Development of machine learning algorithms for wearable devices - BC-393
Preferred Disciplines: Computer Science, Data Science, Machine Learning (Masters, PhD, Post-Doc)
Project length: 16-24 months (4 units)
Approx. start date: As soon as possible
Location: Vancouver, BC
No. of Positions: 1
Company: Form Athletica
Form Athletica is building a new revolution in augmented reality for sports. Run by a team of industry veterans with a reputation for creating amazing, industry-first products. This includes part of the team that introduced the world’s first consumer smart eyewear in 2010. Form is headquartered in Vancouver, BC.
Summary of Project:
Form Athletica is building a new revolution in augmented reality for sports, and we’re looking for a Data Scientist to join our rapidly growing team. In our system, sensor signals from the wearable device are analyzed to give real-time feedback to the user. This role is to expand its capabilities with research into "edge machine learning" -- machine learning prediction designed to run on embedded devices, and how this can complement big data techniques.
We need to compare the performance of different machine algorithms including deep learning on a labelled data set. Approaches could involve other signal processing and related techniques to improve accuracy of predictions. Work would focus on enabling on-line training to individualize performance for each user.
- Design and implement methods to improve prediction accuracy of machine learning algorithms
- Optimize machine learning algorithms for low-power devices
- Investigate both online and postprocessing approaches, with individual and aggregated data, to improve performance and accuracy
- Use statistical approaches to minimize the number of false positive predictions
- Optimize existing machine learning algorithms to improve performance, reduce execution time and cost
Expertise and Skills Needed:
- Ensemble learning techniques
- Deep learning
- General machine learning for classification
- Signal processing
- Firmware development (optional)
- Experience with IMUs, other wearable sensors
For more info or to apply to this applied research position, please