Currently, control of exoskeletons in rehabilitation focuses on imposing desired trajectories to promote relearning of motions. Furthermore, assistance is often provided by imposing these desired trajectories using impedance controllers. However, lower-limb exoskeletons are also a promising solution for mobility problems of individuals in daily life. To develop an assistive exoskeleton which allows the user to be autonomous, i.e. in control of his motions, remains a challenge. This paper presents a model-based control method to tackle this challenge. The model-based control method utilizes a dynamic model of the exoskeleton to compensate for its own dynamics. After this compensation of the exoskeleton dynamics, the exoskeleton can provide a desired assistance to the user. While dynamic models of exoskeletons used in the literature focus on gravity compensation only, the need for modelling and monitoring of the ground contact impedes their widespread use. The control strategy proposed here relies on modelling of the full exoskeleton dynamics and of the contacts with the environment. A modelling strategy and general control scheme are introduced. Validation of the control method on 15 non-disabled adults performing sit-to-stand motions shows that muscle effort and joint torques are similar in the conditions with dynamically compensated exoskeleton and without exoskeleton. The condition with exoskeleton in which the compensating controller was not active showed a significant increase in human joint torques and muscle effort at the knee and hip. Motor saturation occurred during the assisted condition, which limited the assistance the exoskeleton could deliver. This work presents the modelling steps and controller design to compensate the exoskeleton dynamics. The validation seems to indicate that the presented model-based controller is able to compensate the exoskeleton.

Force sensing is highly desirable in minimally invasive medical applications, since this feature shows great potential for reducing tissue damage and enhancing manipulation safety. However, embedding force sensors in medical devices is challenging and costly. This article explores the possibility to use shape sensing as a measure to extract force information. In this work, a model-based approach that allows simultaneous shape and force sensing is proposed. Shape information is reconstructed employing a multicore fiber with fiber Bragg grating sensors spaced over the fiber length. This fiber is capable of distributed 3D shape sensing. It is shown how by making use of extended Kalman filter and a mechanics model of the flexible instrument, it becomes possible to estimate both the magnitudes and locations of externally applied forces. Experiments were carried out to validate the proposed method for both one and two external forces applied at arbitrary locations in different directions on a flexible instrument. Results show that one-directional force magnitude and location can be estimated with an average error of 23.08 mN (15.39%) and 11.06 mm (6.51%), respectively. For two-directional forces, results of the load near the base show an average error of 52.01 mN (30.59%) for the magnitude and 29.24 mm (17.20%) for the location. For the load applied simultaneously near the tip, the mean magnitude error is 16.79 mN (11.19%) and the average location error is 10.18 mm (5.99%). The force sensing algorithm can run in real time with an approximate frequency of 59 Hz. In these experiments, it can be observed that the force-sensing accuracy, which depends on the sensitivity of the shape of flexible instruments with respect to the external force, can vary drastically in function of the force application point and force direction.

Enabling persons with functional weaknesses to perform activities of daily living (ADL) is one of the main challenges for the aging society. Powered orthoses, or exoskeletons, have the potential to support ADL while promoting active participation of the user. For this purpose, assistive devices should be designed and controlled to deliver assistance as needed (AAN). This means that the level of assistance should bridge the capability gap, i.e. the gap between the capabilities of the subjects and the task requirements. However, currently the actuators of exoskeletons are mainly designed using inverse dynamics (ID) based calculations of joint moments. The goal of the present study is to calculate the capability gap for the lower limb during ADL when muscle weakness is present, which is needed for appropriate selection of actuators to be integrated in exoskeletons. A musculoskeletal model (MM) is used to calculate the joint kinematics, joint kinetics and muscle forces of eight healthy subjects during ADL (gait, sit-to-stand, stand-to-sit, stair ascent, stair descent). Muscle weakness was imposed to the MM by a stepwise decrease in maximal isometric force imposed to all muscles. Muscle forces were calculated using static optimization. In order to compensate for muscle weakness, ideal moment actuators that represent the motors of an exoskeleton in the simulation were added to deliver AAN required to perform the task. The ID approach overestimates the required assistance since it relies solely on the demands of the task, whereas the AAN approach incorporates the capabilities of the subject. Furthermore, the ID approach delivers continuous support whereas the AAN approach targets the period where a capability gap occurs. The level of muscle weakness for which the external demands imposed by ADL can no longer be met by active muscle force production, is respectively 40%, 70%, 80% and 30%. The present workflow allows estimating the AAN during ADL for different levels of muscle weakness, which can be used in the mechatronic design and control of powered exoskeletons. The AAN approach is a more physiological approach than the ID approach, since the MM accounts for the subject-specific capabilities of the user.