Dr Hossein Parineh is a researcher and builder working at the intersection of applied artificial intelligence, signal processing, embedded systems, and intelligent real-world platforms.
His PhD research at the University of Melbourne pioneered advanced AI models for acoustic signal processing in urban environments, including the development of the first large-scale benchmark dataset MELAUDIS. As a Postdoctoral Research Fellow, he has continued developing multi-modal AI systems that combine computer vision, audio signal analysis, and sensor fusion for safety, mobility, and health-related applications.
Through Intellimotion, he focuses on turning advanced research into usable systems — building solutions that do not just analyse data, but interact with people, support decisions, and operate in real environments.
His work spans academic research, R&D engineering, product design, and deployment-minded system development in Australia and internationally.
Founder, CEO & CTO — Intellimotion
Education
PhD — Applied Artificial Intelligence (University of Melbourne)
Core focus
Multi-modal AI • Computer Vision • Audio Signal Processing • Embedded / Edge AI • Sensor Fusion • Applied Product Systems
Expertise Summary
End-to-end development spanning problem framing, data strategy, model development, evaluation, and deployment-ready system design.
Pose estimation, tracking, motion measurement, and real-time interpretation for coaching, safety, and monitoring applications.
Acoustic event analysis and robust AI models for real-world urban noise conditions, including benchmark dataset development and experimental validation.
Integration of sensing, edge computation, and software pipelines to create systems that work under real deployment constraints.
Domains of Impact
Motion-aware platforms that support exercise quality, physiological awareness, and measurable user progress.
Intelligent transport analytics and sensing systems that support safer, more efficient movement in real city environments.
Multi-modal AI for situational awareness using combined visual, acoustic, and sensor-driven evidence.
Bridging academic research, engineering, and product development to create usable technology with practical impact.