Design and Control of a Knee Exoskeleton for Assistance and Power Augmentation

Thanks to the technological advancements, assistive lower limb exoskeletons are moving from laboratory settings to daily life scenarios. This dissertation makes a contribution toward the development of assistive/power augmentation knee exoskeletons with an improved wearability, ergonomics and intuitive use. In particular, the design and the control of a novel knee exoskeleton system, the iT-Knee Bipedal System, is presented. It is composed by: a novel mechanism to transmit the assistance generated by the exoskeleton to the knee joint in a more ergonomic manner; a novel method that requires limited information to estimate online the torques experienced by the ankles, knees and hips of a person wearing the exoskeleton; a novel sensor system for shoes able to track the feet orientation and monitor their full contact wrench with the ground. In particular, the iT-Knee exoskeleton, the main component of the aforementioned system, is introduced. It is a novel six degree of freedom knee exoskeleton module with under-actuated kinematics, able to assist the flexion/extension motion of the knee while all the other joint\u2019s movements are accommodated. Thanks to its mechanism, the system: solves the problem of the alignment between the joint of the user and the exoskeleton; it automatically adjusts to different users\u2019 size; reduces the undesired forces and torques exchanged between the attachment points of its structure and the user\u2019s skin. From a control point of view, a novel approach to address difficulties arising in real life scenarios (i.e. noncyclic locomotion activity, unexpected terrain or unpredicted interactions with the surroundings) is presented. It is based on a method that estimates online the torques experienced by a person at his ankles, knees and hips with the major advantage that does not rely on any information of the user\u2019s upper body (i.e. pose, weight and center of mass location) or on any interaction of the user\u2019s upper body with the environment (i.e. payload handling or pushing and pulling task). This is achieved v by monitoring the full contact wrench of the subject with the ground and applying an inverse dynamic approach to the lower body segments. To track the full contact wrench between the subject\u2019s feet and the ground, a novel add on system for shoes has been developed. The iT-Shoe is adjustable to different user\u2019s size and accommodates the plantar flexion of the foot. It tracks the interactions and the orientation of the foot thanks to two 6axis Force/Torque sensors, developed in-house, with dedicated embedded MEMS IMUs placed at the toe and heel area. Different tasks and ground conditions were tested to validate and highlight the potentiality of the proposed knee exoskeleton system. The experimental results obtained and the feedback collected confirm the validity of the research conducted toward the design of more ergonomic and intuitive to use exoskeletons

Estimation of ground reaction forces and moments during gait using only inertial motion capture

Ground reaction forces and moments (GRF&M) are important measures used as input in biomechanical analysis to estimate joint kinetics, which often are used to infer information for many musculoskeletal diseases. Their assessment is conventionally achieved using laboratory-based equipment that cannot be applied in daily life monitoring. In this study, we propose a method to predict GRF&M during walking, using exclusively kinematic information from fully-ambulatory inertial motion capture (IMC). From the equations of motion, we derive the total external forces and moments. Then, we solve the indeterminacy problem during double stance using a distribution algorithm based on a smooth transition assumption. The agreement between the IMC-predicted and reference GRF&M was categorized over normal walking speed as excellent for the vertical (ρ = 0.992, rRMSE = 5.3%), anterior (ρ = 0.965, rRMSE = 9.4%) and sagittal (ρ = 0.933, rRMSE = 12.4%) GRF&M components and as strong for the lateral (ρ = 0.862, rRMSE = 13.1%), frontal (ρ = 0.710, rRMSE = 29.6%), and transverse GRF&M (ρ = 0.826, rRMSE = 18.2%). Sensitivity analysis was performed on the effect of the cut-off frequency used in the filtering of the input kinematics, as well as the threshold velocities for the gait event detection algorithm. This study was the first to use only inertial motion capture to estimate 3D GRF&M during gait, providing comparable accuracy with optical motion capture prediction. This approach enables applications that require estimation of the kinetics during walking outside the gait laboratory

Optimal Inertial Sensor Placement and Motion Detection for Epileptic Seizure Patient Monitoring

Use of inertial sensory systems to monitor and detect seizure episodes in patients suffering from epilepsy is investigated via numerical simulations and experiments. Numerical simulations employ a mathematical model that is able to predict human body dynamic responses during a typical epileptic seizure. An optimized inertial sensor placement procedure is developed to address achievement of highest possible sensing resolution in determining angular accelerations with minimal errors. In addition, a joint torque estimation procedure is formulated to assist in the future development of a possible detection scheme. Experimental motion data obtained from an epileptic seizure patient as well as a healthy subject via a cluster of inertial measurement sensors formed a basis for proposing a suitable detection scheme based on non-linear response analysis. In particular, preliminary experimental data analysis has shown that the proposed modified Poincaré Map based scheme can become an effective tool in detecting of seizure via inertial measurements

Nonlinear modeling of FES-supported standing-up in paraplegia for selection of feedback sensors

This paper presents analysis of the standing-up manoeuvre in paraplegia considering the body supportive forces as a potential feedback source in functional electrical stimulation (FES)-assisted standing-up. The analysis investigates the significance of arm, feet, and seat reaction signals to the human body center-of-mass (COM) trajectory reconstruction. The standing-up behavior of eight paraplegic subjects was analyzed, measuring the motion kinematics and reaction forces to provide the data for modeling. Two nonlinear empirical modeling methods are implemented-Gaussian process (GP) priors and multilayer perceptron artificial neural networks (ANN)-and their performance in vertical and horizontal COM component reconstruction is compared. As the input, ten sensory configurations that incorporated different number of sensors were evaluated trading off the modeling performance for variables chosen and ease-of-use in everyday application. For the purpose of evaluation, the root-mean-square difference was calculated between the model output and the kinematics-based COM trajectory. Results show that the force feedback in COM assessment in FES assisted standing-up is comparable alternative to the kinematics measurement systems. It was demonstrated that the GP provided better modeling performance, at higher computational cost. Moreover, on the basis of averaged results, the use of a sensory system incorporating a six-dimensional handle force sensor and an instrumented foot insole is recommended. The configuration is practical for realization and with the GP model achieves an average accuracy of COM estimation 16 /spl plusmn/ 1.8 mm in horizontal and 39 /spl plusmn/ 3.7 mm in vertical direction. Some other configurations analyzed in the study exhibit better modeling accuracy, but are less practical for everyday usage