Patient Movement Monitoring Based on IMU and Deep Learning

Osteoarthritis (OA) is the leading cause of disability among the aging population in the United States and is frequently treated by replacing deteriorated joints with metal and plastic components. Developing better quantitative measures of movement quality to track patients longitudinally in their own homes would enable personalized treatment plans and hasten the advancement of promising new interventions. Wearable sensors and machine learning used to quantify patient movement could revolutionize the diagnosis and treatment of movement disorders. The purpose of this dissertation was to overcome technical challenges associated with the use of wearable sensors, specifically Inertial Measurement Units (IMUs), as a diagnostic tool for osteoarthritic (OA) and total knee replacement patients (TKR) through a detailed biomechanical assessment and development of machine learning algorithms. Specifically, the first study developed a relevant dataset consisting of IMU and associated biomechanical parameters of OA and TKR patients performing various activities, created a machine learning-based framework to accurately estimate spatiotemporal movement characteristics from IMU during level ground walking, and defined optimum sensor configuration associated with the patient population and activity. The second study designed a framework to generate synthetic kinematic and associated IMU data as well as investigated the influence of adding synthetic data into training-measured data on deep learning model performance. The third study investigated the kinematic variation between two patient’s population across various activities: stair ascent, stair descent, and gait using principle component analysis PCA. Additionally, PCA-based autoencoders were developed to generate synthetic kinematics data for each patient population and activity. The fourth study investigated the potential use of a universal deep learning model for the estimation of lower extremities’ kinematics across various activities. Therefore, this model can be used as a global model for transfer learning methods in future research. This line of study resulted in a machine-learning framework that can be used to estimate biomechanical movements based on a stream of signals emitted from low-cost and portable IMUs. Eventually, this could lead to a simple clinical tool for tracking patients\u27 movements in their own homes and translating those movements into diagnostic metrics that clinicians will be able to use to tailor treatment to each patient\u27s needs in the future

Elbow Joint Contact Mechanics: Multibody and Finite Element Methods

Title from PDF of title page viewed June 6, 2017Thesis advisor: Antonis P. StylianouVitaIncludes bibliographical references (pages 49-53)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2017Only a few millimeter thick articular cartilage is a very specialized connective tissue which withstands high compressive and shear forces while protecting the bone from excessive loading, and provides a smooth articulation for the joint. Better understanding of elbow cartilage contact mechanics can provide a valuable insight into cartilage degeneration mechanisms and osteoarthritis development. Computational modeling is a very efficient tool that helps us gain better understanding of joint biomechanics, particularly elbow joint contact mechanics. This tool can predict parameters that are not feasible to measure experimentally, decrease the cost of physical experiment, help develop better rehabilitation and surgical protocols, and finally improve patient care. The objectives of the study presented here were first, to develop subject specific finite element (FE) models of the isolated ulno-humeral joint of the elbow and validate these models against experiment measurements. Second, to develop multibody (MB) models of the same joints with the humerus cartilage represented with discrete rigid bodies interacting with the ulna cartilage with deformable contacts. Third, to optimize the deformable contact parameters used in the MB models to validated FE models and assess the effect of grid sizes on the contact predictions. These models allow for the prediction of cartilage contact characteristics including maximum and average contact pressure (MPa), and contact area (mm2) under different loading conditions and during activities in the anatomic elbow joint. Finally, the results from optimization indicated that the selection of contact parameters is very critical for accurate prediction of contact mechanics within the MB models of ulno humeral joints.Introduction -- Ulna-humerus contact mechanics: finite element analysis and experimental measurements using a tactile pressure sensor -- Calibrating multibody ulno-humeral joint cartilage using a validated finite element model -- Conclusio

BioMAT (Biomechanics Multiactivity Transformer) Dataset

https://digitalcommons.du.edu/biomat/1000/thumbnail.jp

BioMAT (Biomechanics Multiactivity Transformer) Model

https://digitalcommons.du.edu/biomat/1001/thumbnail.jp

Musculoskeletal Model Development of the Elbow Joint with an Experimental Evaluation

A dynamic musculoskeletal model of the elbow joint in which muscle, ligament, and articular surface contact forces are predicted concurrently would be an ideal tool for patient-specific preoperative planning, computer-aided surgery, and rehabilitation. Existing musculoskeletal elbow joint models have limited clinical applicability because of idealizing the elbow as a mechanical hinge joint or ignoring important soft tissue (e.g., cartilage) contributions. The purpose of this study was to develop a subject-specific anatomically correct musculoskeletal elbow joint model and evaluate it based on experimental kinematics and muscle electromyography measurements. The model included three-dimensional bone geometries, a joint constrained by multiple ligament bundles, deformable contacts, and the natural oblique wrapping of ligaments. The musculoskeletal model predicted the bone kinematics reasonably accurately in three different velocity conditions. The model predicted timing and number of muscle excitations, and the normalized muscle forces were also in agreement with the experiment. The model was able to predict important in vivo parameters that are not possible to measure experimentally, such as muscle and ligament forces, and cartilage contact pressure. In addition, the developed musculoskeletal model was computationally efficient for body-level dynamic simulation. The maximum computation time was less than 30 min for our 35 s simulation. As a predictive clinical tool, the potential medical applications for this model and modeling approach are significant

The Use of Synthetic IMU Signals in the Training of Deep Learning Models Significantly Improves the Accuracy of Joint Kinematic Predictions

Gait analysis based on inertial sensors has become an effective method of quantifying movement mechanics, such as joint kinematics and kinetics. Machine learning techniques are used to reliably predict joint mechanics directly from streams of IMU signals for various activities. These data-driven models require comprehensive and representative training datasets to be generalizable across the movement variability seen in the population at large. Bottlenecks in model development frequently occur due to the lack of sufficient training data and the significant time and resources necessary to acquire these datasets. Reliable methods to generate synthetic biomechanical training data could streamline model development and potentially improve model performance. In this study, we developed a methodology to generate synthetic kinematics and the associated predicted IMU signals using open source musculoskeletal modeling software. These synthetic data were used to train neural networks to predict three degree-of-freedom joint rotations at the hip and knee during gait either in lieu of or along with previously measured experimental gait data. The accuracy of the models’ kinematic predictions was assessed using experimentally measured IMU signals and gait kinematics. Models trained using the synthetic data out-performed models using only the experimental data in five of the six rotational degrees of freedom at the hip and knee. On average, root mean square errors in joint angle predictions were improved by 38% at the hip (synthetic data RMSE: 2.3°, measured data RMSE: 4.5°) and 11% at the knee (synthetic data RMSE: 2.9°, measured data RMSE: 3.3°), when models trained solely on synthetic data were compared to measured data. When models were trained on both measured and synthetic data, root mean square errors were reduced by 54% at the hip (measured + synthetic data RMSE: 1.9°) and 45% at the knee (measured + synthetic data RMSE: 1.7°), compared to measured data alone. These findings enable future model development for different activities of clinical significance without the burden of generating large quantities of gait lab data for model training, streamlining model development, and ultimately improving model performance