Abstract
Freezing of gait (FOG) is one of the most common manifestations of advanced Parkinson’s disease. It represents a sudden interruption of walking forward associated with an increased risk of falling and poor quality of life. Evolutionary algorithms, such as genetic programming (GP), have been effectively applied in modelling many real-world application domains and diseases occurrence. In this paper, we explore the application of GP for the early detection of FOG episodes in patients with Parkinson’s disease. The study involves the analysis of FOG by exploiting the statistical and time-domain features from wearable sensors, followed by automatic feature selection and model construction using GP. Efforts to use data from wearable sensors suffer from challenges caused by imbalanced class labels, which affect the task of GP model development. Thus, the cost-sensitive approach is incorporated into GP to tackle the imbalanced problem. The standard metrics, such as sensitivity, specificity, and F1-score, were used for testing the final model. With 30 repetitions, the average performance of the GP model has shown promising results in detecting the occurrence of FOG episodes in Parkinson’s disease.
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This work was supported by European Union through the Horizon 2020 Research and Innovation Programme in the context of the ALAMEDA project under grant agreement No GA 101017558.
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Nega Tarekegn, A., Alaya Cheikh, F., Sajjad, M., Ullah, M. (2023). Towards Detecting Freezing of Gait Events Using Wearable Sensors and Genetic Programming. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2023. Lecture Notes in Computer Science(), vol 14125. Springer, Cham. https://doi.org/10.1007/978-3-031-42505-9_24
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