Kinect v2 based system for Parkinson's disease assessment

Abstract

Human motion analysis can provide valuable information for supporting the clinical assessment of movement disorders, such as Parkinson's disease (PD). In this contribution, we study the suitability of a Kinect v2 based system for supporting PD assessment in a clinical environment, in comparison to the original Kinect (v1). In this study, 3-D body joint data were acquired from both normal subjects, and PD patients treated with deep brain stimulation (DBS). Then, several gait parameters were extracted from the gathered data. The obtained results show that 96% of the considered parameters are appropriate for distinguishing between non-PD subjects, PD patients with DBS stimulator switched on, and PD patients with stimulator switched off (p-value <; 0.001, Kruskal-Wallis test). These results are markedly better than the ones obtained using Kinect v1, where only 73% of the parameters are considered appropriate (p-value <; 0.001).

Reliability and comparison of Kinect-based methods for estimating spatiotemporal gait parameters of healthy and post-stroke individuals

“…Since the reliable tracking range of the Kinect v2 is restricted to 4 m (from 4.5 to 0.5 m) (Dolatabadi et al 2016;Geerse et al 2015;Rocha et al 2015), the analysis of the data was limited to that space. Spatiotemporal parameters were estimated from both the recorded video and the Kinect-based data.…”

Design and Validation of Rule-Based Expert System by Using Kinect V2 for Real-Time Athlete Support

“…However, also in this study, the effect of the system on physical-cognitive development with its ability to increase training efficiency was not tested for long-term use. In addition to these studies, there are also systems that perform posture and pose analysis by using Kinect in the treatment of Parkinson by reaching the shapes and in the treatment of balance disorder by following walking on a straight line [38][39][40]; also in these studies, however, it was shown that the abovementioned deficiencies were still present.…”

Análisis automático del patrón de la marcha mediante visión por computador: problemas, métodos y aplicaciones

“…Rocha et al [75] evaluate the discriminant power of several gait parameters extracted from Kinect data, for distinguishing between PD patients treated with deep brain stimulation and control subjects. In [86], a Kinect-based approach for analyzing the movements of PD patients during rehabilitation treatment is presented, as a preliminary step towards a system suitable for home usage.…”

“…This makes gait a reliable indicator to detect symptoms of worsening health caused by aging [114], physical malfunction [27], or neurodegenerative disorders. Some examples of these last ailments are Parkinson's disease [75,86,105], multiple sclerosis [55] and strokes [92]. In this regard, neurologists handle a number of diagnostic tests for assessing and manually scoring gait disorders, such as the Unified Parkinson's Disease Rating Scale (UPDRS) [22] or the Rating Scale for Gait Evaluation (RSGE) [59].…”

“…However, the last decade has witnessed a growing interest in clinical applications of gait assessment such as rehabilitation [24], medical diagnosis [75], and detection of medical emergencies in hospital environments [73]. These results are supported by different sensors for extracting gait data, including wearable gadgets (e.g., gyroscopes, accelerometers) [9,79] and vision-based devices (e.g., Microsoft Kinect, RGB cameras) [73,75,86,114]. Sensors in the first group acquire accurate information, although they can be deemed intrusive since they are usually attached to joints, thus causing discomfort to patients.…”