Subject-exoskeleton contact model calibration leads to accurate interaction force predictions

Knowledge of human–exoskeleton interaction forces is crucial to assess user comfort and effectiveness of the interaction. The subject-exoskeleton collaborative movement and its interaction forces can be predicted in silico using computational modeling techniques. We developed an optimal control framework that consisted of three phases. First, the foot-ground (Phase A) and the subject-exoskeleton (Phase B) contact models were calibrated using three experimental sit-to-stand trials. Then, the collaborative movement and the subject-exoskeleton interaction forces, of six different sit-to-stand trials were predicted (Phase C). The results show that the contact models were able to reproduce experimental kinematics of calibration trials (mean root mean square differences - RMSD - coordinates = 1.1° and velocities = 6.8°/s), ground reaction forces (mean RMSD= 22.9 N), as well as the interaction forces at the pelvis, thigh, and shank (mean RMSD = 5.4 N). Phase C could predict the collaborative movements of prediction trials (mean RMSD coordinates = 3.5° and velocities = 15.0°/s), and their subject-exoskeleton interaction forces (mean RMSD = 13.1° N). In conclusion, this optimal control framework could be used while designing exoskeletons to have in silico knowledge of new optimal movements and their interaction forces.Postprint (author's final draft

Gait Trajectory and Event Prediction from State Estimation for Exoskeletons During Gait

A real-time method is proposed to obtain a single, consistent probabilistic model to predict future joint angles, velocities, accelerations and jerks, together with the timing for the initial contact, foot flat, heel off and toe off events. In a training phase, a probabilistic principal component model is learned from normal walking, which is used in the online phase for state estimation and prediction. This is validated for normal walking and walking with an exoskeleton. Without exoskeleton, both joint trajectories and gait events are predicted without bias. With exoskeleton, the trajectory prediction is unbiased, but event prediction is slightly biased with a maximum of 33 ms for the toe off event. Performance is compared with predictions based on only the population mean. Without exoskeleton, estimation errors are 5 to 30% lower with our method. With exoskeleton, trajectory prediction errors are up to 20% lower, but gait event prediction errors only improve for foot flat (30%) and are worse for other events (30%-50%). The ability to predict future joint trajectories and gait events offers opportunities to design exoskeleton controllers which anticipate these trajectories and events, allowing better tracking control and smoother, accurately timed transitions between different control modes.status: publishe

Real-Time Gait Event Detection Based on Kinematic Data Coupled to a Biomechanical Model †

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

Real-Time Gait Event Detection Based on Kinematic Data Coupled to a Biomechanical Model

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 a NR/NP, with exception of the HO event. Kinematic data is used in most NR/NP control schemes and thus available at no additional cost, resulting in a minimal computational burden. The presented methods can also be applied for screening pathological gait or in general gait analysis in/outside of the laboratory.status: publishe

A probabilistic method to estimate gait kinetics in the absence of ground reaction force measurements.

Human joint torques during gait are usually computed using inverse dynamics. This method requires a skeletal model, kinematics and measured ground reaction forces and moments (GRFM). Measuring GRFM is however only possible in a controlled environment. This paper introduces a probabilistic method based on probabilistic principal component analysis to estimate the joint torques for healthy gait without measured GRFM. A gait dataset of 23 subjects was obtained containing kinematics, measured GRFM and joint torques from inverse dynamics in order to obtain a probabilistic model. This model was then used to estimate the joint torques of other subjects without measured GRFM. Only kinematics, a skeletal model and timing of gait events are needed. Estimation only takes 0.28 ms per time instant. Using cross-validation, the resulting root mean square estimation errors for the lower-limb joint torques are found to be approximately 0.1 Nm/kg, which is 6-18% of the range of the ground truth joint torques. Estimated joint torque and GRFM errors are up to two times smaller than model-based state-of-the-art methods. Model-free artificial neural networks can achieve lower errors than our method, but are less repeatable, do not contain uncertainty information on the estimates and are difficult to use in situations which are not in the learning set. In contrast, our method performs well in a new situation where the walking speed is higher than in the learning dataset. The method can for example be used to estimate the kinetics during overground walking without force plates, during treadmill walking without (separate) force plates and during ambulatory measurements.status: Published onlin

Realtime Delayless Estimation of Derivatives of Noisy Sensor Signals for Quasi-cyclic Motions with Application to Joint Acceleration Estimation on an Exoskeleton

The control of mechatronic systems can often be enhanced if realtime information on the derivatives of a signal is available. These derivatives are not always measurable by sensors and should be estimated. Simple numerical derivatives cannot be applied, due to noise on the measured signals. Several researchers managed to reduce the noise and calculate the derivative but as a drawback the estimation has a time delay. In this paper, we focus on the realtime derivative estimation of quasi-cyclic signals. Cycles of these signals are very similar but not exactly alike. At each time instant, the derivatives of the previous cycle are fed to a linear state estimator as virtual measurements. This allows to have a delay-free estimation. The proposed method is tested experimentally on a human walking in an exoskeleton with rotary joint encoders. Results show that it is possible to estimate the angular acceleration of hip, knee and ankle joint in realtime without delay. The algorithm is compared with the technique of adaptive oscillators with non-linear filter, used in literature for a similar application. Our method estimates acceleration better both in steady-state and transient periods.status: publishe

Predicting Seat-Off and Detecting Start-of-Assistance Events for Assisting Sit-to-Stand with an Exoskeleton

Accurate and reliable event prediction is imperative for supporting movement with an exoskeleton. Two events are important during a sit-to-stand movement: seat-off, the event at which the subject leaves the chair and start-of-assistance for hip and knee, the earliest time at which assistance may be provided. This paper analyzes two methods to predict and detect these events. Both methods only have joint encoder data as input. The model-based method uses probabilistic principle component analysis with a Kalman filter. Based on a statistically learned model, a joint trajectory is predicted. The seat-off event is predicted using its correlation with maximum hip angle. Since the start-of-assistance event has no clear correlation with joint trajectories, it cannot be detected with this method. The model-free method is a feed-forward neural network which learns a mapping between inputs and events directly. It is applied to both seat-off prediction and start-of-assistance detection. Methods have been evaluated on 311 lab-recorded movements. For the seat-off event, the model-based method is more reliable than the model-free method. For the start-of-assistance event, the model-free method performs well, except in an outlier case for one subject. Both of these methods allow accurate and reliable event prediction, only using joint encoder data as inputs.status: publishe

Hit-to-Lead Optimization of a Novel Class of Potent, Broad-Spectrum Trypanosomacides

The parasitic trypanosomes Trypanosoma brucei and T. cruzi are responsible for significant human suffering in the form of human African trypanosomiasis (HAT) and Chagas disease. Drugs currently available to treat these neglected diseases leave much to be desired. Herein we report optimization of a novel class of N-(2-(2-phenylthiazol-4-yl)ethyl)amides, carbamates, and ureas, which rapidly, selectively, and potently kill both species of trypanosome. The mode of action of these compounds is unknown but does not involve CYP51 inhibition. They do, however, exhibit clear structure-activity relationships, consistent across both trypanosome species. Favorable physicochemical parameters place the best compounds in CNS drug-like chemical space but, as a class, they exhibit poor metabolic stability. One of the best compounds (64a) cleared all signs of T. cruzi infection in mice when CYP metabolism was inhibited, with sterile cure achieved in one mouse. This family of compounds thus shows significant promise for trypanosomiasis drug discovery