Validation of a Single RGB-D Camera for Gait Assessment of Polyneuropathy Patients

Motion analysis systems based on a single markerless RGB-D camera are more suitable for clinical practice than multi-camera marker-based reference systems. Nevertheless, the validity of RGB-D cameras for motor function assessment in some diseases affecting gait, such as Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP), is yet to be investigated. In this study, the agreement between the Kinect v2 and a reference system for obtaining spatiotemporal and kinematic gait parameters was evaluated in the context of TTR-FAP. 3-D body joint data provided by both systems were acquired from ten TTR-FAP symptomatic patients, while performing ten gait trials. For each gait cycle, we computed several spatiotemporal and kinematic gait parameters. We then determined, for each parameter, the Bland Altman's bias and 95% limits of agreement, as well as the Pearson's and concordance correlation coefficients, between systems. The obtained results show that an affordable, portable and non-invasive system based on an RGB-D camera can accurately obtain most of the studied gait parameters (excellent or good agreement for eleven spatiotemporal and one kinematic). This system can bring more objectivity to motor function assessment of polyneuropathy patients, potentially contributing to an improvement of TTR-FAP treatment and understanding, with great benefits to the patients' quality of life.This research was funded by ERDF – European Regional Development Fund through the Operational Program for Competitiveness and Internationalization - COMPETE 2020, and by national funds through the Porto Hospital Center (CHP) in the context of the scholarship BI.02/2018/UCA/CHP, and through the Portuguese Foundation for Science and Technology (FCT), in the context of scholarship SFRH/BD/110438/2015, and projects UID/CEC/00127/2019, UID/CEC/00127/2013, Incentivo/EEI/UI0127/2014, FCOMP-01-0124-FEDER-028943 and FCOMP-01-0124-FEDER-029673. It was also partially funded by NORTE2020 Integrated Project NanoSTIMA “NORTE-01-0145-FEDER-000016”, and POCI-01-0145-FEDER-028618 (PTDC/CCI-COM/28618/2017) - PERFECT, under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund