D
D. K. Arvind
Researcher at University of Edinburgh
Publications - 85
Citations - 1073
D. K. Arvind is an academic researcher from University of Edinburgh. The author has contributed to research in topics: Wireless sensor network & Asynchronous communication. The author has an hindex of 17, co-authored 83 publications receiving 981 citations.
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Proceedings ArticleDOI
Respiratory Rate and Flow Waveform Estimation from Tri-axial Accelerometer Data
TL;DR: A method based on tri-axial accelerometer data from a wireless sensor device, which tracks the axis of rotation and obtains angular rates of breathing motion is demonstrated, which is validated against gyroscope measurements and shows high correlation to flow rate measurements using a nasal cannula.
Proceedings ArticleDOI
SpeckMAC: low-power decentralised MAC protocols for low data rate transmissions in specknets
Kai-Juan Wong,D. K. Arvind +1 more
TL;DR: This paper introduces SpeckMAC, a novel low-power distributed, unsynchronised, random-access MAC protocol for a wireless mobile ad-hoc network of miniature specks called specknet and compared theoretically with the well-known B-MAC protocol.
Proceedings ArticleDOI
Orient-2: a realtime wireless posture tracking system using local orientation estimation
TL;DR: A realtime posture tracking system has been developed using a network of compact wireless sensor devices worn by the user that reduces bandwidth requirements by 79% and allows for full-body tracking using 15 devices at a 64Hz update rate through a single 250kbps receiver.
Proceedings ArticleDOI
Speckled computing: disruptive technology for networked information appliances
D. K. Arvind,Kai-Juan Wong +1 more
TL;DR: A prototype for Specks called ProSpeckz (Programmable Specks over Zigbee Radio) which is currently used as a rapid development platform for Speckled Computing is described and the challenges to be overcome to realise this technology are outlined.
Proceedings Article
IMUSim: A simulation environment for inertial sensing algorithm design and evaluation
TL;DR: A simulation environment, specifically for inertial sensing applications, is presented, which simulates sensor readings based on continuous trajectory models, and shows how suitable models can be generated from existing motion capture or other sampled data.