tracking and robustness regarding the modeling identification errors and external disturbances. This paper deals with the control of a knee joint exoskeleton for rehabilitation purposes.An observer is proposed to estimate the muscular torque developed by the wearer.A robust terminal sliding mode control method combined with the observer is presented.Asymptotic stability is proved by means of a Lyapunov analysis.Experiment tests were conducted with three subjects.

A robust adaptive control of a parallel robot

The work presented in this article deals with the robust adaptive control tracking of a 6 degree of freedom parallel robot, called C5 parallel robot. The proposed approach is based on the coupling of sliding modes and multi-layers perceptron neural networks (MLP-NNs). It does not require the inverse dynamic model for deriving the control law. The MLP-NN is added in the control scheme to estimate the gravitational and frictional forces along with the non-modelled dynamic effects. The nonlinearity problem, present in neural networks, is resolved using Taylor series expansion. The proposed approach allows to adjust the parameters of neural network and sliding mode control terms by taking into account a reference model and the closed-loop stability in the Lyapunov sense. We implemented our approach on the C5 parallel robot of LISSI laboratory and performed experiments to observe its effectiveness and the robust behaviour of the controller against external disturbances.