Knowledge of human-exoskeleton interaction 1 forces is crucial to assess user comfort and effectiveness 2 of the interaction. The subject-exoskeleton collaborative 3 movement and its interaction forces can be predicted in 4 silico using computational modeling techniques. We devel-5 oped an optimal control framework that consisted of three 6 phases. First, the foot-ground (Phase A) and the subject-7 exoskeleton (Phase B) contact models were calibrated 8 using three experimental sit-to-stand trials. Then, the collab-9 orative movement and the subject-exoskeleton interaction 10 forces, of six different sit-to-stand trials were predicted 11 (Phase C). The results show that the contact models were 12 able to reproduce experimental kinematics of calibration 13 trials (mean root mean square differences (RMSD) coor-14 dinates ≤ 1.1°and velocities ≤ 6.8°/s), ground reaction 15 forces (mean RMSD≤ 22.9 N), as well as the interaction 16 forces at the pelvis, thigh, and shank (mean RMSD ≤ 5.4 N). 17 Phase C could predict the collaborative movements of pre-18 diction trials (mean RMSD coordinates ≤ 3.5°and veloc-19 ities ≤ 15.0°/s), and their subject-exoskeleton interaction 20 forces (mean RMSD ≤ 13.1 N). In conclusion, this optimal 21 control framework could be used while designing exoskele-22 tons to have in silico knowledge of new optimal movements 23 and their interaction forces.

Background 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. Methods 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. Results 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. Conclusions 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.

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Real-time detection of multiple stance events, more specifically initial contact (IC), foot flat (FF), heel off (HO), and toe off (TO), could greatly benefit neurorobotic (NR) and neuroprosthetic (NP) control. Three real-time threshold-based algorithms have been developed, detecting the aforementioned events based on kinematic data in combination with a biomechanical model. Data from seven subjects walking at three speeds on an instrumented treadmill were used to validate the presented algorithms, accumulating to a total of 558 steps. The reference for the gait events was obtained using marker and force plate data. All algorithms had excellent precision and no false positives were observed. Timing delays of the presented algorithms were similar to current state-of-the-art algorithms for the detection of IC and TO, whereas smaller delays were achieved for the detection of FF. Our results indicate that, based on their high precision and low delays, these algorithms can be used for the control of an NR/NP, with the exception of the HO event. Kinematic data is used in most NR/NP control schemes and is thus available at no additional cost, resulting in a minimal computational burden. The presented methods can also be applied for screening pathological gait or gait analysis in general in/outside of the laboratory.