Risk Profiling
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Development Report
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Optimise
Sensor Input
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Engage Industry Leaders
Scope Sensor Outputs
Scope Integration options
Options for users to generate data
Options for cross referencing data
Inferences that can be drawn
Options for fusing data
Fusing data with non public data
Scope opportunities for machine learning
Machine Learning
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Most Appropriate ML model
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Write code to accept training data
Train ML model with data
Test validity, voracity and fitness of results
Compare & contrast inputs and outputs
Optimise ML and sensor input
Cross reference new and old data and inferences
Test applications affect on mobile phone use
Conclude efficacy of the approach
User Experience
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Involve users in the co-design of user interface
Walk partners through the resulting user interface
Present the user interface to those involved in the codesign
Optimise the user interface
Release the solution for testing within a control group
Review user statistics and feedback
Review the fitness for purpose of the user experience with partners
Document the learning points and Next Steps
Hours
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