Max Robotics — Global Service Robot Directory + US Certification

About

Lorena Muscar is a leading researcher in service robotics, specializing in auditory perception and human-robot interaction. Her work focuses on equipping robots, particularly the TIAGo service robot, with advanced audio capabilities for healthcare and home assistance. Muscar has made major contributions by developing comprehensive audio databases—including a 3,300-signal dataset for sound classification and a pioneering Romanian-language emotion recognition database—and by benchmarking classification algorithms that achieve over 98% accuracy using Mel-Frequency Cepstral Coefficients. Her research enables robots to detect indoor activities, recognize emotional states (happiness, neutral, sad), and issue real-time warnings for dangerous situations, effectively transforming TIAGo into a medical assistant. With papers accumulating over 35 citations, her most cited works—such as "Audio Database for TIAGo Service Robot" and "Sound Classification Algorithms for Indoor Human Activities"—have established foundational resources for the field. Notably, her 2024 deep learning study pushes classification to 148 distinct audio classes, demonstrating continuous innovation. Muscar’s work is essential for students and researchers interested in how robots can understand and respond to human environments through sound.