# An investigation of simple neural network models using smartphone signals for recognition of manual industrial tasks

## TL;DR

This paper compares simple and complex neural networks for recognizing manual tasks in industrial settings using smartphone data.

## Contribution

The study demonstrates that simple feedforward neural networks can achieve high accuracy in HAR tasks while being computationally efficient.

## Key findings

- FNN models achieved accuracy rates between 94.28% and 99.19% in HAR tasks.
- FNNs offer faster training times and are suitable for resource-constrained environments like mobile devices.
- Simpler models can be used in cascading systems for real-time monitoring and classification.

## Abstract

This article addresses the challenge of human activity recognition (HAR) in industrial environments, focusing on the effectiveness of various neural network architectures. In particular, simpler Feedforward Neural Networks (FNN) are focused on with an aim to optimize computational performance without compromising accuracy. Three FNN configurations—FNN1, FNN2, and FNN3—were evaluated alongside the Convolutional Neural Network (CNN 1D) model for comparative analysis. The results indicate that the FNN achieved accuracy rates ranging from 94.28 to 99.19%, while the CNN 1D exhibited an accuracy of 98.12%. Despite the CNN 1D’s efficiency for real-time applications, the FNN’s fast training times and high accuracy make them particularly valuable in resource-constrained environments such as mobile devices. The findings suggest that while more complex models such Long Short-Term Memory (LSTM)-Auto-Encoder configurations, that have been tried by the same research group before, may offer better adaptability, simpler architectures can provide effective results in HAR tasks. Notably, these simpler models can be adopted in cascading systems operating online, serving as detectors of known activities for real-time monitoring and classification.