TIGER: TOWARDS INJURY PREDICTION USING G-SENSOR-BASED STRAIN ESTIMATION AND MOTION REPRODUCTIO
Abstract
TIGER: Towards injury prediction using g-sensor-based strain estimation and motionreproductionApproved for Public ReleaseBackground and Significance: Recent studies involving naval special boat operators show that traveling on high speed boats can cause injury requiring hospitalization at rates that are at least 5 times as high as those of the regular Navy personnel. Despite the significant risk motorboat travel poses, to date there have been very few protocols developed to protect motorboat personnel from those risks. The primary impediment to the design and development of suchprotocols has been the lack of fundamental knowledge and a framework to predict the risk of injury based on the boat s and the wearer s particulars. The proposed research will develop the necessary instrumentation, algorithms, and procedures, as well as the soft infrastructure, that can lead us towards the creation and application of such protocols.Rationale: It has been hypothesized that injury takes place through the creation of large strainsin human tissue when the body is subjected to high accelerations. Outside of a laboratory setting, in vivo tissue strains are generally not directly accessible during injury-inducing events, such as motorboat rides. We propose to apply the following indirect strategy to estimate strains/strainratesin the brain and spine during motorboat rides: (a) We will use accelerometers (g-sensors) to measure the accelerations at various points on the head and body; (b) Using algorithms recently developed by the PI, we will model the head and body as an assembly of connected rigid bodiesand calculate their approximate large-scale kinematics; and (c) We will model the tissue at the sites of interest for injury prediction as deformable solids. We can then estimate strain by settingup computational continuum mechanics problems with the kinematics obtained in the previous steps, which provide the necessary boundary conditions and body forces for the problem. The calculated strains will be synthesized with the critical values for those quantities (provided by parallel efforts, e.g. PANTHER), using machine-learning algorithms to estimate the probability of injury.Objectives and technical approaches: As part of this project, we will develop sensor systems, algorithms, and cloud computing infrastructure to predict injury in individuals as a result of travel on a high speed motorboat. In this initial foray, we will focus on two important injury sites: the brain and the lower spine. We will establish the framework for predicting the injury at these two important sites by pursuing the following specific aims: (Aim 1.a) Design and create a smart head wearable that contains 9 sensors. We refer to this head wearable as the Accelo-Hat.(Aim 2.a) Design and create a smart whole-body wearable, an Accelo-Harness, which will contain at least 15 sensors, each of which will make three independent measurements in three orthogonal directions. Develop computational mechanics algorithms and data science proceduresto calculate pertinent biomechanics metrics that are (Aim 1.b) related to urements, and (Aim 2.b) related to lower back injury from Accelo-Harnessmeasurements.Anticipated outcome of the research: The proposed research would provide the knowledge and framework to design and apply protocols to protect the motorboat personnel from injuries due to experiencing high accelerations. Such protocols, for instance, could dictate the operation of the motorboat, the protective gear that should be worn by the motorboat personnel, or the design ofthe seating or tethering apparatus used to fasten a person to the boat.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 02, 2021
- Source ID
- N000142112054
Entities
People
- Haneesh Kesari
Organizations
- Brown University
- Office of Naval Research
- United States Navy