Cycle path mapping with a custom hardware platform
Contacts are: 'Austin Donnelly' <Austin.Donnelly@microsoft.com> 'Scarlet Schwiderski-Grosche' <scarlets@microsoft.com>
Project 1: Project description - Friend meeting/tracking application for the Elderly
Elderly people sometimes need help to remain active and social. This project explores how a smartphone can arrange social gatherings/ meetings for them. This might include encouraging people to meet up by locating their friends, perhaps notifying them that a friend is nearby, and maybe even suggesting a location to meet. It may also be useful as a monitoring device for patients who make a habit of wandering off, distressing their family and carers. The user interface is important, since it must be accessible to those with less good eyesight and hand coordination. There will be little credit for boilerplate features such as user registration, account maintenance and database design, rather we expect to see innovative technical solutions to the user interface, messaging, security (privacy) and identity.
Deliverables: A Windows Phone 7 application that has been accepted into the Marketplace for general availability. We will provide handsets to test with.
Project 2: Project description - Cycle path mapping with a custom hardware platform.
The aim of this project is to produce a highly accurate map of the Cambridge cycle network including information about inclines, one way roads, restricted access. Since this will require more data than can be gathered using a simple GPS device or smartphone this project will have a custom hardware element based around the .NET Gadgeteer platform. The first part of the project will build a device to capture data such as GPS, accelerometer, compass, cadence, wheel rotation, heart rate etc, to produce a sample dataset representative of the Cambridge cycle network, as well as providing the cyclist with relevant data.
The second part of the project will process and visualise the sample dataset. This will include removing erroneous data, fusing data streams and maps to improve accuracy and inferring cycle features from the data whilst keeping an emphasis on data accuracy. This information could be used to recommend cycle routes or diversions in real time, assist with finding a bike rack, or perhaps the addition of new cycle lanes. The resulting dataset should be open and accessible.
