Health-033-19
TECHNOLOGY
INDUSTRY EXCHANGE NETWORK
IXN – CHARTAUCL Center for Global Health Economics
Member Name: Francesco Stefani, Matthis Peltier, Charles SmithClient: Dr Hassan Haghparast-BidgoliSupervisor: Dr. Yun Fu
User InterfaceIntuitive design and functionalities make it easy to use the app on a day-to-day basis
Question SpecificThe questions asked are rigorous and in line with the main academic standards
Multi PlatformThanks to the centralized technology, the app runs easily of different browsers and mobile operating system
Excel FriendlyThe data can be easily manipulated and download in an Excel to allow user flexibility in the analysis.
Statistical AnalysisThe paper's data are saved and quickly analysed by our algorithm in order to provide meaningful statistics figures
Multi AccessibilityDifferent users can use the app with tailored pages and actions
FEATURES
Ruby on
RailsPostgres
Azure
virtual machine
API
WWW
CHARTA
ABSTRACT
Charta is a web and mobile application that aims to facilitate the
appraisalof health researchpapers.
The app allows users to invite students and scholars to provide
feedback on academic papers and gather their responses
immediately.
This process prevents the hassle of having to send forms to
each reviewer individually before then computing their responses
within an excel file once the forms are returned. The main user
receives detailed statistical and graphical reports based on the
data gathered from the reviewers.
FUTURE DEVELOPMENTS
Preliminary check on papers using sentiment analysis via Machine Learning
Notification center to inform the users when a paper is uploaded
Integrate the app with Moodle login
simple UI which is easy to operate
for the GOSH DRE staffs. In the
backend, there is a program written
with python, which cleans, merges
and encodes the files so that the
data is ML (machine learning) ready.
We also produced a working GANS
(Generative Adversarial Networks)
using deep learning and neural
networks, which has the potential
to take in FHIR and none-FHIR data
and learn from them, then produce
synthetic data.
The goal of our project is to justify
that unstructured EHR (Electronic
Health Record) data can be
manipulated in such a way that it
can be learnable by a generative
machine learning model.
Synthetic Healthcare Data Generator with GANS
University College London | Team 1 | Zia Ali, Dylan Vekaria, Sifang Du Client | GOSH DRIVE
Overview & Solution
What is the issue?
With the introduction of GDPR, individuals
have more control over their personal
data and companies use the collected
data more cautiously under regulations.
However, the regulation also limits the
use of personal data in areas such as
research and software development.
How can we solve this problem?
The GOSH DRE team proposed to
generate synthetic data from real data
using machine learning. However, the
GOSH DRE team needs our help with
connecting the three parts shown below.
What are the requirements?
We need to build a pipeline which cleans
the data and encodes it to a machine-
readable format so that the data can be
passed into the GANS later.
Our final delivery
In our final delivery, we produced a
?
The user
interfaceThe data cleaning
programThe Peach GANS
Processor
?
Key Features
Upload raw datasets
Download cleaned datasets
Cleans, data automatically
A GANS that generates
synthetic data
v
The Architecture of GANS
Visualizations from GANS
The User Interface
[email protected] | [email protected] | [email protected] Health-007-19
SMART ON FHIR MODULES
UCLAbstract
The Fast Healthcare Interoperability Resources (FHIR) is a standard for exchanging electronic healthcare data and records
between medical institutions and applications. SMART is a platform that builds upon the FHIR specification and provides developers with a set of APIs to create applications on top of FHIR.
Our aim is to help FHIR application developers who may not be too
familiar with FHIR, to discover the capabilities of SMART APIs and build applications for this next generation of digital healthcare. Currently, we are developing a web application that is a collection of modular SMART functions. It is a library of runnable code snippets that can act as a helpful tool and reference where building SMART applications.
With our application, a developer has access to a variety of JavaScript code snippets, copy them, edit and play around with them, and use them to not only become more familiar with FHIR and the SMART JavaScript client, but to help build their own SMART applications. Furthermore, administrative users can also
create and add their own code snippets to the library.
COMP0016 Team 2: Ralf Yap, Qinyi Tang, Ziyang Dong | Client: GOSH DRIVE
Requirements
Modularized features/functions that can be implemented by a user in their SMART app
Code snippets Description of each features Demo feature Ability to copy/edit code snippets Ability to combine functions User accounts and authentication to save code snippets
Feature curation – allow admin user to add new modules or edit existing ones
Key Features
UCL Internal CodeHealth-008-19
Abstract Requirements
Our Working Product
Solution Technological Structure
Commissioned by GOSH, our project aims to create a prototype of a
system where a wearable device is attached to the discharged patient. In
addition to collecting health data from the wearable itself, it also collects
data about the environment using separate sensors. These combined data
allow for more meaningful analyses, such as when a patient has a seizure,
the doctor is able to look at the info of the environment at that time, and
possibly determine the cause of the seizure (e.g high light & humidity).
•An IoT healthcare monitoring package
•Wearable device (Android WearOS)
•An array of different sensors that can be added or removed from a room,
depending on particular needs.
•Wearable device attached to the patient’s wrist, like a smart watch.
•When the patient is in proximity of the sensors, data from both the
wearable device and sensors will be sent to a cloud based system in real
time, where it can be further analysed in greater detail.
Wearable Sensor
Collects Heart Rate
Collect Data From
Environment
Historical Graph
Analysis
Strong User
Authentication
Web Application
Control Center
Azure Services
Team 3
Jiahui| Xin Deik | Adamos
eVitals Healthcare
Monitoring Solution
Health-009-19
IXN – SOTA/Jibo
as a patient
User Interface
Client: Great Ormond Street Hospital
Team: Phu Sakulwongtana, Cao Khanh Nguyen, Klajdi Lamce
Phone
Jibo
Computer
System
API
Open
Health DB
DialogFlow
API
Machine
Learning
Key Features
• Virtual patient imitates real patient• Personal login details with progress tracking• Provides feedback on disease matched with symptoms• Experiment new chat-bot using State-Of-The-Art Machine
Learning Models1
• Easier Extensible with Micro-Service Like Architecture
Abstract
When training medical personnel, one of the most important goals is for them to have the ability to interact and
communicate with patients in order to understand their condition. However, an approach consisting exclusively
of training with real patients has some restrictions including the availability of both parties and ethical issues.
Here, we present an extensible platform for people in the medical profession to practice basic interaction with
patients, whereby we replace real patients with a chat-bot. The users will be able to interact with a bot to
complete a certain goal, in this case, to diagnose the disease which the bot might have. We employ a powerful
differential diagnostic tool, Isabel, to act as the guidance system for users to measure their own performance
empirically. Furthermore, we explore the design of the chat-bot and propose a mix between a state-of-the-art
neural-network architecture with a rule-based system, powered by DialogFlow.
1, 2Attention Is All You Need, https://arxiv.org/abs/1706.03762
3
ISABEL: a web-based differential diagnostic aid for paediatrics: results from an
initial performance evaluation
Advisor: Dean Mohamedally, Yun Fu,Graham Roberts/Gemma Molyneux, Neil Sebire
Softwares Architecture
Isabel
API
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Front-End: Used React.js for the design of the dashboard, as
it is a very simple library that simplifies the creation of single
page application, making it fast, responsive and user-friendly.
Back-End: Used Django as our back-end solution as we found
it has the perfect balance between having secure and easy
set up as well as freedom to add our custom functionality.
Sensor-Hub: Variety of sensors were used with a number of
Raspberry Pi’s to implement the network of sensors that
collect the data.
HEALTHCARE SENSOR FUSION HARDWARE
Team 05: Nikolay Bortsov | Alexandros Frangos | Ahmed Fawzy
Client Names: Neil Sebire | Sue Conner | Gemma Molyneaux
GOSH DRIVE
System ArchitectureAbstract
The problem we were trying to solve was the identification of
the normal state for a room using variety of sensors. We
have set up the sensors with raspberry pi that communicates
with the backend using an API. The data from the sensors is
then displayed on the dashboard where the medical staff can
control the raspberry pi sensor hubs and record medical
procedures. The data from the sensors in also used in variety
of algorithms that work out the optimal condition for the
room to be operated in. In case of a bad entry the dashboard
flags that the room’s condition maybe not be the best on the
dashboard. We hope that with this technology, issues from
external factors can be found and prevented earlier to
improve the success rate of medical procedures.
Evaluation
The system is comprised from 3 parts: Dashboard, Sensor Hubs
and Learning algorithms. We tested the system extensively,
covering a large number of extreme conditions, placing sensors in
different places and situations. The system works flawlessly
without bugs and with such technology, problems occurring from
external factors can be prevented earlier to improve the success
rate of medical surgeries.
Future work
The sensor hub system could be made more scalable by using
wireless sensors as well as use easier sensor integration. Moreover
algorithms used for the learning of data, could be further
developed and analysed to work out perfectly the optimal
condition for the room to be operated in.
Displaying sensor
data graphically
Start an event for a
specific patient
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Computer Vision for Medical Instrument Detection
Team 6: Benedict Chan, Shirin Harandi
Supervisors: Dr Neil Sebire, Gemma Molyneux
Abstract:With recent advances in Computer Visions, current technologies are able to
identify vast numbers of objects in a wide variety of scenarios. By utilising
such technologies, we attempted to train machines to detect and identify
different medical instruments used in clinical environments. We wanted to
create a system that monitors medical instruments being used in operating
theatres.
The system will be able to correctly identify and display the different
instruments that are present in the operation tray, thus allowing all staff to
gain a clearer overview of the operation at any given time. By gathering this
data, complete operation summaries and timelines can be produced. All of
this real world data can be analysed and shared to all medical personnel to
learn and improve their skills. We hope our system will open up avenues for
object detection in the medical field and allow the field to advance further.
Requirements:• Unique logins for each doctor allowing operations to be
grouped together
• Emergency login for quick access to application
• Live video feed with detected instruments being identified
and highlighted
• List of all instruments used in the operation and the live
status of these objects
• Check that all instruments are present after the operation
• Full summaries can be seen for all operations
With GOSH Drive
Key Features:
Simple User Interface
Data Analytics
Cloud Based API
Live video feed with status of objects used in the operation
Health-012-19
INDUSTRY EXCHANGE NETWORKIXN – Developing Accessibility Technology for the GOSH Sound and Sight Hospital
Great Ormond Street Hospital
Harry Thomas, Xingyu Liu, Sidak Pasricha
Neil Sebire, Gemma Molyneux
Our project is to produce a mobile method of indoor navigation, forpatients of the Great Ormond Street Sound and Sight Hospital. We
hope to improve safety and patient independence within the hospital
by allowing users to navigate without the aid of parents or staff. The
system will allow users with hearing and/or sight loss to select adestination using an Android phone application, and receive turn by
turn directions through the use of haptic feedback. In our specific
deployment, we will be using the NTT Data supplied Buru-Navi to
provide haptic feedback to the user. However, we will design thesystem to allow for alternative output devices to be used.
Abstract Key Features
An easy to use phone
application, with 'Easter Eggs',
will make the experience
enjoyable for children.
Patients with either hearing or
sight loss will be able to easily
select their destination by voice
or touch.
Administrators can see a
heatmap of each location,
showing where users commonly
visit.
Using haptic feedback provided
by a Buru-Navi, we will guide the
user turn by turn to their desired
destination.
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INDUSTRY EXCHANGE NETWORKOptimising 111NHS DigitalGun Woo Park, Mohammed Chouman, and Ziying Cheng (TEAM 10)Joseph Connor – NHS Digital (Client)
Automating call quality analysis with
data samples
POC for prioritising needs by queuing of
the calls
Identifying triage outcomes from the
process pipeline for automating care
advice
References
AbstractThis project developed a toolkit which can
help 111/999 (emergency telephone
number) call handlers to optimise their call
handling practices for the NHS 111 and
999 services.
This is achieved by solving three problems
by the provision of tools to analyse : call
content, queuing optimisation and an
automated care advice system. This was
done using various natural language
processing techniques from Azure
Cognitive Services, IBM Watson, Google
Cloud and number of machine learning
techniques.
The final outcome of the project is an
application that runs on Windows, Linux
and MacOS platform.
LDA/t-SNE
This method enables topic extraction
and clustering. The program visualises
clusters of topics which is easy to
interpret.
[1] Optimising 111, Park, G. et al, 2019
[2] Bayesian Updating with Discrete
Priors, Orloff, J. et al, 2018, MIT
[3] Latent Dirichlet Allocation, Blei, D. et
al, 2003, Journal of Machine Learning
* Further references are provided in [1].
Key
Requirements
111
Call Handler
I want to…
• Automate the call
quality analysis
• Queue the calls
• Give care advice
efficiently
Image © 2019 Wikimedia, Microsoft,
Apple and UCL Team 10
HTTPS
User
Classifier
LDA/t-SNE
Ranker
Trigger words detector
DESKTOP
System Architecture Diagram Health-014-19
INDUSTRY EXCHANGE NETWORK
IXN – Interpreting Black Box Algorithms for the NHS
Company: National Healthy Service
Author: Ayushmaan Seth and Zhong Yi
Client: Joseph Connor
Abstract
The aim of the project is to interpret and explain in lay terms the various black-box algorithms and artificially intelligent models used in healthcare, especially the NHS.
The tasks are to explain why a decision was taken, what could have happened if the inputs were something else, how close were the inputs from the decision being flipped and how can we audit the model to make it suitable for the real-world data.Our solution was designing a web-app integrated with TensorBoard, Google's What If tool for explaining machine learning models as well as combining research projects such as LIME to perform a comprehensive analysis. This will be helpful especially to the NHS since after the enforcement of GDPR it is necessary to explain the outputs of a model.
Key Features
Sign up/Log inUsers can have their personal account and data storage for free & safety.
Personal DatasetUsers are able to upload their special dataset as the training dataset.
VisualizationOur application can analyze the dataset and explain the prediction in simple way.
Decision AnalyzationUsers can see the changes of predictions with different initial inputs in real time.
Key Requirements Solution
Main features need to be accessible for all potential users.
The script should be able to adapt any input dataset from users
Visualization results must be clear to the general public as well as to the NHS staff
Our analyzation and visualization services are built on Web Application by Flask
Our scripts based on Python can adapt different kinds of dataset flexibly
The visualization results from Lime and Tensorboard are comprehensive for public
Log in/Sign up Page Lime Prediction Page User Customization Page Tensorboard Visualization Page
Health-015-19
v
TEAM 12 – COMP0016 Systems Engineering
NHS AI PlayGround – AI Testing Platform
The aim of the project is to provide a secure and comprehensive web platform controlled by NHS for their clinicians to post challenges which are solvable by AI. The developers can work on the challenges on the platform itself and thereafter submit their solutions. Solutions will be evaluated by the clinician and ranked according to its accuracy.
The platform is a Django-based web project which are then further split into smaller Django applications e.g. challenges, solutions, users etc which handles different areas of the project. The on-site coding environment is provided for using JupyterHub and Azure Kubernetes Service is used to manage the hub itself.
The platform has additional features such as a discussion page for developers to interact and a tutorial page. The final product will be deployed onto Azure which will then be transferred to NHS for their usage.
Team Members (Team 12):
Haixiang Sun, Wei Tan, Zixuan Wang
Example of a challenge page
The discussion page for developers to share ideas and questions
1. User authentication and personalisedprofile
2. Differing functionality and interface for the two different user types (clinician & developer)
3. Ability to create challenges and upload data for clinicians
4. Provision of on-site coding environment (Jupyter notebook) for developers
5. Discussion page for collaboration and interaction between developers
TECHNOLOGIES USED
DjangoThe web application itself was developed using the Django framework. The tools used for front-end development are HTML and CSS while the backend is largely done using Python.
The application is connected to a MySQL database.
JupyterHub & Azure Kubernetes ServiceAnother major feature of the application is the provision of an on-site coding environment in the form of a Jupyter notebook.
The notebooks are spawned and configured using JupyterHub which is in turn managed using Azure Kubernetes Service.
POTENTIAL FUTURE DEVELOPMENTS
Client (NHS):
Joseph Connor
Advisors:
Dr Graham Roberts, Dr Dean Mohamedally, Dr Yun Fu
KEY REQUIREMENTS
1. NHS clinicians can create challenges and
specify challenge requirements such as
preferred algorithm and prize money
2. Algorithms can be tested on the platform
i.e. algorithms can be run on-site
3. Solutions can be submitted to the platform
to allow for evaluation by clinicians against
testing sets
The Jupyter notebook provided spawned using JupyterHub
Image Copyrights:©iconfinder
1. Currently, the site relies on clinicians
evaluating the models with external tools and
marking them manually on the solution page.
Such tools could be embedded on the
platform to check the accuracy of the
algorithm once the developer has submitted
his/her model, with the value displayed right
after automatic evaluation.
2. Developers can create models with only
Jupyter Notebook on the platform. Other
methods such as RMarkdown scripts could
be added to the platform in the future as
alternatives.
3. The tutorial section could be expanded to
allow developers to create their own tutorial
posts. Also, an introductory guide providing a
walkthrough of the features the platform
could be created to offer a better user
experience.
ABSTRACT KEY FEATURES
Health-016-19
Pregnant women with a high concentration of protein in urine, hypertension, or diabetes are advised to
frequently visit the hospital for testing to monitor for development of any complications, which can cause
anxiety for patients and have significant cost implications. Our project aims to mitigate the financial
implications and time consumption of this process by extending the Hampton app to allow patients to record
and monitor blood glucose and urine dipstick results through a mobile app while displaying these values to
doctors through a web app for their evaluation. The app will use image processing to analyse urine dipstick
results through pictures and include a chatbot to allow users to discuss their results. It will also alert patients
and doctors to abnormal results and advise patients to consult their doctor. Overall our app is intended to
make this frequent process efficient, cost-effective, and stress-free for both patients and doctors alike.
IXN - INDUSTRY EXCHANGE NETWORK
Client: Trakka Medical - Dr. Asma Khalil
Team 13: Yifan Liu, Zhizhe Xu, and Yomna Ghannam
Abstract
This solution should be integrated with the existing Hampton
Medical solution in order to provide one holistic solution for patient and
doctor use. Future iterations of this project could include support for
the analysis of multiple types of urine dipsticks and extend the usage
of the chatbot to answer questions beyond the frequently asked
questions. The solution may also be expanded to support the
monitoring of other information, such as sleep, water, or
macronutrients, to track the overall health of the patient during their
pregnancy. The app could then be extended to provide feedback such
as reminding the patient to drink water. These additions would allow
the app to serve as a single source for monitoring all the information
that may be important to track during pregnancy.
Future Work
Mobile Application Web Application
Upload new blood glucose/urine
protein test results
Check previous test results and
view them with a line chart
Delete any results that are
accidentally uploaded
Automatically detect urine protein
test results with image
Be alerted when test results are
out of normal range
Chat bot which helps answer
frequently asked questions
View patient data uploaded from
mobile application
Add and modify patient information
Be alerted when patients’ test
results are out of normal range
Trakka Medical Blood Glucose Monitor App for Pregnancy
Key Features
The designed solution consists of a mobile application that is intended for patient use as well as a web
application that is designed to be used by clinicians. The mobile application, which is built using Xamarin,
uses image processing to detect the results of urine dipstick tests through a picture taken by the user.
Moreover, the mobile application displays the blood glucose and urine test history to the patient both
numerically and graphically, alerts them to consult their doctor when their test results are abnormal, and
includes a chatbot which patients can use to respond to some frequently asked questions. Microsoft Azure is
used to host the website designed for use by clinicians as well as the SQL database that contains the
patient data. The website, which is built using ASP.NET, allows clinicians to view the test data entered by
their patients both numerically and graphically as well. Through the website, clinicians can add and modify
their patient list and their information and be alerted when patients input abnormal test results. The two apps
in conjunction allow patients and clinicians to closely and efficiently monitor test results.
Solution
Technological Structure
Blood Glucose Result
Submission PagePatient Information Viewing
and Editing Page
Front-End Back-End Data Store
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Health-018-19
Abstract
INDUSTRY EXCHANGE NETWORKAN IOT-ENABLED CROSS PLATFORM APPLICATION FOR GLUCOSE AND BLOOD
PRESSURE MONITORING FOR PREGNANT WOMENTRAKKA MEDICAL, GOSH
YANKE ZHANG, SONIA SHAH, YUSI ZHOU
DR. MOHAMEDALLY, DR. FU, ASMA KHALIL
Pregnant women and doctors are continually trying to
battle pre-eclampsia development by the continuous
monitoring of blood pressure and glucose levels. However,
experience has shown that it is difficult for the patient to
take these readings in the hospital on a daily basis.
The development of this cross-platform application
provides a new care pathway that gives the patient
flexibility and comfort to measure these readings on their
own and be notified when readings present a potential risk.
The application collects measurement readings from the
respective measuring devices via Bluetooth and uploads
them to a cloud database for monitoring by doctors,
without any cables or manual input. Patients are reminded
to measure the blood pressure and glucose levels at certain
times of the day and are notified of the readings obtained
directly on the application. This has been achieved through
the Ionic frameworks and firebase cloud technologies.
•Automated data entry via Bluetooth
•Integrated communication between application and cloud
database
•Instant notification and reminders to take readings
•Android and IOS platform friendly
Key featuresKey requirements
• Able to automatically receive input data read
from respective measuring device via Bluetooth
• Display collected data on the mobile application
• Able to upload the data to hospital system
database for monitoring by clinician
• Alert/pop up notification to patient when data
readings out of range is collected and doctor
contact details given
• Automatic notification alert/pop up to doctor for
data reading out of range and
• Doctor’s contact details provided
A scalable back-end data engine which distributes data
processing among a cluster of machines
An intuitive, generalized API allowing researchers to efficiently
interact with HaMpton data to run machine learning models
3A front-end web app visualisation which allows authorized
personnel to query and upload medical datasets
2
1
PEACH Engine for HaMpton Pregnancy
Bridging the gap between medical and data science professionals
Our task…
Pre-eclampsia is a leading cause of stillbirths among pregnancies, whose
early detection is an ongoing challenge in the field of Maternal-Fetal
Medicine. In the past year, Prof. Asma Khalil’s “ HaMpton Medical” app
has collected blood pressure data of pregnancy patients with the hope of applying ML to develop predictive models which can lead to
breakthroughs in early detection of pre-eclampsia. This project acts
as a preliminary first step to this ultimate goal by developing a
scalable data engine to house HaMpton’s growing dataset for
medical and data professionals alike, complete with generalisedAPIs to query datasets and features such as visualisation and
data formatter for machine learning readiness.
Our Solut ion…� Back-End data engine built on a Kubernetes computer cluster
running Apache Spark for data processing
� Easy-to-use APIs which process Azure Cosmos database requests for authorized data scientists
� Front-end web application for interactive data visualisation for medical specialists build on Django and Chart.js.
Addit ionally:• Filtering system for selective visualisation and query
• Hampton data cleaner for machine learning readiness
TEAM 15: Wiryawan Mehanda | Max Bert field | Selena Li
In collaborat ion w ith:
Key Features
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Health-020-19
NHS Surgery Camera System for GOSH and St Georges
Team 20: Kyla Aguillo, Venet Kukran, Kailun Shen
Abstract
Often, it is the case that medical students are unable to find sufficientresources from which they can learn the intricacies of surgery, without
having to struggle to find a surgery which they are able to watch in person.In order to better facilitate the learning of trainee surgeons, we aredeveloping a web application which can then be used to store and easilyaccess surgical video. The footage captured, using the Microsoft Kinect, willalso be reconstructed to deliver immersive, 3D views of surgery, in order toproduce the best possible learning resource for students. Various featureswill also be implemented to make the video finding and watching process asstreamlined and intuitive as possible, to produce a platform which can help
develop these students into the best surgeons they can be.
View all accessible recorded videos with information
Create video timestamps using speech recognition during recording
Play both 2D and 3D recorded videos
Key Features
Main page
Record video page Watch video page
Database
Save video page
LocalFile
upload
download
St George’s Hospital
GOSH DRIVE
Record and store videos
Key Requirements
SHARE
STORE
RECORD
Use Kinect camera connected to the web app to record surgeries with timestamps
capabilities
Recorded videos will be stored in 3D along with timestamps and video descriptions
Access to videos can be given to students
Health-021-19
VirtualRehab Hands: Fine Motor Rehabilitation Games
Group 26: Weixi Zhang, Yun Fang, Carlton Ji
Client: Evolv Rehabilitation Technologies
Stroke is the leading cause of adult disabilities and affects
around 100,000 people a year in the UK alone. VirtualRehab
Hands is a suite of modules that addresses and enables the
practice of fine motor skills in the hand area. Our project
pertains to the Exergames module - utilizing the Leap
Motion Controller to produce games that have a focus in
making repetitive exercises more engaging for the user. We
implemented this in the form of an endless runner style
game, using Unity and C#. By creating a fun, interactive and
engaging exergame, we expect an increase in player
enjoyability, the length of time before being bored and most
importantly, the effectiveness of hand rehabilitation.
Abstract
Key Requirements
A fun game that can be played by stroke
patients
Using Leap Motion sensor to detect hand
movements
Supporting multiple hand gestures for
creative game controls
Allowing modifications of dynamic game
elements from VirtualRehab, e.g., target
locations, target numbers and time
allowed to hit the target
A striking aesthetic style
Key Features
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Obstacles to be overcome by specific hand movements, with adjustable difficulty
Changeable game map/theme
Scorer
Target number indicator
Visualiser of player’s hand
Character controlled by player’s hand
System Architecture
openEHR Clinical Knowledge
ExplorerSupervised by Yun Fu UCL Internal Code:
Abstract:
Clinical data is usually distributed in different repositories
called Clinical Data Repositories (CDRs). openEHR is an
open standard that allows clinicians to develop technical
models of clinical information requirements that can be
rapidly deployed to vendor-neutral datastores to underpin the
data storage and querying requirement of a new breed of
clinical/patient facing applications. Some CDR vendors
provide additional tooling that allows developers to easily
browse the data repositories and build queries. Although
there are some open-source CDRs available, they do not
have any current equivalent tools.
Key Features:
Write AQL queries to return health records from CDRs.
Execute AQL queries onto multiple CDRs at once.
Create a single federated table of results from multiple
CDRs.
Easy-to-use GUI built with Electron.
Upload templates found online to multiple CDRs at
once.
University College London | Systems Engineering 2018/2019 | Team 27 | Christian Martin Rios, Daniel Min, Leo McArdle
Working on:• Displaying results in a more user-friendly
manner.
• List templates of multiple CDRs.
• Session-based authentication system on
CDRs.
• Federating results of AQL statements from
CDRs.
Health-023-19
IXN – Exploration of 3D clinical models manipulation
Samuel Bouilloud, Yue Wu, Chris Obasi / Client: Ben Park - Sopra Steria
Key Features
The ability to use gestures for natural
interaction with 3Dmodels inMixed Reality
Guidelines for gestures and manipulation of
objects inMixed Reality
Ability to strip layers of the 3D models, and
place markers on them
Ability to use haptics for a better
interaction experience
Our Technologies
Evaluation
● All key functionalities have been implemented,
but a lot can be done in terms of performances
and advanced features
Future Work
● The integration with the HoloLens 2
● A full working “co-op” mode, with users
manipulating the same objects in real time
The Main Screen in Mixed Reality, where the
user can manipulate the object via gestures
(move, rotate, zoom, etc); or enter the Menu to
choose an object / access the documentation.
The Documentation for each gesture, with some
explanation and videos. A training mode and
tutorial are also available, for the user to get
used to the different devices.
Abstract
This project, in collaboration with Sopra Steria and Great Ormond
Street Hospital, is about creating a way for surgeons at GOSH to
be able to simulate operations. The goal here is to increase
security and efficiency of medical interventions. In addition,
medical students could also use it, as a tool to learn and test their
skills/knowledge.
We aim to integrate the Microsoft HoloLens with the LeapMotion
(hand-tracking device) and the Buru-Navi (device which guides the
user with vibrations) to manipulate 3D organ objects in Mixed
Reality.
We successfully integrated the HoloLens with the devices,
allowing users to naturally manipulate different 3D models, and
being able to place markers, find them using the Buru-Navi, or
even strip layers of the objects. In addition, a full documentation is
available, including a tutorial and a training mode. Health-024-19
Health-024-19
HoloHandClient: KazendiTeam 33: Farid El-Aouadi, Cyrus Horban, Sehej Sethi
Abstract
HoloHand is a mixed reality project
using the Microsoft HoloLens. Using the"research mode" we have made
breakthroughs in live hand trackingusing the HoloLens. The application
intends to deliver an experience whichwalks the users through the hand
segmentation process, allowing them touse finger gestures to paint in
augmented reality.
Key Features
Access Data Streams in the Microsoft HoloLens.
A clear UX design to walk the user through the hand segmentation process.
The functionality to allow users to
use finger gestures to paint in augmented reality.
Hand Tracking Using
HoloLens
Augmented
Reality Finger
Painting
Key RequirementsBold text implies the "must have" requirements
• Ability to access data sensors• Track individual fingers• Overlay stylised hand
instructions• Painting using finger gestures
Health-025-19
TitleMedical AR Object RecognitionCOMP0016 Team 34
Joseph Halse
Duncan Rowe
Xuanwei Chen
AbstractThere is a lack of hands-free technology in the health sector. To combat
this issue, NTT Data has decided to create a hands-free prototype that
uses machine learning and augmented reality to provide instructions to
the user. While wearing Epson smart glasses, our machine learning
algorithm will identify key medical objects and pull up instructions on
how to use these objects. The user will be able to choose which
instructions to view by taking advantage of the voice-control option
allowing them to work on the machinery while viewing the instructions.
User
friendly
Time
saving Interactive
Hands
free
Informative
Real
time object
recognition
Voice
recognition
Health-026-19
AR Portal for GOSH Patients
Client: NTTData, GOSH
Team member: Yin Long Ho, Chirag Hedge, Haonan Zhang
Customizable Augmented Reality Experience
Abstract
The aim of the project is to help reassure
young patients before they have an
operation. Children are more often afraid of
the unknown, hence our solution is made to
help children familiarise themselves with the
medical environment beforehand.
Our project consists of two parts: a web
application where users can customise
augmented reality (AR) rooms, and a mobile
application where the patients can enter an
AR portal and then explore the
aforementioned rooms, whilst interacting
with objects. Both applications are designed
to be easy to use for users without any
technical background.Key Features
- Web app
- Customize AR room and fill it with objects.
- Add object interactions.
- Public/private sharing option.
- Authentication system.
- Mobile app
- Place AR Portal in the real world.
- Enter the AR world by walking through the portal.
- Drag and move objects around.
- Play 360-degree video.
Enter the generated code for
the room in our mobile app
Walk into the AR portal and
explore the room in real
world.
Future work
Whilst our applications are made in mind of
the healthcare sector, our completed project
is a generic design that can be built upon in
the future, in various industries and sectors,
such as for training new staff and leisure
activities.
Health-027-19
HoloLens to Support Clinical Teaching and Training
AbstractWe have created a HoloLens application to assist medical
students and trainees with their education by facilitating mixed reality. Our application is able to extract medical terminology while a senior doctor speaks and provides immediate
information in front of the junior doctors eyes without distracting them with extra screens.
Our Solution• Create immersive three-dimensional content using
Unity and the HoloToolkit• Extract medical terms with the NLP Lexigram API
and provide explanations and diagrams of terms
• Show graphs and patient data from JSON files by utilising the Mixed Reality experience of the HoloLens
Interactive medical application for HoloLens
Real-time extraction of medical terminology
Customisable UI using gestures, voice control and spatial understanding
Key Requirements
Voice commands for navigation and interaction
Natural Language Processing extracting medical terminology from speech
Holographic display of data and User Interface according to Mixed Reality best practices
Key Features
Patient medical data retrieval from files to HoloLens interactive experience
Team 7
Petros Xenofontos, Elena Aleksieva, Amy JeffcoateClients
DRIVE (GOSH), NTTData, Microsoft
Health-028-19
WELLWELLWELLWellbeing Monitoring App
University College London | Team 9 | Zuka Murvanidze, Nanxi Zhang, Azizan Wazir | 2018/2019
Historically, there has been no way of accurately tracking the mental wellbeing of
patients. Triage and resource allocation is difficult as a result of this lack of information,
and as such, the NHS Wellbeing App was designed to collect data in a non-intrusive
and subtle manner.
WellWellWell is an app that tracks a user’s phone usage statistics to determine their
wellbeing following an approved NHS framework - the 5 Ways to Wellbeing. The app
tracks the user’s pedometer data (number of steps taken), social media and other
telecommunication app usage to generate a wellbeing score, which is indicative of how
closely the user is following the NHS framework.
Key Requirements:Project Abstract:
UK Wellbeing data visualized according to postcode area addresses
Compatible with 95%
of Android devices
SQLite for local data
management
MySQL nationwide database to
store anonymously shared data
Node.Js back end for the web
app that processes and
visualizes the shared data
Using ML.NET and Tensorflow
to predict wellbeing scores
Home page
Data processing and
classification, data sharing
StatisticsGenerated PDFLive Monitoring
Key Features and Technologies:
Full anonymity of all user
shared information
System Architecture
Design Machine Learning
model to predict well being scores
Manage data locally to give
users full control of their data
Create user friendly android
application for data gathering and sharing
Enable data sharing via
generated documents
Design network for
anonymous data sharing and give an user option to opt in or
out
Display user history
comparing current to previous data
Data sharing via pdf and
txt documents
Health-029-19
IXN – CRF Staff Activity Tracker
Great Ormond Street Hospital + DRIVE
Team 17: Darren Ko & Jake Currant
GOSH Clinical Trial employees currently employ basic excel spreadsheets
to log and analyse staff activity data over designated time intervals.
This suffers from inaccurate data collection and low user satisfaction
stemming from inefficient logging processes and being constrained to PC
desktop-reliant data input. The project improves and streamlines the
process by moving data collection to mobile devices. Erroneous user input
is greatly reduced and activity logging is made more
efficient as minimal user interaction is required to input data.
Simultaneously, the project improves support of management staff
workflows, with data handling and analytics output delivered via a
companion web page for administrators’ and business managers’
ease of use.
Abstract Technological Solution
Mobile App (Ionic)
Web Client (HTML, CSS)
Web Server (Django) Database (MySQL)
Requirements and Implementation
Administrative Staff
• Assign specific bands of staff individual
activities
• Generate analytics and information
procedurally, and export them
• Manage and oversee use across all user
types
End Users
• Enter activity data on the go as fast as
possible, according to trial and time spent
• Manage all allotted tracking days
• Ensure ease of use with help features and
minimal required input
• Implement reminders and notifications
In association with Lorraine Hodson, Gemma Molyneux, Daiana Bassi, Christy Rowley
Supervisor: Dr Yun Fu
Health-030-19
A disease is classified as rare if it occurs in fewer than 5 in
10,000 people. Due to the low incidence, research on rare
diseases is limited and it is hard for doctors to identify them due to
the lack of precedence cases. 75% of rare diseases affectchildren and 30% of patients will die before their fifth birthday. To
improve that situation, in 2015 GOSH opened the world’s first
research centre for rare diseases in children. Our project aims to
build on that research to create a comprehensive educational
platform that allows trainee doctors to view anonymized clinicalobservations of patients with rare diseases. The platform will
improve their knowledge and training experience and prepare
them better to identify and recognize rare diseases in the future.
Rare Disease Repository
Team 03: David Elston, Georgi Krastev, Max von Borch | Client: GOSH | Supervisor: Dr. Yun Fu London, March 2019
Key Features
Account creation only
allowed for NHS
approved domains
Registered doctors can
upload cases of rare
diseases
Administrator will check
cases for anonymity
and correctness
Approved cases can be
accessed by public
Registered users can
bookmark cases for
later use
Registered users can
contact case authors
for questions or
recommendation
Solution
Web-based application developed with python
and django
Back-end hosted on Microsoft Azure using
PostgreSQL as a
database
Responsive UI with NHS look and feel implement-
ted using HTML,
Bootstrap and CSS
Requirements
Intuitive functionality and ease of use
Guarantee anonymity of medical data due to
data protection
Case authors can upload media files
along cases
Abstract
Health-031-19
Client
Musgrove Park Hospital is part of Taunton and Somerset
NHS Foundation Trust. It is a successful District General
Hospital, the largest in Somerset; serves a population of
over 340,000 and with an excellent reputation for
providing a comprehensive range of medical, surgical and
specialist services.
Background
The Pre-Operative Assessment Clinic (POAC) Working Group is looking to utilise a digital
platform to screen patients prior to their operation to identify those that are more
complex and who may need a POAC appointment/multi-disciplinary team (MDT)
involvement.
Requirements
Health Assessment – assessment questions
History – patients can retrieve previous answers
Profile – patients can modify personal details
Help – FAQs
Feedback– patients’ feedback on the app
View Patient – users can search for patients’
responses
Assessment Dashboard – summary results from
the assessment
Feedback Dashboard - summary results from the
patient feedback
User Management – administrators can edit user
details and deactivate users
Question Management – users can modify the
questions
System Architecture
Mobile
App Website
Database
ServerHTTP requests
and responses
HTTP requests
and responses
SQL
Team 09Jonathan Choi | Sheng-Wen Huang |Nishchal Sen
IXN – Pre-Operative Assessment Clinic
Health-032-19
Health-001-19
INDUSTRY EXCHANGE NETWORK
Visitor Management App
Students: Benjamin Smith, Chao Ding (Team 8)
Company: Great Ormond Street Hospital, DRIVE
Clients: Gemma Molyneux, Daiana Bassi
Features
Ionic App
• During an event, visitors who have pre-booked can search for their
details in a list that is dynamically filtered based on text input.
• During normal office hours, visitors can quickly input their details into
the system so that they are stored whilst the visitor is in the building.
• Admin users can access the web admin panel or fire checklist from
the main page of the app.
Web App
• A list of current events, upcoming events or past events can be seen
and these are ordered by date and time.
• Clicking on an event in this list allows a user to see its details, modify
them and delete or close the event.
• New events can be added using a button on the same screen.
• A list of the visitors that are currently signed into the building can also
be accessed so that their details can be retrieved.
• Event or visitor data can be uploaded into the system or exported to
an email address in a .csv file.
• During an evacuation, visitors that are currently in the building can be
signed off on a fire checklist. This is persistent across all devices, so
multiple members of staff can check people off at the same time.
AbstractGOSH DRIVE regularly host events in their office space, such as seminars for IT and healthcare professionals. In
addition, a number of other visitors enter the unit during normal office hours. Currently however, management of
both event visitors and drop-in visitors is done using physical records. Therefore, it would be beneficial if this
process could be digitised. Digitalisation will improve the visitor sign in experience and staff can more readily
access event and visitor data when required. The goal of this visitor management app has therefore been to
increase the efficiency of the sign in procedure for visitors, as well as give the staff at DRIVE an easier way of
accessing and manipulating this event/visitor data. The system we have created involves four main components: an
Ionic app, which the visitors can use to sign in/out of the building; a web admin panel that allows staff members to
view, modify or export the data in the database; a node.js backend and a MySQL database (both of which are
hosted in the Microsoft Azure Cloud).
UI Design
Ionic App
Visitor Sign-out Page
Web App
Events List Page
System• Client-side Ionic app
and web interface.
• Node.js back-end on
NGINX server in
Azure Cloud.
• MySQL database
server also in Azure
Cloud.
Health-002-19
INDUSTRY EXCHANGE
NETWORK
Abstract
This project involves prototyping, designing and developing an
application, which will allow the Taunton and Somerset NHS Foundation
Trust to triage the patients to an appropriate specialist or specialists. This
would, in turn, ensure a higher efficiency and consequently shorter
waiting-times for patients to be assigned to either a dietitian, an
endocrinologist or another specialist where appropriate. The application,
apart from allowing the patients to track their weight, mood and other
factors is ensure that the patients are triaged efficiently, which will reducethe waiting time and thus provide a higher quality service.
Tech Behind
Admin Page
Server Database
Architecture
The project contains one mobile app
for patients and one admin website
for the management team. We use
node.JS to transfer the data between
the front-end of the application and
the database. The back-end is
deployed on Microsoft Azure.
COMP0067: Design (2018/19)Organisation: Taunton and Somerset NHS Foundation Trust
Client: Marie Little
Team 18: Damian Harateh, Wentian Fang, Demilson Fayika
Admin Website
Requirements
1. Will allow users to enter information about their diet.
2. Present a set of questions to the patient in relation to their
diet to determine the level of care they require.
3. Requires patients to enter their login details to use the app.
4. The app will provide doctors an update with information
about the patients’ health and medical questionnaire responses.
Phone Features:
Team 16 – Wilfrid Berry, Cecilia Pretus, Poyzan TaneliSupervisors – Dr Yun Fu (UCL), Daiana Bassi (GOSH Drive)
GOSH Drive Device Management System
The Key Features: Website & App
The Requirements
For the app:
• Use QR codes for easy check in/out and manual option
• Enter client details at check out
• Send confirmation emails to client and GOSH
For the website:
• Ability to bulk import device data
• Generate QR codes
• Add or remove devices
Abstract
“GOSH Drive is the new unit of Great Ormond Street Hospital that aims to
enhance the use of technology in healthcare, digitalise and transform the
existing systems to improve patient outcomes”(1).
Our client has a number of Samsung devices they lend out to students and other individuals. Manually managing and keeping track of the different devices can get messy. Our client wants a way to automate the management for the devices they lend out. We decided that the best solution was to create a mobile app to check-in/out devices and a separate website to allow bulk imports and manage all devices.
The Solution
• An easy to use app with a clean design to quickly check in/out devices, with the option to scan a QR code or enter manually.
• A flexible and customisable website with an easy to navigate dashboard that makes adding, removing and tracking devices easy and convenient. Select check in/out manually
or with QR codeEnter client detailsTrack and manage devices
from dashboard
The Technology
MySQL Database
PHP REST API
Azure Web ServerClient Browser (CSS, HTML, PHP)
Android App(Ionic 4)
(1) GOSH Drive Website
Health-003-19
Abstract
Often within medical research, clinical trials and studies are
required to test or evaluate new drugs against performance
requirements. The process of doing so currently involves
recruiting potential trial participants, and providing them with
information sheets to read and consent forms to fill out,
which are filed away, in case of the need to retrieve at a later
date. With our solution we aim to automate this process by
creating a web application that allows those authorised, to
create studies and relevant information sheets, and manage
the medical staff who have access to such studies and
information. We also aim to build a mobile app that allows
medical staff to present the information sheets and consent
forms to prospective participants live, allowing for information
sheets to be sent to participants email addresses, and for
signatures of consent to be stored in a remote database.
Evaluation
The implementation of the front-end pages for web application and the mobile app have been done using HTML and CSS, the mobile app done specifically using the Ionic Framework.
Addition of dynamic elements and behaviour such as, creating certain page actions given a certain button click, has been implemented using PHP. PHP allows for the addition of dynamic contents to the application that was created in HTML and embedding PHP into the static code is can be achieved more easily than other back end scripting languages. Furthermore, PHP is compatible with multipleplatforms and provides fast outputs, both of which are necessary features given the nature of the application.
The creation of the mobile app has been done using the ionic as aforementioned. Wireframes and basic designs had initially been made on Ionic Creator, hence made the transition to prototype more seamless. Ionic also allows for standalone UI design and creation without access to back-end solutions and therefore was a sensible choice.
The database management system used MySQL. It was chosen because of its good security profile as well as its capability for integration with Azure which we are using to host and deploy our applications.Thus far, we have created the web application pages that allow an authorised member of staff to create accounts for other medical staff and manage their access to certain studies. We have also created pages for the mobile application that allow frontline users to show forms that were created by authorised users on the web application
Future workFor the web application, we hope to augment the ability to create a form for a study, with interactive drag and drop features such as a text box, tick box,
smiley face (for children) and ability to add images to the form. For the mobile application we aim to finalise the ability to save participant signatures given on a mobile device, as a PDF to a remote database.
INDUSTRY EXCHANGE NETWORK
Informed Consent App
GOSH Drive
Dan Ward | Ross Murray | Azariah Kusi-Yeboah
Daiana Bassi | Gemma Molyneux
Client Browser (HTML, CSS, JavaScript
Web Server (PHP) Database (MySQL)
Health-004-19
UCL internal code
Abstract
Our client is entering the clinical trial phase of a new treatment for Osteoradionecrosis; a rare side effect that develops some time after radiation therapy has ended. It usually occurs in the lower jaw, or mandible. We provide a solution that will run concurrently with the clinical trial. The mobile client will allow collection of this data, previously done by a research nurse which is costly and impractical for patients. A Web Client provides admin services for the trial, allowing the set up, maintenance and data extraction from the trial.
INDUSTRY EXCHANGE NETWORK
RAPTOR – Patient feedback solution
Team 19: Samin Ahbab, Shan Pandya, Yusuf Sohoye
Client: Dr. Richard ShawSupervisor: Dr Yun Fu
Logos from company
and technologies used
Mobile Client
• Cross platform mobile app that allows patients to record their symptoms (Pain,
Swelling, Eating)
• Push Notifications to remind users to take the
survey every 10 days.
• Accessibility, varied education level and English speaking ability.
• Minimal time cost, the survey can be
completed in under 30 seconds
• Secured using a unique Trial ID and
authentication code for each user
• Documentation for information about the
current trial, always available.
Web Client
We developed a custom frontend to create a
bespoke solution to manage the dataflow of the
client. This was written in React and is a fully
responsive bootstrap led design. This handles the
needs of the nurse, and smooths business
processes for the NHS trial.
Backend
Node.js and mySQL is used at the backend.
A robust safety netting protocol has also been
designed which automatically flags up patients to
doctors if necessary.
Security considerations have been made and proper
authentication is required to access any routes on the server
Health-005-19
UCL internal code
Abstract
A web portal that allows nurses to easily conduct annual health questionnaires with CIC (Children in Care). Children can select skills that they themselves are interested in developing, and establish a plan with the nurse on how best to achieve their goals.
CIC can also track their progress of these goals over time, allowing for genuine personal development.
INDUSTRY EXCHANGE NETWORK
IXN – Looked after Children Health Plan
Company - North Tyneside CCG
Team - Christopher Pettinga, Nilayraj Patel, Anthony Williams
Client - Marc Rice
Setting personalised goalsCreate sense of achievement and self-
efficacy through progress tracking
Simple ranking system
Results easily shared with relevant peopleQuestionnaire design
Client computer Python (Flask) backend Amazon AWS RDS instance
Health-005-19
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