PhD Dissertation Proposal by Meeralakshmi RADHAKRISHNAN | Enabling Gesture Recognition for Lifestyle Monitoring Applications through Sensor Fusion

About The Talk

The widespread availability of multitude of sensors on personal devices (e.g., smartphones, smartwatches) and other cheap, commodity off-the-shelf (COTS) IoT devices in the environment has opened up the opportunity for developing applications that are targeted at improving daily lifestyle of individuals. Moreover, there is a growing trend of ubiquitous computing towards individuals’ well-being, with increased interest among people in leveraging technology for deriving a better quality of life. Several of the lifestyle monitoring applications rely primarily on the capability of recognizing contextually relevant human movements, actions and gestures. This thesis explores how such ubiquitously available sensing devices and the wealth of data that can be sensed from such devices be utilized for inferring fine-grained gestures that are elemental in various daily lifestyle activities and tackles several challenges pertaining to its use in real-life scenarios. Using two commonplace everyday activities (shopping and exercising) as examples, this thesis demonstrates that unobtrusive, accurate and robust monitoring of various aspects of daily activities is indeed possible with minimal overhead and at a low-cost and can help enable exciting useful applications (e.g., smart reminder or proactive retail help in a grocery store or digital personal coach in a gym).

Speaker Biography

Meeralakshmi Radhakrishnan is a PhD candidate in the School of Information Systems, Singapore Management University, working in the area of mobile and ubiquitous computing. She is advised by Professor Archan Misra. She received her Bachelor of Engineering degree from the Mahatma Gandhi University, India, in 2011. Prior to joining the PhD programme, she was a Senior Software Engineer at Infosys Limited, India. Her current research focuses on exploiting mobile, wearable and IoT sensing for lifestyle monitoring applications.