Exploiting wearables to enhance the accessibility in electronic devices: a machine learning approach

Mobile devices like smartphones, smartwatches, or other wearables are becoming fundamental to our daily lives. Thanks to the multiple sensors embedded to interact with the user and the environment, they enable tasks such as cardiovascular and daily activity monitoring, apart from receiving calls. However, their capabilities can be further enhanced with computational models to enable autonomous learning from user behavior. Consequently, given the potential of jointly considering wearables and Machine Learning (ML) models, this research seeks to facilitate end users’ daily lives by making mobile devices such as smartphones more accessible. More in detail, ML models allow pattern detection and significant task automation, such as making calls, sending messages, or opening applications. Moreover, synthetic data generation is applied, which contributes to cost-effective, multi-spectral, scalable, and stochastic simulation. Results obtained with experimental data by applying 10-fold cross-validation are up to 96 % in all evaluation metrics. The ultimate objective is to increase accessibility for society in general and especially for people with cognitive or physical disabilities, reducing the traditional usability barriers.