LabelSens: Enabling Real-time Sensor Data Labelling at the point of Collection on Edge Computing

TL;DR

This paper introduces techniques for real-time sensor data labelling at collection points on edge devices, improving accuracy and efficiency for machine learning applications.

Contribution

It presents new labelling methods integrated into edge devices, along with a systematic comparison of neural network models and practical guidelines for deployment.

Findings

Achieved 68.5-89% accuracy with GRU models within devices.

Highest LSTM model reached 92.8% accuracy.

Provided field-tested guidelines for designing high-performance edge labelling tools.

Abstract

In recent years, machine learning has developed rapidly, enabling the development of applications with high levels of recognition accuracy relating to the use of speech and images. However, other types of data to which these models can be applied have not yet been explored as thoroughly. Labelling is an indispensable stage of data pre-processing that can be particularly challenging, especially when applied to single or multi-model real-time sensor data collection approaches. Currently, real-time sensor data labelling is an unwieldy process, with a limited range of tools available and poor performance characteristics, which can lead to the performance of the machine learning models being compromised. In this paper, we introduce new techniques for labelling at the point of collection coupled with a pilot study and a systematic performance comparison of two popular types of deep neural networks running on five custom built devices and a comparative mobile app (68.5-89% accuracy within-device GRU model, 92.8% highest LSTM model accuracy). These devices are designed to enable real-time labelling with various buttons, slide potentiometer and force sensors. This exploratory work illustrates several key features that inform the design of data collection tools that can help researchers select and apply appropriate labelling techniques to their work. We also identify common bottlenecks in each architecture and provide field tested guidelines to assist in building adaptive, high-performance edge solutions.