Development and validation of a multi-modal contactless sensing system for surgical risk analysis in a real-world environment

Gait measurements are a central component of functional assessments and risk stratification before surgery. Various sensors can measure gait metrics, but none are routinely integrated into surgical workflows because they are too challenging to implement at scale in clinical situations. In this manuscript, we report the development and validation of a rapidly-deployable, low footprint, entirely contactless sensing system, called GroundCode, that is explicitly integrated within a surgical workflow. GroundCode combines the Microsoft Kinect with seven floor-mounted single-axis accelerometers, overcoming the weaknesses of each individual sensor technology and providing both robust spatiotemporal resolution (Kinect) and high-fidelity footstep detection and quantification (floor accelerometers). We show that GroundCode-derived gait speed and cadence are highly precise measurements (>90%), and we validate them against two standard clinical gait measurements relevant to pre-surgical evaluations - stopwatch time and six-minute walk test distance. We show that GroundCode-derived gait metrics identify various surgical risk factors, like age, sex, and frailty. In addition, we show that preoperative gait is associated with postoperative quality of recovery. Importantly, we designed this system to be deployed by non-technical personnel and performed this study in a non-laboratory setting, providing proof-of-principle that GroundCode can be used in various real-world environments. We conclude that GroundCode provides highly robust gait measurements in real-world settings with possible applications spanning clinical diagnosis, risk stratification, and digital biomarker development.