MindHive: An HCI-Based Assistive Smart Home System for Paralyzed Individuals

Paralysis is a severe medical condition that affects millions of people worldwide, often resulting in the partial or complete loss of voluntary motor functions. For individuals living with paralysis, basic daily activities can become significant challenges, impacting physical well-being and increasing depen-dence. This paper presents MindHive, an assistive smart home system designed to address these challenges. The system inte-grates Human–Computer Interaction (HCI), Electromyography (EMG), and Internet of Things (IoT) technologies to empower paralyzed individuals. We detail the system’s architecture, which leverages non-invasive surface EMG signals to control household appliances and communication tools. The algorithmic approach, including signal preprocessing, feature extraction, and machine learning classification (LDA, SVM, and 1D-CNN), is discussed. MindHive focuses on creating an adaptive, affordable, and user-friendly environment, transforming minimal muscle activity into actionable smart home commands, thereby aiming to restore a degree of autonomy to its users.