# PerceptionNet: A Deep Convolutional Neural Network for Late Sensor Fusion

arXiv: https://arxiv.org/abs/1811.00170 · HTML: https://mlsanity.com/paper/1811.00170/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition](https://mlsanity.com/paper/2410.10624/) (2024)
- [Human Activity Recognition using Wearable Sensors: Review, Challenges, Evaluation Benchmark](https://mlsanity.com/paper/2101.01665/) (2021)
- [MoRSE: Deep Learning-based Arm Gesture Recognition for Search and Rescue Operations](https://mlsanity.com/paper/2210.08307/) (2022)

## Abstract (arXiv)

Human Activity Recognition (HAR) based on motion sensors has drawn a lot of attention over the last few years, since perceiving the human status enables context-aware applications to adapt their services on users' needs. However, motion sensor fusion and feature extraction have not reached their full potentials, remaining still an open issue. In this paper, we introduce PerceptionNet, a deep Convolutional Neural Network (CNN) that applies a late 2D convolution to multimodal time-series sensor data, in order to extract automatically efficient features for HAR. We evaluate our approach on two public available HAR datasets to demonstrate that the proposed model fuses effectively multimodal sensors and improves the performance of HAR. In particular, PerceptionNet surpasses the performance of state-of-the-art HAR methods based on: (i) features extracted from humans, (ii) deep CNNs exploiting early fusion approaches, and (iii) Long Short-Term Memory (LSTM), by an average accuracy of more than 3%.