# Portable RGB-D Camera-Based System for Assessing Gait Impairment Progression in ATTRv Amyloidosis
## 5 Conclusions  
In this study, we explored the use of an affordable, portable, simple to operate, and non-intrusive system based on a single RGB-D camera to complement the clinical evaluation of ATTRv amyloidosis progression. More specifically, we investigated if our RGB-D camera-based gait analysis system is a viable alternative to a multi-camera marker-based reference system for detecting relevant changes in the gait of ATTRv amyloidosis patients during their follow-up. Gait data were collected from six ATTRv amyloidosis patients at two different time points using our gait analysis system based on a single RGB-D camera (Kinect v2) and a multi-camera marker-based Qualisys system (reference). Several gait parameters were extracted from the 3D data provided by the two systems. For each parameter, patient, and system, we verified if there was an increase or decrease in the mean parameter value between the two assessment moments and if the difference was statistically significant or not. We found that the RGB-D system is in accordance with the reference system for half or more patients for all spatiotemporal parameters, except for step, swing, single and double support duration, and TBCM ML sway. On the other hand, there was agreement between both systems for less than half of the patients for all kinematic parameters, except for spine shoulder angle and maximum and minimum elbow angles (accordance for half the patients). An almost perfect strength of agreement was obtained for the best six gait parameters when considering the existence or not of a statistically significant difference between the two analysis moments and all patients. To the best of our knowledge, these results are the first to show the potential of using a gait analysis system based on a single RGB-D camera for detecting statistically significant progression in the gait of ATTRv amyloidosis patients over time, demonstrating that this type of system can become a useful tool for detecting the evolution of ATTRv amyloidosis patients regarding several spatiotemporal gait parameters, during follow-up, and in a clinical environment. They can also be useful as a basis to propose a new and more fine-grained scale based on gait characteristics in the future. This system can also contribute to enhanced knowledge regarding disease progression (individually and collectively). Moreover, it can be useful for the evaluation of the outcome of new treatments or treatment adjustments, as well as the development of targeted interventions and new strategies to improve the patients' gait and consequently their quality of life. Although the obtained results suggest that a single RGB-D camera can provide valuable information for gait assessment during ATTRv amyloidosis patient follow-up, further studies with a larger number of patients and data acquisition sessions per patient, as well as greater clinical insight, are necessary. The improvement of gait stability detected in the increased muscle strength and range of joint motion, resulting from exercise programs, has been suggested for diabetic patients with gait impairments [61; 62] and ATTRv amyloidosis patients' quality of life may benefit from the same approach. Furthermore, it has previously been shown that RGB-D cameras can be used to assess balance in healthy subjects [35]. Therefore, it would also be interesting to investigate if an RGB-D camera can be used to support the assessment of ATTRv amyloidosis patients during balance tests, which may additionally help to deepen the understanding of the evolution of stability during gait in ATTRv amyloidosis. Additionally, in future work, it would also be important to explore ways of further improving the gait parameters, especially kinematic parameters, obtained from the RGB-D camera data, by investigating alternative methods for noise removal, such as other filters, or machine learning. Conceptualization, M.d.C.V.-B. and J.P.S.C. ; methodology, M.d.C.V.-B, H.M.P.C., A.P.R. ; software, H.M.P.C., A.P.R., M.d.C.V.-B. ; validation, M.d.C.V.-B., M.N.C. and J.P.S.C.