The physiological basis of cerebrovascular accidents make gait deficits a common sequelae after stroke . More than 60% of stroke survivors are unable to walk independently after the injury  and, even after rehabilitation, more than half of the cases still present gait-related deficits . Most prevailing deficits after stroke include reduced speed  and increased gait inter-limb asymmetry . These gait impairments can be aggravated in the elderly, due to the natural musculoskeletal and cognitive decline with age [6, 7], where the incidence of stroke is higher . Importance of these deficits relies on their great impact on independence , quality of life , and fall risk . Consequently, their adequate assessment is necessary for a proper diagnosis and to plan, if required, customized interventions to each individual’s condition and evaluate the effectiveness of these interventions.
Assessment of gait is commonly performed in the clinical setting using standardized scales and tests that evaluate different aspects of human locomotion and, in some cases, compare the results of the person being tested with those obtained by a matched healthy sample . Although these tools are easy to administer and, in general, not time-consuming, they can present lack of specificity and, more importantly, may have poor accuracy and be biased by subjective evaluations . Over the years, different technological solutions have been proposed to overcome these limitations. Accurate estimation of spatiotemporal parameters has been enabled by instrumented walkways  and force plates , generally, from ground reaction forces during walking. Estimation of kinematic parameters, however, require the position of several joints to be tracked during the test, which has been indirectly facilitated by different technological solutions that estimate the position of some sensors that are attached to specific body parts [16,17,18]. Among them, optical motion tracking has become the most common alternative for accurate investigation of kinematic gait parameters . Although instrumented systems allow for accurate spatiotemporal and kinematic analysis, their high cost and large size have restricted their use to research laboratories and large clinical centers with high economic resources .
In the last years, the Microsoft Kinect (Microsoft, Redmond, WA), a portable off-the-shelf infrared camera originally intended for entertainment, has enabled human motion tracking without using wearable sensors at a very low-cost. Reliability studies have shown comparable performance of the Kinect to laboratory-grade gait analysis systems, for both the first [21, 22] and the second version of the device , known as the Kinect v2, which features improved depth accuracy and number of joints tracked . Characteristics of the Kinect v2 have motivated their use for assessing spatiotemporal [25,26,27] and kinematic parameters of gait [26, 28] with promising results in healthy individuals, even on treadmills [28, 29]. Its reliability in stroke population, however, remains almost unexplored. Little evidence suggests that data retrieved from the Kinect v2 can be used to differentiate healthy subjects from individuals with stroke  and to complement clinical assessment . Despite of the existing data supporting the reliability of the Kinect v2 to assess spatiotemporal and kinematic gait parameters, the unavailability of the software, the limited investigation in individuals with stroke, and the unknown psychometric properties of Kinect-based tests in this population could compromise the clinical relevance of these results.
The objective of this study was fourfold. First, to compare a cohort of individuals with stroke with respect to a group of healthy controls to determine the sensitivity of an open-access Kinect v2-based gait analysis system to motor disability and aging. Second, to determine the concurrent validity of the system with standardized clinical tests in individuals with stroke. Third, to quantify its reliability as defined by the inter and intra-rater reliability, the standard error of measurement, and the minimal detectable change. And, finally, to investigate the ability of the system to identify risk of falls after stroke.