Title: Computational methods towards monitoring in-vivo hip joint loads during physical exercises (CHOOSE).
Funder: The Academy of Medical Sciences

Abstract: AIM: The principal aim of the present study is to develop a rapid and accurate method to quantify femoral neck (FN) loads during ballistic exercise training. OBJECTIVES: 1) Quantify the effects of different ballistic exercises on peak FN loads; and 2) Quantify the accuracy of using wearable sensors and machine learning in the prediction of FN loads. BACKGROUND: Femoral neck fractures are the most common of all osteoporotic-related fractures in older adults, which are associated with significant morbidity and mortality. Exercise is often prescribed to improve the FN’s bone mineral density (BMD), although there is significant variability in the osteogenic response to training. A fundamental problem in current training studies is that there are no logistically feasible methods of continuously monitoring FN loads throughout an exercise program. This means that optimal exercise mode and volume (repetitions, sets, sessions) to improve bone mineral density cannot be determined. METHODS: Three-dimensional motion capture along with inertial measurement units (IMUs) assessment will be performed on 60 healthy adults, during the performance of four ballistic exercises (bilateral and unilateral hopping, countermovement jumps, and squat jumps). Musculoskeletal and finite element modelling will be performed to quantify in-vivo FN contact forces and strain patterns, and comparison of loads between exercises will be performed. Angular and linear kinematics from the IMUs will be used as predictors during machine learning to quantify peak FN loads during the exercises performed. SIGNIFICANCE: The capacity to rapidly predict FN loads will help advance the design of future exercise interventions to improve BMD.

Lay abstract: Hip fractures commonly occur in older adults with poor bone strength, which can result in pain, reduce independence, and increase the risk of death. Weight bearing exercise is often advised to promote the formation of new bone and improve bone strength. The amount of exercise required for new bone formation in the hip is unclear, given we cannot easily measure hip forces as traditional methods of measuring hip forces are very time consuming. The immediate aim is to develop a method to rapidly measure hip forces during exercise that will help clinicians monitor the appropriateness of an exercise. This project has two objectives: 1) to measure how different exercises influence hip forces and 2) to compare the accuracy of a new method of estimating hip forces (using statistics), against the traditional method (building a complex computer model of the hip). A sophisticated system of high-speed cameras, force plate, wearable sensors to measure muscle activity and body movements will be used on 60 healthy adults, during the performance of four exercises (including hopping and jumping). We will build a realistic computer representation of the body to quantify hip forces, comparing the four exercises. We will then use complex statistics to harness movement information from the sensors to predict the hip forces obtained from the computer model. Beyond this project, the capacity to rapidly predict hip forces will help guide clinicians design better exercise regimes to improve bone strength.

Research activity codes: 5.3 MEDICAL DEVICES
Health categories: MUSCULOSKELETAL