Development of a depth camera-based instructional tool for resistive exercise during spaceflight

Abstract

Resistive exercise is essential to maintaining proper musculoskeletal health during spaceflight. Therefore crewmembers receive instruction from strength, conditioning, and rehabilitation specialists on proper exercise technique to maximize exercise effectiveness and prevent injuries. However, long duration missions can make real-time exercise instruction and feedback problematic. A depth camera-based software tool was developed to provide exercise instruction and feedback during the deadlift exercise. The software tool uses a machine learning algorithm to identify 5 common deadlift technique mistakes. A subset containing 2 subjects with no deadlift training experience were coached on the deadlift exercise and separated into 2 groups: experimental group using the software tool or a control group without the tool. Motion-capture data were collected to evaluate the kinematic characteristics between the test and control group. It was found that the software tool assisted with increased torso, hip, and knee joint angle consistency and improved form during deadlifts.