Sundara Tejaswi Digumarti

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—Human activity recognition is a thriving research field. There are lots of studies in different sub-areas of activity recognition proposing different methods. However, unlike other applications, there is lack of established benchmarking problems for activity recognition. Typically, each research group tests and reports the performance of their algorithms(More)
There is a growing interest on using ambient and wearable sensors for human activity recognition, fostered by several application domains and wider availability of sensing technologies. This has triggered increasing attention on the development of robust machine learning techniques that exploits multimodal sensor setups. However, unlike other applications,(More)
In today's digital media landscape, we are constantly surrounded by displays, from the LCDs found on the phones in our pockets to the ubiquitous screens that greet us whenever we enter a store, airport, taxicab, doctor's office, or educational institution. This plethora of displays both allures us and contributes to the media's saturation of our lives. The(More)
This paper presents underwater 3D capture using a commercial depth camera. Previous underwater capture systems use ordinary cameras, and it is well-known that a calibration procedure is needed to handle refraction. The same is true for a depth camera being used underwater. We describe a calibration method that corrects the depth maps of refraction effects.(More)
A smart wheelchair can restore autonomy to patients with sensori-motor disabilities by enabling them to move around freely without depending on the care givers. The objective of a smart wheelchair is to reduce user effort in controlling the wheelchair and to ensure safety during movement. In this paper, our focus is to design and develop a smart wheelchair(More)
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