Assessing dynamic postural control during exergaming in older adults: A probabilistic approach.

Digital games controlled by body movements (exergames) have been proposed as a way to improve postural control among older adults. Exergames are meant to be played at home in an unsupervised way. However, only few studies have investigated the effect of unsupervised home-exergaming on postural control. Moreover, suitable methods to dynamically assess postural control during exergaming are still scarce. Dynamic postural control (DPC) assessment could be used to provide both meaningful feedback and automatic adjustment of exergame difficulty. These features could potentially foster unsupervised exergaming at home and improve the effectiveness of exergames as tools to improve balance control. The main aim of this study is to investigate the effect of six weeks of unsupervised home-exergaming on DPC as assessed by a recently developed probabilistic model. High probability values suggest 'deteriorated' postural control, whereas low probability values suggest 'good' postural control. In a pilot study, ten healthy older adults (average 77.9, SD 7.2 years) played an ice-skating exergame at home half an hour per day, three times a week during six weeks. The intervention effect on DPC was assessed using exergaming trials recorded by Kinect at baseline and every other week. Visualization of the results suggests that the probabilistic model is suitable for real-time DPC assessment. Moreover, linear mixed model analysis and parametric bootstrapping suggest a significant intervention effect on DPC. In conclusion, these results suggest that unsupervised exergaming for improving DPC among older adults is indeed feasible and that probabilistic models could be a new approach to assess DPC.

Classification of Parkinsonian syndromes from FDG-PET brain data using decision trees with SSM/PCA features.

Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy) compared to healthy controls. The scaled subprofile model/principal component analysis (SSM/PCA) method was applied to FDG-PET brain image data to obtain covariance patterns and corresponding subject scores. The latter were used as features for supervised classification by the C4.5 decision tree method. Leave-one-out cross validation was applied to determine classifier performance. We carried out a comparison with other types of classifiers. The big advantage of decision tree classification is that the results are easy to understand by humans. A visual representation of decision trees strongly supports the interpretation process, which is very important in the context of medical diagnosis. Further improvements are suggested based on enlarging the number of the training data, enhancing the decision tree method by bagging, and adding additional features based on (f)MRI data.