Personalised Speech-Based Heart Rate Categorisation Using Weighted-Instance Learning

Running as one of the most popular sports comes with many positive effects, but also with risks. Most injuries are caused by overexertion. To optimise training and prevent injuries, approaches are needed to easily monitor training behaviour. Previous research has shown that heart rate (HR) can be automatically classified using speech data. Real-world applications pose challenges due to the heterogeneity of individuals, which is why we introduce a personalised HR classification in this work. In particular, we first determine runners in the train set with similar acoustic patterns (x-vectors) compared to a runner in the test set. Further, we extract deep representations and hand-crafted features from the input data. Subsequently, using the computed similarity values, we adapt a Support Vector Machine (SVM) for each individual. In this context, we choose the runners with the lowest Euclidean distances and weight their train samples more heavily during the training process of the SVM. Our personalised approach yields a best relative improvement of 20.8 % compared to a non-personalised model in a 5-class HR classification task. The obtained results demonstrate the effectiveness of our approach, paving the way for real-world, personalised applications.