BackgroundThe introduction of low cost optical 3D motion tracking sensors provides new options for effective quantification of motor dysfunction.ObjectiveThe present study aimed to evaluate the Kinect V2 sensor against a gold standard motion capture system with respect to accuracy of tracked landmark movements and accuracy and repeatability of derived clinical parameters.MethodsNineteen healthy subjects were concurrently recorded with a Kinect V2 sensor and an optical motion tracking system (Vicon). Six different movement tasks were recorded with 3D full-body kinematics from both systems. Tasks included walking in different conditions, balance and adaptive postural control. After temporal and spatial alignment, agreement of movements signals was described by Pearson’s correlation coefficient and signal to noise ratios per dimension. From these movement signals, 45 clinical parameters were calculated, including ranges of motions, torso sway, movement velocities and cadence. Accuracy of parameters was described as absolute agreement, consistency agreement and limits of agreement. Intra-session reliability of 3 to 5 measurement repetitions was described as repeatability coefficient and standard error of measurement for each system.ResultsAccuracy of Kinect V2 landmark movements was moderate to excellent and depended on movement dimension, landmark location and performed task. Signal to noise ratio provided information about Kinect V2 landmark stability and indicated larger noise behaviour in feet and ankles. Most of the derived clinical parameters showed good to excellent absolute agreement (30 parameters showed ICC(3,1) > 0.7) and consistency (38 parameters showed r > 0.7) between both systems.ConclusionGiven that this system is low-cost, portable and does not require any sensors to be attached to the body, it could provide numerous advantages when compared to established marker- or wearable sensor based system. The Kinect V2 has the potential to be used as a reliable and valid clinical measurement tool.

Fluctuations of motor symptoms make clinical assessment in Parkinson’s disease a complex task. New technologies aim to quantify motor symptoms, and their remote application holds potential for a closer monitoring of treatment effects. The focus of this study was to explore the potential of a stepping in place task using RGB-Depth (RGBD) camera technology to assess motor symptoms of people with Parkinson’s disease. In total, 25 persons performed a 40 s stepping in place task in front of a single RGBD camera (Kinect for Xbox One) in up to two different therapeutic states. Eight kinematic parameters were derived from knee movements to describe features of hypokinesia, asymmetry, and arrhythmicity of stepping. To explore their potential clinical utility, these parameters were analyzed for their Spearman’s Rho rank correlation to clinical ratings, and for intraindividual changes between treatment conditions using standard response mean and paired t-test. Test performance not only differed between ON and OFF treatment conditions, but showed moderate correlations to clinical ratings, specifically ratings of postural instability (pull test). Furthermore, the test elicited freezing in some subjects. Results suggest that this single standardized motor task is a promising candidate to assess an array of relevant motor symptoms of Parkinson’s disease. The simple technical test setup would allow future use by patients themselves.

BackgroundPhysical activity (PA) is frequently restricted in people with multiple sclerosis (PwMS) and aiming to enhance PA is considered beneficial in this population. We here aimed to explore two standard methods (subjective plus objective) to assess PA reduction in PwMS and to describe the relation of PA to health-related quality of life (hrQoL).MethodsPA was objectively measured over a 7-day period in 26 PwMS (EDSS 1.5–6.0) and 30 matched healthy controls (HC) using SenseWear mini® armband (SWAmini) and reported as step count, mean total and activity related energy expenditure (EE) as well as time spent in PA of different intensities. Measures of EE were also derived from self-assessment with IPAQ (International Physical Activity Questionnaire) long version, which additionally yielded information on the context of PA and a classification into subjects’ PA levels. To explore the convergence between both types of assessment, IPAQ categories (low, moderate, high) were related to selected PA parameters from objective assessment using ANOVA. Group differences and associated effect sizes for all PA parameters as well as their relation to clinical and hrQoL measures were determined.ResultsBoth, SWAmini and IPAQ assessment, captured differences in PA between PwMS and HC. IPAQ categories fit well with common cut-offs for step count (p = 0.002) and mean METs (p = 0.004) to determine PA levels with objective devices. Correlations between specifically matched pairs of IPAQ and SWAmini parameters ranged between r .288 and r .507. Concerning hrQoL, the lower limb mobility subscore was related to four PA measures, while a relation with patients’ report of general contentment was only seen for one.ConclusionsBoth methods of assessment seem applicable in PwMS and able to describe reductions in daily PA at group level. Whether they can be used to track individual effects of interventions to enhance PA levels needs further exploration. The relation of PA measures with hrQoL seen with lower limb mobility suggests lower limb function not only as a major target for intervention to increase PA but also as a possible surrogate for PA changes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12883-016-0783-0) contains supplementary material, which is available to authorized users.

Contactless measurements during the night by a 3-D-camera are less time-consuming in comparison to polysomnography because they do not require sophisticated wiring. However, it is not clear what might be the diagnostic benefit and accuracy of this technology. We investigated 59 persons simultaneously by polysomnography and 3-D-camera and visual perceptive computing (19 patients with restless legs syndrome (RLS), 21 patients with obstructive sleep apnea (OSA), and 19 healthy volunteers). There was a significant correlation between the apnea hypopnea index (AHI) measured by polysomnography and respiratory events measured with the 3-D-camera in OSA patients (r = 0.823; p < 0.001). The receiver operating characteristic curve yielded a sensitivity of 90% for OSA with a specificity of 71.4%. In RLS patients 72.8% of leg movements confirmed by polysomnography could be detected by 3-D-video and a significant moderate correlation was found between PLM measured by polysomnography and by the 3-D-camera (RLS: r = 0.654; p = 0.004). In total, 95.4% of the sleep epochs were correctly classified by the machine learning approach, but only 32.5% of awake epochs. Further studies should investigate, if this technique might be an alternative to home sleep testing in persons with an increased pre-test probability for OSA.

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