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.

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.

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.

Knowledge of the mass and inertial parameters of a humanoid robot is crucial for the development of model-based controller and motion planning in dynamics situation. Parameters are usually provided from Computer Aided Design (CAD) data and thus inaccurate specially if the robot is modified over time. In this paper, a practical method consisting of hanging a humanoid robot to a fix force sensor to perform its dynamic identification is proposed. This allows, contrary to the literature, to generate very exciting and dynamic motions to identify most of the elements of the inertia tensors in a reduced amount of time. This procedure transforms an instable floating base legged humanoid robot to a safe fix base tree structure robot which makes easier to generate optimal exciting motions. Because of a better excitation the overall trajectory lasts for less than a minute. The method was experimentally validated with a HOAP3 humanoid robot and using a 6-axis force sensor. A reduction of 3 times in average of the RMS difference between measured external reaction forces and moments and their estimates from CAD data was obtained with a single minute of optimal exciting motions.

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.

Knowledge of the mass and inertial parameters of a humanoid robot or a human being is crucial for the development of model-based control, as well as for monitoring the rehabilitation process. These parameters are also important for obtaining realistic simulations in the field of motion planning and human motor control. For robots, they are often provided by computer-aided design data, while averaged anthropometric table values are often used for human subjects. The unit/subject-specific inertial parameters can be identified by using the external wrench caused by the ground reaction. However, the identification accuracy intrinsically depends on the excitation properties of the recorded motion. In this paper, a new method for obtaining optimal excitation motions is proposed. This method is based on the identification model of legged systems and on optimization processes to generate excitation motions while handling mechanical constraints. A pragmatic decomposition of this problem, the use of a new excitation criterion, and a quadratic program to identify inertial parameters are proposed. The method has been experimentally validated onto an HOAP-3 humanoid robot and with one human subject.

In this paper we present a real-time method for identification of the dynamic parameters of a manipulator and its load using kinematic measurements and either joint torques or force and moment at the base. The parameters are estimated using the Extended Kalman Filter and constraints are imposed using Sigmoid functions to ensure the parameters remain within their physically feasible ranges, such as links having positive masses and moments of inertia. Identified parameters can be used in model based controllers. The presented approach is validated through simulation and on data collected with the Barret WAM manipulator. Using the estimated parameters instead of ones provided by the manufacturer greatly improves joint torque prediction.

This paper models recent data in the field of postural coordination showing the existence of self-organized postural states, and transition between them, underlying suprapostural tracking movements. The proposed closed-loop controller captures the complex postural behaviors observed in humans and can be used to implement efficient and simple balance control principles in humanoids.