Skip to main content

LUCID – Concussion Identification app

We have developed a portable smartphone app which can be used on any smartphone by anyone to measure concussion. It can indicate whether someone has a concussion pitch-side during sports matches or in hospitals, and may also be useful for any industry where concussion may occur e.g. Theme parks, construction, etc.
https://www.lboro.ac.uk/research/national-rehabilitation-centre/research/projects/eye-tracking-concussion/

INDAF – App that generates automated student feedback reports for exams & assessments.

In many University assessments, the only feedback students receive is an overall final mark. INDAF provides students with a more detailed account of their performance in the form of a feedback report. If an assessment can be divided into categories, performance can be rated for each category, giving students more specific information about their strengths and weaknesses within the assessment to improve their performance.
INDAF provides a feature that does not exist in Loughborough’s Learn platform (for students) and it offers users a tool to create customised feedback reports flexibly (than buying several apps to carryout different tasks). INDAF offers options to enable collaborative marking and to customise to a range of assessments. (eg. choice of question number, category number, or exam structure, assessments with optional questions vs essay assessments vs MCQ assessments).
INDAF has been successfully used for one year in three Loughborough University Schools and it could be rolled out more widely in higher education, or in any setting for which performance is assessed and fed back (so also has potential use in industry).

Personalised Space Technology Exercise Platform (P-Step)

The University of Leicester and Leicester’s hospitals have secured £2 million from the UK Space Agency, in collaboration with NHS England and the European Space Agency, to develop a mobile app addressing the challenge of managing long-term conditions for the NHS’ 70th birthday. The Personalised Space Technology Exercise Platform (P-STEP) app will leverage space data and artificial intelligence to provide disease-specific exercise advice at a 10-metre resolution, including pollution warnings. Led by Professor André Ng, the Leicester team aims to combine high-resolution air quality data with personalised exercise guidance, addressing concerns for patients with conditions like heart disease and asthma. The app aims to simplify exercise prescriptions, improve well-being, and mitigate the impact of air pollution on health. The demonstrator project is being delivered by a broad team including clinicians, computer scientists and informatitions, health psychologists, primary care providers and environmental health and Earth observation experts in collaboration with EarthSense, a local SME.