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Applying machine learning capabilities to wearable IoT devices for boxing technique management

Liffey Hall 2
Start (Dublin time):
Start (your time):
30 minutes


IoT devices are increasing in power and capabilities, now allowing developers to deploy machine learning models on the device. This talk will analyse a boxing training session with motion sensors onboard multiple IoT devices using TinyML: a TensorFlow-based framework. Ultimately, these machine-learning powered IoT devices provide feedback to boxers on their technique.

TalkPyData: Machine Learning, Stats


Internet of Things (IoT) devices are becoming more advanced through additional sensors, reduced size and increased computational power. In particular, this increase in computational power allows one to run previously-trained machine learning algorithms natively on an IoT device.

This presents an exciting opportunity: IoT devices often feature a variety of onboard sensors which can be used as inputs into a machine learning algorithm.

This talk will use the presenter's boxing training as a practical example of applying sensor data to a machine learning algorithm. In particular, this talk will demonstrate using motion sensor data obtained on an Arduino Nano 33 BLE Sense configured with TensorFlow Lite. This talk will discuss the entire analytical process from problem and data analysis through to algorithm training and deployment. It will also discuss boxing concepts and how these concepts are modelled in an IoT context.

The links to code are provided below:

The slide deck is available here:

The speaker

Anthony I. Joseph

Anthony is an Australian software engineer and mathematician. As a UTS MBT graduate, Anthony is the technology co-founder of a property and cycling-tech startup and enjoys teaching and learning coding with the Australian startup scene.

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