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As an important movement of the daily living activities, sit-to-stand (STS) movement is usually a difficult task facing elderly and dependent people. In this article, a novel impedance modulation strategy of a lower-limb exoskeleton is proposed to provide appropriate power and balance assistance during STS movements while preserving the wearer’s control priority. The impedance modulation control strategy ensures adaptation of the mechanical impedance of the human–exoskeleton system toward a desired one requiring less wearer’s effect while reinforcing the wearer’s balance control ability during STS movements. A human joint torque observer is designed to estimate the joint torques developed by the wearer using joint position kinematics instead of electromyography or force sensors; a time-varying desired impedance model is proposed according to the wearer’s lower-limb motion ability. A virtual environmental force is designed for balance reinforcement control. Stability and robustness of the proposed method are theoretically analyzed. Simulations are implemented to illustrate the characteristics and performance of the proposed approach. Experiments with four healthy subjects are carried out to evaluate the effectiveness of the proposed method and show satisfactory results in terms of appropriate power assist and balance reinforcement. [ABSTRACT FROM AUTHOR]

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 arc 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. Index Terms - Assembly task, flexible assembly cell, modeling, parallel robot, singular configurations, workspace.

Gesture recognition becomes a thriving research area in modern human motion recognition systems. The intensification of demands on efficient interactive human–machine-interface systems, commercial objectives, and many other factors contributes to fuel this revival dynamics. Understanding human gestures becomes essential for prevention and health monitoring applications. In particular, analyzing hand gestures is of paramount importance in personalized healthcare-related applications to help practitioners providing more qualitative assessments of subject’s pathologies, such as Parkinson’s diseases. This work proposes a novel deep neural network approach to forecast future gestures from a given sequence of hand motion using a wearable capacitance sensor of an innovative gesture recognition hardware system. To do this, we use an attention-based recurrent neural network to capture the temporal features of hand motion to unveil the underlying pattern between the gesture and these sequences. While the attention layers capture patterns from the weights of the short term, the gated recurrent unit (GRU) neural network layer learns the inherent interdependency of long-term hand gesture temporal sequences. The efficiency of the proposed model is evaluated with respect to cutting-edge work in the field using several metrics. Note to Practitioners—In this article, the problem of human hand gesture recognition is analyzed using deep learning techniques. The proposed model uses input historical motion sequences collected from a wearable capacitance sensor to predict hand gestures. The model leverages the intrinsic correlation of motion sequences and extracts the salient part of the sequences by taking into consideration their temporal, complex, and nonlinear features. The approach studies the effect of different lengths of historical motion sequences in prediction outcomes. This allows for avoiding using cumbersome data collection, heavy data treatment, and high computational cost. The model performance is trained and assessed on real-world data by performing comparisons with alternative approaches, including well-known classifiers. The model yields very encouraging results and demonstrates that the proposed approach is quite competitive as it can reproduce typical activity trends for important channels. The present findings could help in the development of intelligent wearable devices for predicting hand gestures using a limited number of channels. This work could also help practitioners to provide a more qualitative appraisal of patients suffering from different pathologies such as Parkinson’s diseases to personalized healthcare-related applications and to develop wearable gesture recognition devices on a large scale. [ABSTRACT FROM AUTHOR]

An important and challenging research problem in web of things is how to select an appropriate composition of concrete services in a dynamic and unpredictable environment. The main goal of this article is to select from all possible compositions the optimal one without knowing a priori the users’ quality of service (QoS) preferences. From a theoretical point of view, we give bounds on the problem search space. As the QoS user's preferences are unknown, we propose a vector-valued MDP approach for finding the optimal QoS-aware services composition. The algorithm alternatively solves MDP with dynamic programming and learns the preferences via direct queries to the user. An important feature of the proposed algorithm is that it is able to get the optimal composition and, at the same time, limits the number of interactions with the user. Experiments on a real-world large size dataset with more than 3500 web services show that our algorithm finds the optimal composite services with around 50 interactions with the user. [ABSTRACT FROM AUTHOR]

In this article, a proxy-based force control method is proposed for three important human–robot interaction modes: zero-impedance mode, force assistive mode, and large force mode. A two-mass dynamic-model-based nonlinear disturbance observer is used to meet the zero-impedance output and accurate force tracking requirements with respect to disturbances from the wearer and environment. Additionally, significant force compliance can be achieved to guarantee the wearer's safety when the interaction torque is large. The proposed method is evaluated via experiments by comparison to the conventional proportional–integral–derivative and proxy-based sliding mode control methods. The results indicate that the proposed approach achieves better force tracking accuracy, robustness, and force compliance in three-mode human–robot interactions. [ABSTRACT FROM AUTHOR]

Summary: This paper deals with the control of an active ankle foot orthosis (AAFO) for paretic patients. State of the art methods using an AAFO try to track a predefined trajectory of the ankle joint while guaranteeing the wearer's safety in the presence of a large tracking error. Combining the wearer's safety and tracking accuracy is generally difficult to achieve at the same time, hence a trade-off should be found. Proxy-based sliding mode control (PSMC) offers great performances in both position tracking and safety guarantee. However, its tracking performance is subject to the influences of parameter uncertainties and external disturbances that generally occur during walking. This paper introduces an adaptation interaction method to the basic PSMC with an online adaptation of the proportional, integral and derivative parameters. At the same time, a gait phase-based ankle reference generation algorithm was proposed to adjust the joint reference trajectory in real time. The experiments using the AAFO show better tracking results with respect to basic PSMC while guaranteeing the safety. [ABSTRACT FROM AUTHOR]

Reducing human lower limb effort during walking is still a challenge for wearable exoskeletons. From the wearer’s viewpoint, joint impedance increases while wearing the exoskeleton due to the fact that the exoskeleton’s mass, friction, and gravity are added to those of the human lower limbs. Active impedance is required from the exoskeleton to compensate not only the exoskeleton’s mechanical impedance but also that of the human limb. This paper presents a nonlinear impedance reduction control (IRC) approach applied to a knee joint exoskeleton during flexion/extension movements. The IRC approach ensures impedance adaptation of the human-exoskeleton system toward a desired level. The proposed method is an alternative to sensor-based impedance controllers where the human joint torque is estimated using electromyography (EMG) sensors, force/torque sensors, and so on. The performance of the proposed approach and its robustness with respect to modeling uncertainties are theoretically analyzed and discussed. Experiments were conducted with four healthy subjects to verify the effectiveness of human effort reduction. The root mean square of the EMG signals of the muscles involved in the flexion/extension was used as a metric. The experimental results show that EMG signal levels can be efficiently reduced by inertia, damping, and gravity compensation compared with those obtained without wearing the exoskeleton. [ABSTRACT FROM AUTHOR]

Received signal strength (RSS) is a simple and low-cost method of localization in wireless sensor networks (WSNs) and is of significant interest in ambient intelligence technologies. However, RSS-based indoor localization poses important challenges due to the intrinsic characteristics of RSS measurements. This paper proposes a localization approach that accounts for the imperfection of RSS measurements and the reliability of RSS sources to estimate the target node position in an indoor WSN environment. Non-Gaussian probability density functions are used to model RSS deviations more realistically in the context of indoor environments. In addition, the proposed approach uses the Dempster–Shafer theory to represent and combine separate pieces of information (evidence) provided by more or less reliable or conflicting RSS sources (anchor nodes) on the same hypotheses regarding the target node position. Experiments conducted in two different indoor environments demonstrate the effectiveness of the proposed approach in terms of its accuracy, robustness, and computation time and its superiority compared with state-of-the-art methods. Note to Practitioners—This paper was motivated by the problem of indoor localization in the context of ambient intelligence applications. The localization technique proposed in this paper exploits RSS measurements to estimate the target node position. This technology is very attractive to system designers, due to its simplicity and low cost. This paper also suggests a new approach using, on the one hand, the belief function theory to represent and manage the imperfection of RSS measurements and the reliability of the RSS sources, and, on the other hand, a more realistic modeling of the variability of RSS measurements due to interference and attenuation phenomena that strongly affect signal propagation in indoor environments. Experimental results obtained in two different indoor environments (a residential apartment and a laboratory) are provided to demonstrate the effectiveness of the proposed approach and its superiority compared to state-of-the-art localization techniques. [ABSTRACT FROM AUTHOR]

Gait modes, such as level walking, stair ascent/descent, and ramp ascent/descent, show different lower-limb kinematic and kinetic characteristics. Therefore, an accurate detection of these modes is critical for a wearable robot to provide appropriate power assistance. In this paper, a fast gait-mode-detection method based on a body sensor system is proposed. A fuzzy logic algorithm is used to estimate the likelihoods of gait modes in real time. Since the proposed fast gait mode detection makes it possible to select appropriate kinematic and kinetic models for each gait mode, assistive torques required for assisting the human motions can be obtained more naturally and immediately. The proposed methods are all verified by experiments with a lower-limb exoskeletal assistive robot with transparent actuation by series elastic actuators, called the exoskeletal robotic orthosis for walking assistance. Four healthy subjects participated in the experiments. All subjects were asked to perform different gait modes using their normal and simulated abnormal gaits, i.e., blocking the knee joint of one leg during walking. Latency and success rate of gait mode detection are selected as performance criteria. The effectiveness of the proposed gait-mode-based assistive strategy is evaluated using electromyography muscular activities. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for automatic recognition of gait phases based on the use of an in-shoe pressure measurement system and a multiple-regression hidden Markov model (MRHMM) that takes into account the sequential completion of the gait phases. Recognition of gait phases is formulated as a multiple polynomial regression problem, in which each phase, called a segment, is modeled using an appropriate polynomial function. The MRHMM is learned in an unsupervised manner to avoid manual data labeling, which is a laborious time-consuming task that is subject to potential errors, particularly for large amounts of data. To evaluate the efficiency of the proposed approach, several performance metrics for classification are used: accuracy, F-measure, recall, and precision. Experiments conducted with five subjects during walking show the potential of the proposed method to recognize gait phases with relatively high accuracy. The proposed approach outperforms standard unsupervised classification methods (Gaussian mixture model, k-means, and hidden Markov model), while remaining competitive with respect to standard supervised classification methods (support vector machine, random forest, and k-nearest neighbor). [ABSTRACT FROM AUTHOR]

The paradigm of Internet of Robotic Things (IoRT) extends the scope of the Internet of Things by endowing any object with the three main typical functions of any robotic system: perception, actuation, and control. This paper presents a semantic framework for context-aware IoRT systems to support the development of applications for monitoring and managing IoRT systems. A knowledge representation framework, called SmartRules, is proposed for context modeling. SmartRules is a production rules language that enables reactive reasoning based on the closed world and unique name assumptions. It allows producing actions based on contextual information represented in a dedicated ontology language, called μ -Concept. An operational platform, centered on the notion of manageable object (MO), is also proposed to abstract the access to any physical or virtual device, which can communicate through the Internet. In addition, an integrated methodology and tools are proposed for guiding the development and deployment of context-aware Semantic IoRT systems, and, in particular, for defining context semantics and creating context management rules. To show the effectiveness of the proposed framework in ambient assisted living (AAL) applications, an IoRT system dedicated to the monitoring and assistance of an elderly person during his/her daily living activities is described and evaluated. [ABSTRACT FROM AUTHOR]

This paper presents a bounded control strategy of a knee-joint exoskeleton that ensures knee flexion/extension movements during the assistance and rehabilitation processes. The control strategy is easy to implement, since it does not require any prior knowledge of the human effort. The proposed control law is based on nested saturations with the main advantage of explicitly avoiding the actuator’s saturation, guaranteeing and enhancing the wearer’s security. Lyapunov-based analysis is stated to prove: 1) the asymptotic stability of the shank-foot-exoskeleton in the absence of a human active effort; 2) the trajectories ultimate boundedness in the presence of parameter uncertainties; and 3) the input-to-state stability with respect to a bounded human active torque. Preliminary real-time experiments show satisfactory results in terms of assistance-as-needed and passive/resistive rehabilitation performances following desired trajectories expressing knee flexion/extension movements. [ABSTRACT FROM AUTHOR]

Neurologic injuries, such as stroke, spinal cord injuries, and weaknesses of skeletal muscles with elderly people, may considerably limit the ability of this population to achieve the main daily living activities. Recently, there has been an increasing interest in the development of wearable devices, the so-called exoskeletons, to assist elderly as well as patients with limb pathologies, for movement assistance and rehabilitation. In this paper, we review and discuss the state of the art of the lower limb exoskeletons that are mainly used for physical movement assistance and rehabilitation. An overview of the commonly used actuation systems is presented. According to different case studies, a classification and comparison between different types of actuators is conducted, such as hydraulic actuators, electrical motors, series elastic actuators, and artificial pneumatic muscles. Additionally, the mainly used control strategies in lower limb exoskeletons are classified and reviewed, based on three types of human–robot interfaces: the signals collected from the human body, the interaction forces between the exoskeleton and the wearer, and the signals collected from exoskeletons. Furthermore, the performances of several typical lower limb exoskeletons are discussed, and some assessment methods and performance criteria are reviewed. Finally, a discussion of the major advances that have been made, some research directions, and future challenges are presented. [ABSTRACT FROM PUBLISHER]

An important challenge to be addressed in the domain of Internet of Things (IoT) is the development of efficient services selection algorithms for an optimal management of both energy and Quality of Service (QoS) in the context of IoT services composition. This issue becomes crucial in the case of large-scale IoT environments composed of thousands of distributed entities. In this paper, an energy-centered and QoS-aware services selection algorithm (EQSA) is proposed for IoT services composition. The proposed selection approach consists of preselecting the services offering the QoS level required for user's satisfaction using a lexicographic optimization strategy and QoS constraints relaxation technique. In order to reduce the energy consumption of a composite service without affecting the user's satisfaction, the most suitable services among the preselected ones are then selected using the concept of relative dominance of services in the sense of Pareto. The relative dominance of a candidate service depends on its energy profile and QoS attributes, and user's preferences. The proposed algorithm has been evaluated through several simulation scenarios. The obtained results show clearly the good performances of the EQSA algorithm in terms of selection time, energy efficiency, composition lifetime, and optimality and its added value in comparison with algorithms dealing separately with QoS and energy consumption. [ABSTRACT FROM PUBLISHER]

Action languages have emerged as an important field of knowledge representation for reasoning about change and causality in dynamic domains. This paper presents Cerbere, a production system designed to perform online causal, temporal and epistemic reasoning based on the Event Calculus. The framework implements the declarative semantics of the underlying logic theories in a forward-chaining rule-based reasoning system, coupling the high expressiveness of its formalisms with the efficiency of rule-based systems. To illustrate its applicability, we present both the modeling of benchmark problems in the field, as well as its utilization in the challenging domain of smart spaces. A hybrid framework that combines logic-based with probabilistic reasoning has been developed, that aims to accommodate activity recognition and monitoring tasks in smart spaces. [ABSTRACT FROM PUBLISHER]

The Internet of Things is a natural continuity of the Ambient Intelligence where smart and ambient environments are built by integrating a large number of interconnected smart objects with heterogeneous capabilities abstracted as software services. The aim is to design cross-domain applications that compose and select most relevant services, which best match user requirements and closely meet the specified quality-of-service level. However, semantic description and representation of such smart devices, including their hosted services and their provided real world data, is still a challenging issue. Semantic Web technologies are seen as a promising tool for this purpose. Indeed, applying these technologies in the Internet of Things enables smart objects to efficiently share their data, exchange their services and cooperate to better satisfy both functional and non-functional user requirements. In this paper, we propose a new semantic model for smart objects description and users request resolution using ontological techniques combined with description logics. Such a model facilitates intelligent functions, including reasoning over service data and semantic interoperability enabling among devices. A case study for smart environment monitoring has been proposed to illustrate the effectiveness and usability of our approach. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recognition of the wearer’s 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 wearer's 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. [ABSTRACT FROM AUTHOR]

Fault diagnosis is a critical task for operators in the context of e-TOM (enhanced Telecom Operations Map) assurance process. Its purpose is to reduce network maintenance costs and to improve availability, reliability and performance of network services. Although necessary, this operation is complex and requires significant involvement of human expertise. The study of the fundamental properties of fault diagnosis shows that the diagnosis process complexity needs to be addressed using more intelligent and efficient approaches. In this paper, we present a hybrid approach that combines Bayesian networks and case-based reasoning in order to overcome the usual limits of fault diagnosis techniques and to reduce human intervention in this process. The proposed mechanism allows the identification of the root cause with a finer precision and a higher reliability. At the same time, it helps to reduce computation time while taking into account the network dynamicity. Furthermore, a study case is presented to show the feasibility and performance of the proposed approach based on a real-world use case: a virtual private network topology. [ABSTRACT FROM PUBLISHER]

Purpose – The purpose of this paper is the control of lower limb orthosis acting at the knee joint level for a passive rehabilitation purpose. Design/methodology/approach – A control law, based on a saturated proportional derivative controller, is proposed in order to drive the shank-foot-orthosis system along a desired trajectory. Findings – The proposed control law is tested in real time using the orthosis EICOSI of the LISSI-Laboratory. The experiments show that the proposed control law is capable of providing satisfactory trajectory tracking performance given only the knee joint angle measurement. Moreover, the control law is robust with respect to external disturbances. Originality/value – Robust control of an actuated lower limb orthosis. [ABSTRACT FROM AUTHOR]

Powered orthoses are external mechanical devices used to stabilize human limbs, to restore or to reinforce lost or weak functions of people with reduced mobility. The embodied actuators produce the necessary joint torques to compensate gravity and passive effort as well as to generate the intended human movements. Nonlinearities due to human orthosis coupling, as well as modeling errors, parameter uncertainties, and external disturbances, necessitate the use of a robust closed-loop controller in order to guarantee precise movement generation. This paper aims to present a new prototype of an actuated knee joint orthosis using a robust controller. This orthosis is designed to restore or to assist knee-joint movements of dependent people. Dynamic modeling of the lower limb/orthosis is presented, and its parameters are estimated using different techniques. Control strategies based on second-order sliding mode are applied, which show satisfactory performance compared to classical controllers in terms of tracking errors and robustness with respect to parameter uncertainties and external disturbances. Real-time experiments are conducted on healthy subjects to illustrate the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Using supervised machine learning approaches to recognize human activities from on-body wearable accelerometers generally requires a large amount of labeled data. When ground truth information is not available, too expensive, time consuming or difficult to collect, one has to rely on unsupervised approaches. This paper presents a new unsupervised approach for human activity recognition from raw acceleration data measured using inertial wearable sensors. The proposed method is based upon joint segmentation of multidimensional time series using a Hidden Markov Model (HMM) in a multiple regression context. The model is learned in an unsupervised framework using the Expectation-Maximization (EM) algorithm where no activity labels are needed. The proposed method takes into account the sequential appearance of the data. It is therefore adapted for the temporal acceleration data to accurately detect the activities. It allows both segmentation and classification of the human activities. Experimental results are provided to demonstrate the efficiency of the proposed approach with respect to standard supervised and unsupervised classification approaches. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]