An adaptable cognitive microcontroller node for fitness activity recognition

TL;DR

This paper presents a low-power, adaptive microcontroller device using deep learning for accurate fitness activity recognition on wobble boards, achieving over 97% accuracy and significant power savings.

Contribution

It introduces a dynamically reconfigurable microcontroller-based system with a deep learning model for exercise recognition, optimizing power consumption in wearable health devices.

Findings

Power consumption reduced by up to 60% through adaptive reconfiguration.

Achieved over 97% accuracy in detecting specific exercises.

Demonstrated effectiveness on a custom dataset for fitness activity recognition.

Abstract

The new generation of wireless technologies, fitness trackers, and devices with embedded sensors can have a big impact on healthcare systems and quality of life. Among the most crucial aspects to consider in these devices are the accuracy of the data produced and power consumption. Many of the events that can be monitored, while apparently simple, may not be easily detectable and recognizable by devices equipped with embedded sensors, especially on devices with low computing capabilities. It is well known that deep learning reduces the study of features that contribute to the recognition of the different target classes. In this work, we present a portable and battery-powered microcontroller-based device applicable to a wobble board. Wobble boards are low-cost equipment that can be used for sensorimotor training to avoid ankle injuries or as part of the rehabilitation process after an injury. The exercise recognition process was implemented through the use of cognitive techniques based on deep learning. To reduce power consumption, we add an adaptivity layer that dynamically manages the device's hardware and software configuration to adapt it to the required operating mode at runtime. Our experimental results show that adjusting the node configuration to the workload at runtime can save up to 60% of the power consumed. On a custom dataset, our optimized and quantized neural network achieves an accuracy value greater than 97% for detecting some specific physical exercises on a wobble board.