Zaqiatud Darojah
Papers
1
Total Citations
4
H-Index
1
About
Zaqiatud Darojah is a researcher whose work bridges artificial intelligence, robotics, and signal processing, with a particular focus on human-robot interaction and autonomous systems. Her most cited paper, "Mel-Frequency Cepstral Coefficient (MFCC) for Music Feature Extraction for the Dancing Robot Movement Decision" (2016), demonstrates her innovative approach to enabling robots to interpret and respond to auditory cues. By applying MFCC—a technique commonly used in speech recognition—to music feature extraction, Darojah developed a method that allows robots to make real-time movement decisions based on rhythmic and tonal patterns. This work, which has garnered 4 citations, represents a foundational contribution to the field of socially interactive robotics, where machines are designed to engage with humans in more natural, intuitive ways. Her research has implications for entertainment, education, and therapeutic robotics, showcasing how low-cost signal processing can enhance robotic autonomy. Darojah’s work stands out for its practical integration of audio analysis into robotic decision-making, offering a scalable framework for future developments in responsive, context-aware machines.
Research Focus
Key Achievements
Top Papers
- 1