This study aims to explore the potential for accurately estimating joint angles during upper limb rehabilitation tasks with different calibration procedures, inverse kinematics methods and measurement modalities. Affordable embedded visual-inertial measurement units offer a promising alternative to the costly and cumbersome gold standard marker-based optical motion capture systems. However, affordability comes with inherent sensors inaccuracies. Hence, prior to their application in a real clinical setting, it is important to demonstrate their ability for accurate joint angle estimation. Discrepancies in joint angles arise due to the inaccuracies of different sensing modalities but also to sensor-to-segment calibration procedures that significantly alter the joint offsets. Therefore, in this paper, the impact of functional and anatomical calibration procedures on joint angle estimation was compared among seven healthy young volunteers. When the same calibration procedures were applied with visual-inertial measurement units and optical motion capture systems data, a relatively small root mean square error of 7.9 deg and correlation coefficients exceeding 0.86 were observed. When different calibration procedures were applied with visual-inertial measurement units and optical motion capture systems data, higher root mean square superior to 10 deg were observed, highlighting the importance of consistency with the reference set when assessing accuracy. Furthermore, our analysis shows the benefit of using multi-body inverse kinematics procedure over treating inverse kinematics separately for each segment when dealing with inaccurate visual-inertial measurement units data. Note to Practitioners—This study addresses the practical challenge of accurately estimating upper limb joint angles in rehabilitation, using affordable Visual-Inertial Measurement Units (VIMUs) and cameras. The key finding for practitioners is the importance of consistent calibration procedures, either anatomical or functional, across both VIMUs and standard reference systems. This consistency significantly improves measurement accuracy, essential for effective rehabilitation assessment and planning. We also demonstrate that multi-body inverse kinematics (IK) methods are more reliable than single-body IK when using data from low-cost sensors. Multi-body IK better handles inaccuracies typical of affordable devices, making it a more suitable choice for clinical applications. While our results are promising, they are based on controlled conditions and do not encompass whole-body movements. Future research should focus on extending these findings to more diverse and challenging clinical scenarios, ensuring the practical applicability of this cost-effective technology in real-world rehabilitation settings.

Active suspended backpacks represent a promising solution to mitigate the impact of inertial forces on individuals engaged in load carriage. However, identifying effective control objectives aimed at enhancing human carrying capacity remains a significant challenge. In this study, we introduce a novel approach by integrating a limb-like structure-type (LLS) bioinspired vibration isolator, modeled using Lagrangian mechanics, into an active load-transfer suspended backpack to primarily alleviate human shoulder pressure, thereby constructing a humanrobot interaction control framework for the system. Drawing from a double-mass coupled oscillator model, this approach formulates a vertical dynamics model for the human-backpack system, systematically exploring the principles of both static load transfer and dynamic load reduction on the human shoulder. Subsequently, a series elastic actuators-based controller with prescribed performance is proposed to simultaneously achieve trajectory tracking and ensure load motion within the limited range. Theoretically, we validate the input-output stability of the LLS model and guarantee the ultimate uniform boundedness of the closed-loop system. Simulation and experimental trials conducted across different terrain scenarios validate the effectiveness of the proposed method, highlighting reductions of 18.68% in metabolic rate during level ground walking, 9.58% in a staircase scenario and 12.35% in a complex terrain, involving uphill, downstairs, and flat ground walking.

Supernumerary robotic limbs (SRLs) offer substantial potential in both the rehabilitation of hemiplegic patients and the enhancement of functional capabilities for healthy individuals. Designing a general-purpose SRL device is inherently challenging, particularly when developing a unified theoretical framework that meets the diverse functional requirements of both upper and lower limbs. In this paper, we propose a multi-objective optimization (MOO) design theory that integrates grasping workspace similarity, walking workspace similarity, braced force for sit-to-stand (STS) movements, and overall mass and inertia. A geometric vector quantification method is developed using an ellipsoid to represent the workspace, aiming to reduce computational complexity and address quantification challenges. The ellipsoid envelope transforms workspace points into ellipsoid attributes, providing a parametric description of the workspace. Furthermore, the STS static braced force assesses the effectiveness of force transmission. The overall mass and inertia restricts excessive link length. To facilitate rapid and stable convergence of the model to high-dimensional irregular Pareto fronts, we introduce a multi-subpopulation correction firefly algorithm. This algorithm incorporates a strategy involving attractive and repulsive domains to effectively handle the MOO task. The optimized solution is utilized to redesign the prototype for experimentation to meet specified requirements. Six healthy participants and two hemiplegia patients participated in real experiments. Compared to the pre-optimization results, the average grasp success rate improved by 7.2%, while the muscle activity during walking and STS tasks decreased by an average of 12.7% and 25.1%, respectively. The proposed design theory offers an efficient option for the design of multi-functional SRL mechanisms.

The implementation of real-time gait mode detection is paramount in effectively providing tailored support for individuals utilizing actuated ankle-foot orthoses (AAFOs), thereby enhancing their walking capabilities and overall mobility. However, existing systems often rely on multiple sensors and struggle with accurate and prompt detection of gait transitions, especially in varied and challenging environments. This study aims to develop a novel real-time gait mode detection system that accurately identifies five essential daily living gait modes, namely level walking, ramp ascent/descent, and stair ascent/descent using only two foot-mounted inertial measurement units (IMUs). By using a long short-term memory (LSTM)-based algorithm trained on data collected from ten healthy subjects, the system extracts six kinematic features to predict gait modes with high accuracy. The proposed method integrates this detection system with a task-oriented control strategy to adapt the control of the AAFO based on the identified gait modes. The real-time experiments involving three healthy participants demonstrated robust gait mode detection, achieving an average estimation accuracy of$98 \pm 1$% across the five gait modes, even with the application of assistive torque. In cases mimicking abnormal gait, the system maintained an accuracy of$93 \pm 3$%. Additionally, each transition delay between gait modes was analyzed, showing that gait mode detection can occur between the transitions of the leading and trailing foot. The results of the control strategy showed a reduction in muscle activation of the dorsiflexor and plantarflexor muscles as measured by EMG, as well as improved tracking performance during the swing phase. Gait mode detection robustness was further evaluated by including walking with obstacles and changes in environmental dimensions.

This paper introduces a novel funnel-based adaptive ${{\mathcal{L}}_1}$ fuzzy control strategy for assisting ankle joint movement during walking with the use of an actuated ankle foot orthosis (AAFO). A projection-based adaptation mechanism employing a fuzzy system is used to estimate the unknown time-varying parameters of the ${{\mathcal{L}}_1}$ control law, ensuring precise tracking of the AAFO-wearer system by the state estimator. The projection operator guarantees the convergence of the parameters while offering a limited amount of assistance torque. Funnel-based feedback control is used to mitigate the typical time lag seen when using ${{\mathcal{L}}_1}$-based approaches due to the presence of a low-pass filter commonly used in this type of approach. The effectiveness of the proposed control strategy is demonstrated through real-time experiments involving five healthy subjects.

Stroke affects a large number of people every year. One consequence is the weakness of ambulatory muscles resulting in a paretic gait. Actuated ankle foot orthoses can be a solution to assist paretic patients to dorsiflex and/or plantar flex their ankle joint during the gait phases. To assist the wearer following a predefined ankle joint desired trajectory, an adaptive active disturbance rejection controller is proposed in this study. The human muscular torque and estimation errors are estimated through a nonlinear disturbance observer based on the estimated model. This estimated torque is compensated within the proposed projection based adaptive controller combined to a saturated proportional derivative term. The purposes of using this controller are: i) the no need of prior system's parameter identification due to the adaptive structure, ii) the assistance-as-needed of the wearer through the rejection term and iii) the avoidance of the actuator saturation by including projection and saturation functions. This controller is tested in real time using an actuated ankle-foot-orthosis (AAFO) in lab environment with three healthy subjects to show its effectiveness.

This study aims at developing a new affordable motion capture system for human upper limbs' joint kinematics estimation based on a reduced set of visual inertial measurement units coupled with a markerless skeleton tracking algorithm. The markerless skeleton tracking algorithm allows to alleviate the kinematic redundancy that is observed if only a single visual inertial measurement unit is used at the hand level but it introduces undesired outliers. A Sliding Window Inverse Kinematics Algoritm based on a biomechanical model is proposed to filter out outliers. It has the advantage to constrain the evolution of joint kinematics while being able to handle multi- modalities. The proposed system was validated with five healthy volunteers performing a popular rehabilitation pick and place task. Joint angles estimated using our method were compared with the ones obtained using a reference stereophotogrammetric system. The results showed an average root mean square error of 9.7deg along with an average correlation of 0.8. These results compare favorably with literature results obtained with more numerous and relatively costly sensors or more elaborated and expensive markerless systems.

The dynamic load attached to the load gravity imposes an excessive burden to human shoulders during load carriage, resulting in possible muscle injuries and additional physical exertion. This paper proposes an active suspension backpack, capable of transferring partial load from human shoulders to pelvis and alleviating the dynamic load through separated panels and motor actuation, to reduce pressure on human shoulders and improve walking metabolic efficiency. Based on the human body motion in the vertical direction, the dynamical model of the human-backpack system with shoulder interaction force measured by a soft ballonet with an embedded air pressure sensor is introduced, and an impedance controller has been implemented to maintain a relatively small and constant pressure on the shoulder. In an experimental case study, we presents preliminary results of three healthy subjects performing a treadmill walking with a 20kg load in ACTIVE configuration where the shoulder pressure shows a decrease by 30% along with a reduction of the metabolic energy consumption by 16.4%, compared with the load LOCKED case.

This study aims at developing and evaluating an affordable and user-friendly motion capture system for human upper limbs’ joint kinematics estimation. The objective is to provide quantitative assessments during the clinical evaluation of poststroke patients performing daily living activities. The proposed system is based on the simultaneous use of affordable inertial measurement units, and a set of augmented reality markers tracked with an affordable RGB camera. Two practical calibration processes were developed to calibrate the sensors modules and then to determine their location on body segments. Then, all measured quantities were fused into a constrained extended Kalman filter based on an upper limbs’ biomechanical model. The proposed system was validated with nine healthy volunteers performing five daily living activities. Joint angles estimated using the proposed affordable system were compared with a gold standard stereophotogrammetric system. The results showed a low average rms difference (2.7°) along with a high average correlation (0.87).Note to Practitioners—This article was motivated by the problem of assessing human upper limbs’ mobility during a rehabilitation process. Existing systems are often inaccurate and/or not affordable. This article suggests a new approach by combining measurements from inertial measurement units and augmented reality markers into an adaptive filter that is taking into account the kinematics model of the human arm and its limitation to filter out unfeasible solutions. The system is also making use of a new very practical calibration method not requiring any external equipment while remaining very affordable. Experimental results suggest that the proposed system is able to estimate more accurately than state-of-the-art joint angles of the upper limbs.

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.

The papers in this special section focus on the development and applications supported by wearable robots. Wearable powered robots may be used for functional substitution in patients suffering from motor disorders, rehabilitation, assistance, and strength augmentation. Despite recent technological and scientific achievements, more research is needed to realize the promise of intuitive, easy-to-wear, safe, and effective wearable robots

As a life-threatening disease, stroke can lead to long-term problems affecting the patients’ daily living ability. A common problem facing post-stroke patients is foot drop. An emerging modality of interest for correcting the foot drop is to combine both actuated ankle-foot orthosis (AAFO) and functional electrical stimulation (FES). Such hybrid assistive system not only ensure effective assistance but also can avoid fast muscular fatigue due to excessive muscular stimulation. Due to the significant changes in the ankle joint’s kinematics and kinetics with gait cycles, optimization control strategies for hybrid AAFO and FES systems are highly demanded. However, it is challenging to develop accurate gait phase detection algorithms to guide the control of AAFO and FES while ensuring robustness with respect to the diversity and variability of patients’ gaits. In this paper, we present a novel swing sub-phase detection algorithm based on a moving average convergence divergence (MACD) indicator. The proposed detection algorithm uses only information collected from the affected leg by means of two inertia measurement units (IMU) and the AAFO. Moreover, a gait-phase based control strategy is developed to optimize the assistive effect of a hybrid AAFO and FES system. Experimental results with five healthy show the potential of the proposed approaches in ensuring both satisfactory ankle joint trajectory tracking and effective reduction in stimulation intensity, compared to the use of conventional FES assistance.

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.

Analysis of human daily living activities, particularly walking activity, is essential for health-care applications such as fall prevention, physical rehabilitation exercises, and gait monitoring. Studying the evolution of the gait cycle using wearable sensors is beneficial for the detection of any abnormal walking pattern. This paper proposes a novel discrete/continuous unsupervised Hidden Markov Model method that is able to recognize six gait phases of a typical human walking cycle through the use of two wearable Inertial Measurement Units (IMUs) mounted at both feet of the subject. The results obtained with the proposed approach were compared to those of well-known supervised and unsupervised segmentation approaches. The obtained results show the efficiency of the proposed approach in accurately recognizing the different gait phases of a human gait cycle. The proposed model allows the consideration of the sequential aspect of the walking gait phases while operating in an unsupervised context that avoids the process of data labeling, which is often tedious and time-consuming, particularly within a massive-data context.

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.

This paper presents a practical method to identify body segments inertial parameters of a human-exoskeleton system using affordable and easy-to-use sensors. First, the joints and the base kinematics are estimated based on the use of an extended Kalman filter and QR visual markers. Then, joints kinematics are used in a dynamic identification pipeline together with the ground reaction force and moments collected with an affordable Wii Balance Board. The identification process is done using an augmented regressor matrix to identify at once each segment mass, center of mass 3D position and inertia tensor elements of both human locomotor apparatus and exoskeleton. The proposed method is able to accurately estimate external force and moments, with less than 6 % of normalized RMS difference in average, and is experimentally validated with a subject wearing a full lower limb exoskeleton.

This paper describes a cooperative control approach that combines the use of a powered knee joint orthosis along with Functional Electrical Stimulation (FES) for knee joint flexion-extension movement restoration. A closed-loop adaptive control and an open-loop FES of the quadriceps muscle group are combined together to track a desired knee joint angle trajectory of flexion/extension movements. A nonlinear disturbance observer is used to estimate the torque provided by the subject's muscles through the FES. Simulations and experiments with a healthy subject show the feasibility of the proposed approach. Experiments show the repeatability of motion and the complementarity between the torque provided by the quadriceps muscle through FES and the one delivered by the orthosis actuator to ensure satisfactory tracking of the desired trajectory.

In this paper, a model reference adaptive control with saturated proportional derivative (PD) action for an active ankle foot orthosis (AAFO) to assist the gait of paretic patients, is studied. Unlike most classical model-based controllers, the proposed controller does not require any prior estimation of the system's model parameters. The AAFO system is actively driven by the residual human torque delivered by muscles spanning the ankle joint and the AAFO's actuator's torque. The ankle reference trajectory is updated online based on the self-selected walking speed of the wearer. The input-to-state stability of the AAFO-wearer system with respect to a bounded human muscular torque is proved in closed-loop based on a Lyapunov analysis. Experimental results, obtained from one healthy subject and one paretic patient, show satisfactory results in terms of tracking performance and ankle joint assistance throughout the full gait cycle.

This paper presents an adaptive knee-joint based Functional Electrical Stimulation (FES)method to correct the foot drop of paretic patients during swing phase. The rationale behind the adaptive FES is to amplify dorsiflexor stimulation in the late swing when it is most needed in order to face the increased plantar flexor co-contraction as gastrocnemius muscles are stretched by knee re-extension. To accurately detect the swing phase (i.e., toes off (TO)and initial contact (IC)), a novel algorithm is proposed by using a foot-mounted inertial measurement unit (IMU). The proposed strategy is verified by experiments conducted with three healthy subjects and three paretic patients. The experimental results show that highly accurate detection of TO/I C can be achieved under different walking speeds and foot contact conditions (normal and abnormal gaits). The clinical experimental results with paretic patients also reveal that similar effects on ankle dorsiflexion can be observed during mid and late swing using the proposed adaptive FES with respect to the classical FES method, while the adaptive FES used lower stimulation intensity.

Posture analysis in quiet standing is an essential element in evaluating human balance control. Many factors enhance the human control system's ability to maintain stability, such as the visual system and base of support (feet) placement. In contrast, many neural pathologies, such as Parkinson's disease (PD) and cerebellar disorder, disturb human stability. This paper addresses the problem of the automatic segmentation of stabilometric signals recorded under four different conditions related to vision and foot position. This is achieved for both control subjects and PD subjects. A hidden Markov model (HMM)regression-based approach is used to carry out the segmentation between the different conditions using simple and multiple regression processes. Twenty-eight control subjects and thirty-two PD subjects participated in this study. They were asked to stand upright while recording stabilometric signals in mediolateral and anteroposterior directions under two permutations: feet apart and together with eyes open or closed. The results show high values for the correct segmentation rates, up to 98%, for the separation between the different conditions. The present findings could help clinicians better understand the motor strategies used by the patients during their orthostatic postures and may guide the rehabilitation process. The proposed method compares favorably with standard segmentation approaches.

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.

As an important movement of the daily living activities, sit-to-stand (STS) movement is usually a difficult task facing elderly and dependent people. To provide appropriate power assistance for the sit-to-stand movement, a novel intention-based Active Impedance Control (AIC) strategy applied on a lower limb exoskeleton is proposed in this paper. The AIC is able to adapt the mechanical impedance of the human-exoskeleton system towards a desired one using the exoskeleton's power assistance. In the AIC structure, a human joint torque observer is designed to estimate the human joint torques using joint angles information instead of electromyography (EMG) or force/torque sensors; a time-varying desired impedance model is proposed according the wearer's lower limb motion ability. Simulations were implemented to illustrate the characteristics and performances of the proposed approach. Experiments with a healthy subject were carried out to evaluate the effectiveness of the proposed method. The experiments show satisfactory results in terms of appropriate power assist based on the wearer's motion intention.

This paper deals with the control of a lower limb exoskeleton acting at the knee joint level. Classical −1 adaptive control law is proposed to ensure assistance-as-needed and resistive rehabilitation following a desired trajectory that is defined by a therapeutic doctor. This control law introduces a time lag within the desired trajectory tracking due to the presence of a filter in its structure. In order to mitigate this drawback, the classical −1 adaptive control is augmented by a nonlinear proportional control. The classical and augmented −1 adaptive control laws are tested in real-time using the Exoskeleton Intelligently COmmunicating and Sensitive to Intention (EICOSI) of LISSI-lab. Real-time experimental results highlight the utility of these control laws in assistance-as-needed and resistive rehabilitation paradigms.

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.

A number of control methodologies have been studied for assistive robotic technologies. Since the human motions in a daily life consist of multiple phases, such as walking, sitting and standing, controllers for assistive robots are required to be able to cope with different motion phases. For this reason, hybrid control which is able to occasionally switch control algorithms according to the motion phases has been preferred in the assistive robots, in particular wearable robots. In this paper, a generalized control framework is proposed as a fundamental framework for the hybrid assistive control and its stability is analyzed using the framework. The proposed control framework is implemented into a lower-limb exoskeleton robot and its effectiveness is verified thorough experiments.

This paper presents a new human-exoskeleton interaction approach to provide torque assistance of the lower limb movements upon wearer's intention. The exoskeleton interacts with the wearer; the shank-foot orthosis system behaves as a second order dynamic system with gravity and elastic torque balance. The intention of the wearer is estimated by using a realistic musculoskeletal model of the muscles actuating the knee joint. The identification process concerns the inertial parameters of the shank-foot, the exoskeleton and the musculotendon parameters. Real-time experiments, conducted on a healthy subject during flexion and extension movements of the knee joint, have shown satisfactory results in terms of tracking error, intention detection and assistance torque generation. This approach guarantees asymptotic stability of the shank-foot-exoskeleton and adaptation to human-exoskeleton interaction. Moreover, the proposed control law is robust with respect to external disturbances.

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.

Ambient intelligence, ubiquitous and networked robots, cloud robotics, are new research hot topics that start to gain popularity among the robotics community. They enable robots to acquire richer functionalities and open the way for the composition of a variety of robotic services with three functions: semantic perception, reasoning and actuation. This paper introduces the recent challenges and future trends of these topics.

The paper concerns the control of a lower limb orthosis acting on the knee joint level. Therefore, a model of the shank-orthosis system is given considering the human effort as an external torque acting on the system. A model reference adaptive control law is developed and applied to the orthosis in order to make the system (shank-orthosis) track a desired trajectory predefined by a rehabilitation doctor. The main advantage of this control law is the on-line parameters regulation allowing to ensure the best performance of the system. A Lyapunov-based analysis is performed to prove the input-to-state stability of the orthosis with respect to a bounded human torque. The performance of the system is then shown through some simulations.

Unsupervised learning approaches are used in various applications such as speech recognition, image compression, information retrieval and activity recognition. This paper introduces a novel unsupervised approach for clustering multi-dimensional time series that present the 3-d acceleration data measured with body-worn accelerometers. More specifically, the proposed approach uses a statistical model based on Multiple Hidden Markov Model Regression (MHMMR) to automatically analyze the human activity. This method takes into account the sequential appearance and temporal evolution of the data to easily detect static and dynamic activities. Comparisons with existing unsupervised approaches, including the standard Gaussian Mixture Model, the k-means algorithm, the DBSCAN algorithm and the standard HMM, demonstrate the effectiveness of the proposed approach.

In this paper, we present a robust controller of a new knee joint orthosis. This orthosis is intended to help or to restore lower limb movements of people with reduced mobility. Dynamic modeling and parametric identification of the knee joint-orthosis system are presented. Due its robustness, a High Order Sliding Mode Controller (HOSMC) is used to control the knee joint. Experiments were conducted on a person in sitting position with flexion/extension of the knee. Performances of the HOSMC are compared to those of a classical Proportional Integrator Derivative (PID) controller in terms of stability, tracking trajectory, convergence in a finite time and robustness against external perturbations.

The main challenge that we face when applying functional electrical stimulation (FES) to paralyzed lower limbs is to avoid hyperstimulation and to defer the muscular fatigue as much as possible. FES is used to excite paralyzed muscles that are under lesions and consequently no more controlled by paraplegic patients. We aimed in this study to compute the needed patterns stimulation necessary to perform a desired given motion of the knee joint. We coupled the exact input output feedback linearization with a model predictive controller (MPC). This latter enables us to incorporate explicitly constraints on inputs, outputs and system states. Internal dynamics stability was mathematically proved and MPC performances were compared to a classical pole placement controller in terms of robustness, stability and finite time convergence.

The main goal when applying functional electrical stimulation (FES) to the paralyzed lower limbs of the paraplegic patients is to avoid hyperstimulation and to defer the muscular fatigue as much as possible. In this paper a closed loop position control of the knee joint actuated by the quadriceps muscle to perform flexion-extension has been presented. The feedback control consists of a model predictive control (MPC) technique which is also known by a receding horizon control or moving horizon control. This controller is applied to a complex physio-mathematical muscle model that is based on a macroscopic Hill and a microscopic Huxley concepts. An MPC constitutes an adequate controller with nonlinear multivariable systems. Furthermore it enables us to incorporate explicitly constraints on inputs, outputs and system states. The controller has shown a robustness against force perturbation and model mismatch as well as high capability of tracking a pre-defined reference trajectory

Functional electrical stimulation (FES) is used to excite paralysed muscles that would otherwise be uncontrollable by paraplegic patients. Consequently, the patient could recover partially some of lower limb functions improving the cardiovascular system, increasing oxygen uptake and bettering the whole quality of life. In this paper, we apply a control design based on a higher order sliding mode to a complex physio-mathematical muscle model. This model is based on macroscopic Hill and microscopic Huxley concepts. The main goal concerns the prediction of the needed pattern stimulation (current and pulse width), which will extend the overall performances and defer the muscle fatigue as much as possible. The controller is mathematically computed and shown to provide satisfactory stability and tracking errors. Its efficiency is illustrated with the control of the knee joint angle under a co-contraction approach.