High performance computer vision on low performance hardware

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It has become widely accepted that machine learning can help with data analysis and conversion to knowledge.**In particular, studies have shown that machine-learning driven monitoring and analysis can increase athlete**performance and skills (goo.gl/rtHfhk). Technology (and specifically wearable technology) has become a**critical component of the sports industry. There are several companies that focus on wearable technology, but**their ability to leverage machine learning is limited by the computing hardware in these wearable sensors;**typically embedded micro-controllers and processors. Embedded processors are typically are limited by**memory and power consumption, making it unclear the how machine learning models can be deployed reliably**without losing any accuracy.**Form Athletica, develops wearable technology to help athletes collect information about their activity and**provide feedback to athletes. Currently, Form's wearable devices primarily collect and meter activity data, and**use offline machine learning to determine feedback to the athletes. The feedback to the athletes is slow and**lacks specificity due to the data from wearable devices being limited by the small amount of memory on the**device. Form is in the process of making their service real-time and moving the machine learning onto the**wearable device itself. Our work will seek to optimize and compress the machine learning models to run on the**wearable device and leverage the data already being generated by the sensors. Our primary goal is to develop**system optimizations and implementations that leverage data compression, model compression, instruction**layouts to enable the deployment of ML algorithms on embedded processors; prior work has largely focused on**resource hungry GPUs. Beyond testing its core functionality and implementation, we will also explore the**accuracy of the feedback provided by the compressed machine learning algorithms to athletes.