SBIR-STTR Award

A major challenge facing rehabilitation research is to measure relationships between impairments, functional limitations, and disabilities. Biomechanical analyses are a key tool for establishing these relationships by providing quantitative objective measures of patient status and treatment outcomes. At the heart of many biomechanical analyses is estimation of the pose (position and orientation) of a multi-segment model based on recording of 3D motion data using sensors (optical, electro-magnetic, or inertial). Visual3D, the most advanced clinical biomechanics analysis software available commercially for 3D motion capture data, contains solutions for the estimation of pose from 3D sensor data that have been tested in laboratories throughout the world, and are used on a daily basis for clinical assessment. Researchers have come to rely on Visual3D's capabilities. C-Motion is proposing a collaborative research and development effort to get new pose estimation techniques into the hands of researchers. The algorithms from Phase I and the enhancements in Phase II will be included in Visual3D. At the core of Visual3D's functionality are flexible algorithms for identifying a mapping from 3D motion capture sensors to the 3D pose of a segmented skeletal model. The principle assumption of the Visual3D pose estimation algorithms (and other commercial biomechanics software) is that sensors move rigidly with the body segments to which they are attached. It is accepted, however, that sensors attached to the skin move relative to the underlying skeleton and that this Soft Tissue Artifact is challenging to quantify or model because it is often systematic but varies on a case by case basis. This artifact is a serious challenge to the relevance of non-invasive clinical motion analyses. The current pose estimation algorithms were not designed to incorporate models of soft tissue artifact. Uncertainty in data (e.g. sensor noise and artifact) cannot be addressed directly using current discriminative methods, but may be addressed by casting the Pose Estimation problem in the general framework of probabilistic inference (Todorov, 2007). In this framework, the pose and any prior knowledge about the pose are encoded probabilistically, and the ""artifacts and noise"" are captured by a generative model, which defines the conditional probability of the data given the pose. In Phase I we will implement and test a kinematics-based probabilistic algorithm for computing the pose (position and orientation) of a subject using Bayesian inference as proposed by Dr. Todorov. The results will be compared to a set of biplanar cinefluoroscopy data and 3D motion capture data recorded simultaneously by our collaborator Dr. Scott Tashman (Biodynamics Laboratory at the University of Pittsburgh), which we will treat as our ""gold standard"" for bone motion. The overall project is very ambitious, so in Phase I we are attempting an important subset of the overall algorithm to demonstrate feasibility of this approach, and to provide evidence that we are capable of tackling the even more ambitious Phase II project.

Public Health Relevance:
There is a tremendous need for improved rehabilitation research and clinical services to lower individual health care costs and improve productivity and quality of life. Biomechanical analysis is a key tool for understanding the relationships between impairments, functional limitations, and disabilities by providing quantitative, objective measures of patient status and treatment outcomes. This project is designed to apply probabilistic algorithms developed in the field of machine vision to make a new generation of biomechanical techniques available commercially, which will enable researchers to improve movement analysis dramatically and ultimately patient outcomes.

Thesaurus Terms:
Address;Algorithms;Applications Grants;Artifacts;Au Element;Biomechanics;Clinical;Clinical Services;Clinical Assessments;Computer Software;Data;Data Set;Dataset;Development;Development And Research;Effectiveness;Electromagnetic;Electromagnetics;Estimation Techniques;Fluoroscopy;Functional Impairment;Generations;Gold;Grant;Grant Proposals;Hand;Health Care Costs;Health Costs;Healthcare Costs;Heart;Individual;Investigators;Kinetics;Knowledge;Loinc Axis 4 System;Laboratories;Link;Magnetism;Maps;Measures;Methods;Modeling;Morphologic Artifacts;Motion;Movement;Nih;National Institutes Of Health;Noise;Optics;Outcome;Patients;Phase;Position;Positioning Attribute;Probability;Process;Productivity;Publishing;Qol;Quality Of Life;R &D;R&D;Rehabilitation Research;Relative;Relative (Related Person);Research Personnel;Researchers;Sight;Skeleton;Skin;Software;Solutions;Stream;System;Techniques;Technology;Testing;Time;Treatment Outcome;Uncertainty;United States National Institutes Of Health;Universities;Vision;Base;Biomechanical;Body Movement;Bone;Case-By-Case Basis;Computer Program/Software;Data Management;Design;Designing;Developmental;Disability;Doubt;Flexibility;Flexible;Functional Disability;Improved;Joint Mobilization;Joint Movement;Kinematics;Magnetic;Optical;Optical Sensor;Research And Development;Sensor;Simulation;Skeletal;Soft Tissue;Tool;Visual Function

Orthopaedic disorders are a leading cause of disability in the U.S., with arthritis and/or spine problems adversely affecting quality of life fo more than 20% of adults. With an aging population the rate of disability from orthopaedic disorders has been increasing steadily. While advances in diagnostic imaging (including CT, MRI and ultrasound) have greatly improved our ability to detect structural changes in musculoskeletal tissues, they typically reveal little about joint function. There is evidence that abnormal mechanical joint function contributes significantly to the development and progression of many types of joint disease. There is, therefore, a significant clinical need for the widespread use of technologies that can identify subtle abnormalities in joint function that, if left untreate, can compromise long-term joint health. Biomechanical analyses are a key tool for providing quantitative objective measures of patient status and treatment outcomes. At the heart of most in vivo biomechanical analyses is the estimation of the position and orientation (Pose) of a multi-segment rigid body model based on recordings of 3D motion sensor data. The principal assumption of existing Pose estimation algorithms is that the motion sensors move rigidly along with the body segments to which they are attached; it is known, however, that this assumption is an approximation and that the sensors in reality move relative to the underlying skeleton. This project is designed to apply algorithms, based on Bayesian Inference, which have the potential for mitigating soft tissue artifacts and dramatically improving the spatial resolution of 3D movement analysis. To address this soft-tissue problem we redefined Pose estimation using the general framework of probabilistic (Bayesian) inference. In Phase I we developed a general Bayesian Prior based on soft tissue motion that produced substantially lower errors than all generative methods. In Phase II a new Bayesian Priors will be implemented to mitigate soft tissue artifact based on DSX data of the knee and ankle for a set of control subjects. In Phase II we will test this Probabilistic Inference approach against an independent set normal subjects during walking and running, against a set of subjects with Cerebral Palsy during walking, and against a set of subjects with Anterior Collateral Ligament (ACL) injuries during walking and running. The improvement in spatial resolution demonstrated in Phase I and the enhancements proposed for Phase II will enable non-invasive Motion Capture to achieve sufficiently high spatial accuracy to describe the dynamic functioning of joints and ligaments, which will lead to an experimental and analytical tool suitable for studying joint disease and disorders.

Public Health Relevance Statement:


Public Health Relevance:
Biomechanical analyses based on 3D Motion Capture are a key tool for establishing quantitative, objective measures of functional movement status and treatment outcomes. In Phase I we implemented a probabilistic (Bayesian) approach for estimating the position and orientation of anatomical bodies and found that when tested against Dynamic Stereo X-ray data the approach was more accurate than existing discriminative solutions and demonstrated the potential to achieve sufficiently high spatial accuracy to study joint disease and disorders.

Project Terms:
Address; Adult; Affect; aging population; Algorithms; analytical tool; Ankle; ankle joint; Anterior; Arthritis; arthropathies; Articular ligaments; base; Bayesian Analysis; Bayesian Method; Biomechanics; bone; Cerebral Palsy; Clinical; clinical movement analysis; collateral ligament; Communities; Computer software; Custom; Data; Data Set; design; Development; Diagnostic Imaging; disability; Disease; Exhibits; foot; Gait; Health; Heart; Implant; improved; in vivo; joint function; joint stress; Joints; kinematics; Knee; Knee joint; Lead; Left; ligament injury; Magnetic Resonance Imaging; Measures; Mechanics; Methods; millimeter; Modality; Modeling; Morphologic artifacts; Motion; Movement; movement analysis; Musculoskeletal; Operative Surgical Procedures; Optics; Orthopedics; Patients; Phase; Positioning Attribute; public health relevance; Quality of life; reconstruction; Relative (related person); Resolution; Roentgen Rays; Running; Rupture; Schedule; sensor; skeletal; Skeleton; Skin; Small Business Innovation Research Grant; soft tissue; Solutions; Surface; Technology; Test Result; Testing; Thigh structure; Tissues; tool; Translations; treadmill; Treatment outcome; Ultrasonography; United States National Institutes of Health; Universities; Vertebral column; Walking