Wearable Technology for Ride Safety Evaluation
Developing a set of wearables to record g-force exerted on a ride participant exposed to 8 degrees of freedom
Status:
Published
Categories:
Development, Data Analysis, Research
Technology:
Python, Jupyter Notebook
Links:
TEAM:
- Alex Ferworn Supervisor, Computer Science
- Kathryn Woodcock Supervisor, Human Factors & Themed Entertainment Specialist
- In association with N-CART Labs, Thrill Labs Ryerson University
Overview
Overview
- Designed and developed a wearable architecture catered for ride safety evaluation - analyzing g-force, acceleration, and impact of a ride participant.
- Conducted research on existing/similar solutions, sensor systems and industry standards on human-factors, safety and ergonomics (ASTM and TSSA).
- Iteratively developed low-fidelity mockups into wearable prototypes considering sensor displacement, enhancing comfort, and ensuring technical sustainability in high-impact environments.
- Collected 40 runs on a Zip Line, cleaned and reformatted the data set based on industry requirements to analyze the rider’s movement, g-force and speed. Created data visualization models to assess results with stakeholders.
- TSSA Graduate Research Scholarship Recipient 2018.
Publications
Solution
Results
Learnings
You might Also Like

Data Engineering, Data Science, Data Analysis
Performance Forecasting
An annual deliverable to predict website traffic and set performance targets
Read More
Data Engineering, Data Science, Data Analysis
Script Automation
Automating data input, calculations and output to reduced time spent on reoccurring reports
Read More
Data Analysis
Data Science for a Non-Profit Organization
Volunteering as Manager of Analytics & Insights at Chic Geek
Read More