eXplainable and Individualizable for Physiotherapeutic Decision Support for the Elderly

Motion and cognitive impairments can make the life of elderly people more difficult. Physiotherapists seek solutions to increase the quality of life of their patients through exercises and monitoring. Analysis of gait and cognitive data, collected during one year, showed patients’ responses during treatment. In this project, we used age as a feature to compare the pacient’s age and the predicted age value from gait data. The aim of this research is to show the most important features that contributed to the result of the age prediction. Support Vector Machine (SVM), Multi Layer Perceptron (MLP) and LightGBM are algorithms that had the lowest estimation errors for age prediction. Next, we carried out experiments to identify the most relevant features for the age difference. SHappley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were applied for global and local interpretation, respectively. The results showed that groups of features could be identified during the interpretation. These preliminary results encourage our research to apply XAI and reduce the features dimensionality, providing more confidence as well as simplicity in clinical applications that use Artificial Intelligence.