Sensor-Based Movement and Sleep Analysis in Parkinson’s Disease
In D04, innovative, non-contact EmpkinS sensor technology using machine learning algorithms and multimodal reference diagnostics is evaluated using the example of Parkinson’s-associated sleep disorder patterns. For this purpose, body function parameters of sleep are technically validated with wearable sensor technology and non-contact EmpkinS sensor technology in comparison to classical polysomnography and correlated to clinical scales. In an algorithmic approach, multiparametric sleep parameters and sleep patterns are then evaluated in correlation to movement, respiratory, cardiovascular, and sleep phase regulation disorders. Thereby, using the combination of intelligent Machine Learning algorithms and contactless EmpkinS Sensor Technology facilitates unobtrusive sleep analysis. This opens up new possibilities, such as continuing long-term monitoring of sleep-related Parkinson’s symptoms improving diagnosis and treatment.
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