About the session
Full presentation title: Rehabilitation movement correctness classification using rough path theory and long-short-term memory neural networks.
Advances in technology in human motion capture have been quite remarkable during the last decade. Specialised depth sensors are now embedded even on mobile devices. Recent progress in Deep Learning allows standard video camera to capture 3D human motion.
Their main advantages are their non-intrusive nature, low cost and widely available support for developers offered by large corporations or Open Communities. They inspired numerous healthcare-related ideas and projects were developed in areas such as Medical Disorder Diagnosis, Assisted Living, Rehabilitation and Surgery.
In Assisted Living, human motion analysis allows continuous monitoring of elderly and vulnerable people and their activities to potentially detect life-threatening events such as falls. Human motion analysis in rehabilitation provides the opportunity for motivating patients through gamification, evaluating prescribed programmes of exercises and assessing patients’ progress. In operating theatres, surgeons may use a gesture-based interface to access medical information or control a telesurgery system. Human motion analysis may also be used to diagnose a range of mental and physical diseases and conditions.
This event will discuss recent advances in human motion analysis with applications in healthcare and provide opportunities for networking and exploring potential synergies and collaborations.
The event is co-organised by the IET Vision & Imaging TPN and the IET Healthcare TPN.
It is also sponsored by the BMVA.
