# Portable RGB-D Camera-Based System for Assessing Gait Impairment Progression in ATTRv Amyloidosis
## 1 Introduction
### Related Work  
The Kinect's ability to assess 3D motion has been reported in the literature for healthy [29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39] and impaired [40; 41; 42; 43; 44; 45; 46; 47] populations. [PERSON] et al. validated the Kinect v2 tool for gait assessment (30 healthy subjects), reporting that the studied spatiotemporal gait parameters had a consistent and excellent relative agreement; however, the kinematic parameters showed poor results [36]. [PERSON] et al. evaluated the gait of 20 subjects while walking and jogging at three velocities on a treadmill and assessed the validity of the Kinect when compared to a reference system [30]. Conclusions of this study included the poor performance of the system when assessing hip angular displacement, as well as a general under-estimation of joint flexion and over-estimation of joint extension. [PERSON] et al. performed sensor validation for ranges of motions, torso sway, movement velocities, and cadence during sitting, standing, and walking activities [39]. They evaluated 19 healthy subjects and the joint movement signals showed moderate to excellent agreement, with noisier ankles and feet signals. As for clinical parameters, most of them showed good to excellent agreement. They concluded that the Kinect v2 can be used as a reliable and valid clinical measurement tool.  
In studies with patients suffering from neurologic diseases, [PERSON] et al. studied the agreement between the Kinect and a reference system for clinically relevant movements in [PERSON]'s disease, concluding that the Kinect tool can accurately measure timing and gross spatial parameters, but does not present the same spatial accuracy for smaller movements, such as hand clasping [43]. [PERSON] et al. [44; 45] and [PERSON] et al. [40] studied the use of Kinect v1 for supporting the gait assessment of patients with multiple sclerosis.  
Our group carried out two studies that explored the possibility of using a Kinect for distinguishing between Parkinson's disease patients and healthy controls based on their gait [46; 47]. Furthermore, we performed a study on gait analysis using a Kinect (version 2) involving ATTRv amyloidosis patients and healthy subjects [48]. In this study, we found that several gait parameters have the potential to be used to distinguish between ATTRv amyloidosis mutation carriers (with and without symptoms) and control subjects, and thus can be used to support ATTRv amyloidosis diagnosis and/or stage distinction.  
We also carried out studies on the validity and/or reliability of the Kinect against a reference motion capture system, with a healthy population (N = 20) [49] and ATTRv amyloidosis patients (N = 6) [50]. The results achieved in both situations showed that a single Kinect can be used to obtain several spatiotemporal measures with excellent agreement with the reference system. This can be useful for supporting clinical evaluations during the 6 MWT. Overall, results were better for spatiotemporal than for kinematic parameters.  
A preliminary study on the ability of the Kinect to assess changes in the gait of ATTRv amyloidosis patients during their clinical follow-up was also performed by our group [51]. We carried out a quantitative gait analysis for six patients at two different moments in time (with an 18-month interval), which included the computation of thirteen gait parameters for each analysis and patient. Overall, we found that the Kinect can potentially be used for the defined aim based mostly on the considered spatiotemporal parameters.  
In addition to the studies described above, there are reports of experiments using more than one Kinect v2 for gait analysis [31; 32]. However, as we intend to find a suitable yet simple approach to use in a clinical ward, these contributions are out of the scope of this work.