Emotional Design Framework for Pediatric Home Medical Devices Validated Through Machine Learning

Abstract

The rising use of the pediatric home medical devices has been the subject of discussion which involved the application of user-oriented and empathetic design methods. The traditional medical devices are mainly oriented towards the performance of functionality, disregarding emotional aspects of the pediatric users, this may result into anxiety, decreased usability and improper use of the device. To overcome this shortcoming, it is in this context that this paper introduces the ED-PMD framework (Emotional Design Pediatric Medical Devices) a new concept that would combine both the concepts of emotional design and machine learning to create a new framework. The framework proposed is based on structured interaction information, such as age, time of interaction, rate of error, use patterns, and behavioral cues, to generate emotional states, such as comfort, anxiety, and neutral behavior, prediction. An all-encompassing methodology based on data preprocessing, feature engineering, and trained machine learning models, such as Decision Tree, Support Vector machine (SVM) and Random Forest are created. One of them, the Random Forest model, has better results because it can process the more complex behavioral patterns, and decreases overfitting. As a performance metric, the performance of the framework can be confirmed by the analysis of the confusion matrix and visual interpretation of results (heatmaps and accuracy-loss curves). The findings reveal that the suggested approach is reliable and strong, which is proved by their high classification and low rates of misclassification. Moreover, post-processing optimization, in turn, increases model stability and convergence behavior. The suggested ED-PMD framework will allow designing adaptable and emotionally intelligent medical devices capable of responding dynamically to the needs of users. The framework is associated with enhancing the quality of interaction, decreasing anxiety levels, and increasing the level of usability, that leads to improved care outcomes and satisfaction of users. This study explains the significance of incorporating emotional intelligence into medical technology and offers a scalable base upon which the next-generation health care equipment design should be founded.