Inertial sensor based full body 3D kinematics in the differential diagnosis between Parkinson’s Disease and mimics

The differential diagnosis of Parkinson’s Disease (PD) remains challenging with frequent mis and underdiagnosis. DAT-Scan has been a useful technique for assessing the lost integrity of the nigrostriatal pathway in PD and differentiating true parkinsonism from mimics. However, DAT-Scan remains unavailable in most non-specialized clinical centres, making imperative the search for other easy and low-cost solutions. This dissertation aimed to investigate the role of inertial sensors in distinguishing between the denervated and the non-denervated individuals. In this dissertation, we've used Inertial Sensor Based 3D Full Body Kinematics (FBK) and tested if this technique was able to distinguish between patients with changes in the DAT-Scan from those without. This was divided into two parts, being that firstly, a group of individuals was referred by the attending physician for DAT-Scan (123I-FP-CIT SPECT) to be able to compare FBK in those with and without evidence of dopaminergic depletion. Second, it was tested whether FBK could be used as a metric for the severity of dopaminergic depletion. Twenty-one patients participated in this study, being recruited from the Nuclear Medicine Unit in the Champalimaud Clinical Centre (CCC), Lisbon. Within these 21 patients, 10 of them had denervation (mean age, 68.4 ± 7.8 years) and the remaining 11 (mean age, 66.6 ± 7.4 years) did not present denervation. The analysis between the worst uptake ratio features and dimensional features, as well as the asymmetry indexes in the striatum revealed significant differences between denervated and non-denervated individuals. On the contrary, the kinematics did not do it. Overall, based on the collected kinematics data, it was identified that there was not any significant correlation between the kinematics and the DAT-Scan. What means that these kinematics variables were not able to explain the DAT-Scan. On the other hand, it was also checked that the kinematics data were strongly correlated to the motor symptoms (MDS-UPDRS III). This way, it was concluded that the classical biomechanics did not distinguish denervated from non-denervated individuals. Therefore, the kinematics could not give the same answer as the DAT-Scan. In spite of these results it would be relevant to keep researching other methods in order to find out the distinction between the denervation and no denervation in a low-cost way

ESTIMATION AND PREDICTION OF THE HUMAN GAIT DYNAMICS FOR THE CONTROL OF AN ANKLE-FOOT PROSTHESIS

With the growing population of amputees, powered prostheses can be a solution to improve the quality of life for many people. Powered ankle-foot prostheses can be made to behave similar to the lost limb via controllers that emulate the mechanical impedance of the human ankle. Therefore, the understanding of human ankle dynamics is of major significance. First, this work reports the modulation of the mechanical impedance via two mechanisms: the co-contraction of the calf muscles and a change of mean ankle torque and angle. Then, the mechanical impedance of the ankle was determined, for the first time, as a multivariable and time-varying system. These findings reveal the importance of recognizing the state of the user during the gait when the user interacts with the environment. In addition to studying the ankle impedance, a wearable device was designed and evaluated to further the studies on robotic perception for ankle-foot prostheses. This device is capable of characterizing the ground environment and estimating the gait state using visual-inertial sensors. Finally, this study contributes to the field of ankle-foot prostheses by identifying the mechanical behavior of the human ankle and developing a platform to test perception algorithms for the control of robotic prostheses

Validity of inertial sensor based 3D joint kinematics of static and dynamic sport and physiotherapy specific movements

3D joint kinematics can provide important information about the quality of movements. Optical motion capture systems (OMC) are considered the gold standard in motion analysis. However, in recent years, inertial measurement units (IMU) have become a promising alternative. The aim of this study was to validate IMU-based 3D joint kinematics of the lower extremities during different movements. Twenty-eight healthy subjects participated in this study. They performed bilateral squats (SQ), single-leg squats (SLS) and countermovement jumps (CMJ). The IMU kinematics was calculated using a recently-described sensor-fusion algorithm. A marker based OMC system served as a reference. Only the technical error based on algorithm performance was considered, incorporating OMC data for the calibration, initialization, and a biomechanical model. To evaluate the validity of IMU-based 3D joint kinematics, root mean squared error (RMSE), range of motion error (ROME), Bland-Altman (BA) analysis as well as the coefficient of multiple correlation (CMC) were calculated. The evaluation was twofold. First, the IMU data was compared to OMC data based on marker clusters; and, second based on skin markers attached to anatomical landmarks. The first evaluation revealed means for RMSE and ROME for all joints and tasks below 3°. The more dynamic task, CMJ, revealed error measures approximately 1° higher than the remaining tasks. Mean CMC values ranged from 0.77 to 1 over all joint angles and all tasks. The second evaluation showed an increase in the RMSE of 2.28°– 2.58° on average for all joints and tasks. Hip flexion revealed the highest average RMSE in all tasks (4.87°– 8.27°). The present study revealed a valid IMU-based approach for the measurement of 3D joint kinematics in functional movements of varying demands. The high validity of the results encourages further development and the extension of the present approach into clinical settings

CNN-Based Estimation of Sagittal Plane Walking and Running Biomechanics From Measured and Simulated Inertial Sensor Data

Machine learning is a promising approach to evaluate human movement based on wearable sensor data. A representative dataset for training data-driven models is crucial to ensure that the model generalizes well to unseen data. However, the acquisition of sufficient data is time-consuming and often infeasible. We present a method to create realistic inertial sensor data with corresponding biomechanical variables by 2D walking and running simulations. We augmented a measured inertial sensor dataset with simulated data for the training of convolutional neural networks to estimate sagittal plane joint angles, joint moments, and ground reaction forces (GRFs) of walking and running. When adding simulated data, the root mean square error (RMSE) of the test set of hip, knee, and ankle joint angles decreased up to 17 %, 27 % and 23 %, the RMSE of knee and ankle joint moments up to 6 % and the RMSE of anterior-posterior and vertical GRF up to 2 and 6 %. Simulation-aided estimation of joint moments and GRFs was limited by inaccuracies of the biomechanical model. Improving the physics-based model and domain adaptation learning may further increase the benefit of simulated data. Future work can exploit biomechanical simulations to connect different data sources in order to create representative datasets of human movement. In conclusion, machine learning can benefit from available domain knowledge on biomechanical simulations to supplement cumbersome data collections

ANTHROPOMORPHIC ROBOTIC ANKLE-FOOT PROSTHESIS WITH ACTIVE DORSIFLEXION- PLANTARFLEXION AND INVERSION-EVERSION

The main goal of the research presented in this paper is the development of a powered ankle-foot prosthesis with anthropomorphic characteristics to facilitate turning, walking on irregular grounds, and reducing secondary injuries on bellow knee amputees. The research includes the study of the gait in unimpaired human subjects that includes the kinetics and kinematics of the ankle during different types of gait, in different gait speeds at different turning maneuvers. The development of a robotic ankle-foot prosthesis with two active degrees of freedom (DOF) controlled using admittance and impedance controllers is presented. Also, a novel testing apparatus for estimation of the ankle mechanical impedance in two DOF is presented. The testing apparatus allows the estimation of the time-varying impedance of the human ankle in stance phase during walking in arbitrary directions. The presented work gives insight on the turning mechanisms of the human ankle and how they can be mimicked by the prosthesis to improve the gait and agility of below-knee amputees

Stability of Mina v2 for Robot-Assisted Balance and Locomotion

The assessment of the risk of falling during robot-assisted locomotion is critical for gait control and operator safety, but has not yet been addressed through a systematic and quantitative approach. In this study, the balance stability of Mina v2, a recently developed powered lower-limb robotic exoskeleton, is evaluated using an algorithmic framework based on center of mass (COM)- and joint-space dynamics. The equivalent mechanical model of the combined human-exoskeleton system in the sagittal plane is established and used for balance stability analysis. The properties of the Linear Linkage Actuator, which is custom-designed for Mina v2, are analyzed to obtain mathematical models of torque-velocity limits, and are implemented as constraint functions in the optimization formulation. For given feet configurations of the robotic exoskeleton during flat ground walking, the algorithm evaluates the maximum allowable COM velocity perturbations along the fore-aft directions at each COM position of the system. The resulting velocity extrema form the contact-specific balance stability boundaries (BSBs) of the combined system in the COM state space, which represent the thresholds between balanced and unbalanced states for given contact configurations. The BSBs are obtained for the operation of Mina v2 without crutches, thus quantifying Mina v2's capability of maintaining balance through the support of the leg(s). Stability boundaries in single and double leg supports are used to analyze the robot's stability performance during flat ground walking experiments, and provide design and control implications for future development of crutch-less robotic exoskeletons

Control of Bio-Inspired Sprawling Posture Quadruped Robots with an Actuated Spine

Sprawling posture robots are characterized by upper limb segments protruding horizontally from the body, resulting in lower body height and wider support on the ground. Combined with an actuated segmented spine and tail, such morphology resembles that of salamanders or crocodiles. Although bio-inspired salamander-like robots with simple rotational limbs have been created, not much research has been done on kinematically redundant bio-mimetic robots that can closely replicate kinematics of sprawling animal gaits. Being bio-mimetic could allow a robot to have some of the locomotion skills observed in those animals, expanding its potential applications in challenging scenarios. At the same time, the robot could be used to answer questions about the animal's locomotion. This thesis is focused on developing locomotion controllers for such robots. Due to their high number of degrees of freedom (DoF), the control is based on solving the limb and spine inverse kinematics to properly coordinate different body parts. It is demonstrated how active use of a spine improves the robot's walking and turning performance. Further performance improvement across a variety of gaits is achieved by using model predictive control (MPC) methods to dictate the motion of the robot's center of mass (CoM). The locomotion controller is reused on an another robot (OroBOT) with similar morphology, designed to mimic the kinematics of a fossil belonging to Orobates, an extinct early tetrapod. Being capable of generating different gaits and quantitatively measuring their characteristics, OroBOT was used to find the most probable way the animal moved. This is useful because understanding locomotion of extinct vertebrates helps to conceptualize major transitions in their evolution. To tackle field applications, e.g. in disaster response missions, a new generation of field-oriented sprawling posture robots was built. The robustness of their initial crocodile-inspired design was tested in the animal's natural habitat (Uganda, Africa) and subsequently enhanced with additional sensors, cameras and computer. The improvements to the software framework involved a smartphone user interface visualizing the robot's state and camera feed to improve the ease of use for the operator. Using force sensors, the locomotion controller is expanded with a set of reflex control modules. It is demonstrated how these modules improve the robot's performance on rough and unstructured terrain. The robot's design and its low profile allow it to traverse low passages. To also tackle narrow passages like pipes, an unconventional crawling gait is explored. While using it, the robot lies on the ground and pushes against the pipe walls to move the body. To achieve such a task, several new control and estimation modules were developed. By exploring these problems, this thesis illustrates fruitful interactions that can take place between robotics, biology and paleontology

The inertial properties of the German Shepherd

Previously held under moratorium from 30th November 2016 until 30th November 2021The police service dog has a long history stretching as far back as the 1400’s. One of the most popular dog breeds deployed by both the police and military has been the German Shepherd yet little is known about the morphology or body segment parameters of this breed. Knowledge of these measures is essential for developing biomechanical models that can guide clinicians in developing surgical interventions, injury treatment and prevention procedures. The aim of this thesis was to provide a complete set of body segment parameters and inertial properties for the German Shepherd. In addition, a canine motion capture suit and marker model was proposed for use with this dog population. Morphometric measures and 3-dimensional inertial properties, including mass, centre of mass, moment of inertia and volume, were measured from 17 segments from each of 6 German Shepherd police service dog cadavers. Measurements were performed with frozen segments similar to the procedure on primates described by Reynolds (1974), on humans by Chandler et al. (1975) and on horses by Buchner et al. (1997). Using whole body mass and geometric modelling, multiple linear regression equations were developed from the collected data so that they may be used to estimate segment masses and inertial tensors in living dogs. Using a custom Lycra suit and 44-marker full-body marker set, kinematic data were collected to assess the practicality of the model, to observe the dogs’ acceptance of the motion capture suit and to ensure fore and hind limb flexion/extension angles were comparable to those of other canine studies. Using frozen cadavers, tissue loss was minimal at an average loss of 0.49% of total body mass. Hind limbs, at 6.8% of body mass, were 2.3% heavier than the forelimbs. Of the over 100 morphometric measures analysed, 33 were kept for inclusion in the linear regression equations and joint centre estimations. Analyses of body mass alone, found that, except for the abdominal segment (r = .845, p≤.05), body mass did not correlate well with segmental masses. Similarly for moments of inertia, only the manus and pes produced predictive results using body mass alone. 11 regression equations were developed for predicting segment masses, and 33 equations were developed for predicting moments of inertia about the three primary axes of each segment. Regression correlation analyses were summarized for each segment and a table of normalised average segment masses, centres of mass, radii of gyration and segment densities was produced. Five police service dogs took part in the evaluation of the motion capture suit. Overall the marker set and suit performed well and was well-received by dog/handler teams. The markers took very little time to apply, remained in place for the majority of trials and the suit itself did not visibly affect the dog’s natural movement. An analysis of the kinematic data produced outputs showing characteristic patterns of flexion/extension similar to those found in other canine research. With the development of regression equations for predicting segment mass and moments of inertia combined with the proposed marker model and novel method of marker attachment, inverse dynamic analyses may be applied in future investigations of canine mechanics, potentially guiding surgical procedures, rehabilitation and training for the German Shepherd breed. Key Words: Canine, German Shepherd, morphometry, kinematics, kinetics, inertial properties, body segment parameter, segment model, moment of inertia, mass distribution.The police service dog has a long history stretching as far back as the 1400’s. One of the most popular dog breeds deployed by both the police and military has been the German Shepherd yet little is known about the morphology or body segment parameters of this breed. Knowledge of these measures is essential for developing biomechanical models that can guide clinicians in developing surgical interventions, injury treatment and prevention procedures. The aim of this thesis was to provide a complete set of body segment parameters and inertial properties for the German Shepherd. In addition, a canine motion capture suit and marker model was proposed for use with this dog population. Morphometric measures and 3-dimensional inertial properties, including mass, centre of mass, moment of inertia and volume, were measured from 17 segments from each of 6 German Shepherd police service dog cadavers. Measurements were performed with frozen segments similar to the procedure on primates described by Reynolds (1974), on humans by Chandler et al. (1975) and on horses by Buchner et al. (1997). Using whole body mass and geometric modelling, multiple linear regression equations were developed from the collected data so that they may be used to estimate segment masses and inertial tensors in living dogs. Using a custom Lycra suit and 44-marker full-body marker set, kinematic data were collected to assess the practicality of the model, to observe the dogs’ acceptance of the motion capture suit and to ensure fore and hind limb flexion/extension angles were comparable to those of other canine studies. Using frozen cadavers, tissue loss was minimal at an average loss of 0.49% of total body mass. Hind limbs, at 6.8% of body mass, were 2.3% heavier than the forelimbs. Of the over 100 morphometric measures analysed, 33 were kept for inclusion in the linear regression equations and joint centre estimations. Analyses of body mass alone, found that, except for the abdominal segment (r = .845, p≤.05), body mass did not correlate well with segmental masses. Similarly for moments of inertia, only the manus and pes produced predictive results using body mass alone. 11 regression equations were developed for predicting segment masses, and 33 equations were developed for predicting moments of inertia about the three primary axes of each segment. Regression correlation analyses were summarized for each segment and a table of normalised average segment masses, centres of mass, radii of gyration and segment densities was produced. Five police service dogs took part in the evaluation of the motion capture suit. Overall the marker set and suit performed well and was well-received by dog/handler teams. The markers took very little time to apply, remained in place for the majority of trials and the suit itself did not visibly affect the dog’s natural movement. An analysis of the kinematic data produced outputs showing characteristic patterns of flexion/extension similar to those found in other canine research. With the development of regression equations for predicting segment mass and moments of inertia combined with the proposed marker model and novel method of marker attachment, inverse dynamic analyses may be applied in future investigations of canine mechanics, potentially guiding surgical procedures, rehabilitation and training for the German Shepherd breed. Key Words: Canine, German Shepherd, morphometry, kinematics, kinetics, inertial properties, body segment parameter, segment model, moment of inertia, mass distribution

Multibody dynamics model of a full human body for simulating walking

Indiana University-Purdue University Indianapolis (IUPUI)Khakpour, Zahra M.S.M.E., Purdue University, May 2017. Multibody Dynamics Model of A Full Human Body For Simulating Walking, Major Professor: Hazim El-Mounayri. Bipedal robotics is a relatively new research area which is concerned with creating walking robots which have mobility and agility characteristics approaching those of humans. Also, in general, simulation of bipedal walking is important in many other applications such as: design and testing of orthopedic implants; testing human walking rehabilitation strategies and devices; design of equipment and facilities for human/robot use/interaction; design of sports equipment; and improving sports performance & reducing injury. One of the main technical challenges in that bipedal robotics area is developing a walking control strategy which results in a stable and balanced upright walking gait of the robot on level as well as non-level (sloped/rough) terrains. In this thesis the following aspects of the walking control strategy are developed and tested in a high-fidelity multibody dynamics model of a humanoid body model: 1. Kinematic design of a walking gait using cubic Hermite splines to specify the motion of the center of the foot. 2. Inverse kinematics to compute the legs joint angles necessary to generate the walking gait. 3. Inverse dynamics using rotary actuators at the joints with PD (Proportional-Derivative) controllers to control the motion of the leg links. The thee-dimensional multibody dynamics model is built using the DIS (Dynamic Interactions Simulator) code. It consists of 42 rigid bodies representing the legs, hip, spine, ribs, neck, arms, and head. The bodies are connected using 42 revolute joints with a rotational actuator along with a PD controller at each joint. A penalty normal contact force model along with a polygonal contact surface representing the bottom of each foot is used to model contact between the foot and the terrain. Friction is modeled using an asperity-based friction model which approximates Coulomb friction using a variable anchor-point spring in parallel with a velocity dependent friction law. In this thesis, it is assumed in the model that a balance controller already exists to ensure that the walking motion is balanced (i.e. that the robot does not tip over). A multi-body dynamic model of the full human body is developed and the controllers are designed to simulate the walking motion. This includes the design of the geometric model, development of the control system in kinematics approach, and the simulation setup