# A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices

arXiv: https://arxiv.org/abs/1909.12917 · HTML: https://mlsanity.com/paper/1909.12917/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2019.

## Built on by (most cited first, 7 of 7)

- [HARMamba: Efficient and Lightweight Wearable Sensor Human Activity Recognition Based on Bidirectional Mamba](https://mlsanity.com/paper/2403.20183/) (2024)
- [Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks](https://mlsanity.com/paper/2209.00839/) (2022)
- [LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification](https://mlsanity.com/paper/2101.02268/) (2021)
- [Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data](https://mlsanity.com/paper/2103.03836/) (2021)
- [Practical Insights on Incremental Learning of New Human Physical Activity on the Edge](https://mlsanity.com/paper/2308.11691/) (2023)
- [Non-stationary BERT: Exploring Augmented IMU Data For Robust Human Activity Recognition](https://mlsanity.com/paper/2409.16730/) (2024)
- [SPECTRA: An Efficient Spectral-Informed Neural Network for Sensor-Based Activity Recognition](https://mlsanity.com/paper/2603.26482/) (2026)

## Abstract (arXiv)

Human Activity Recognition (HAR) using wearable and mobile sensors has gained momentum in last few years, in various fields, such as, healthcare, surveillance, education, entertainment. Nowadays, Edge Computing has emerged to reduce communication latency and network traffic.Edge devices are resource constrained devices and cannot support high computation. In literature, various models have been developed for HAR. In recent years, deep learning algorithms have shown high performance in HAR, but these algorithms require lot of computation making them inefficient to be deployed on edge devices. This paper, proposes a Lightweight Deep Learning Model for HAR requiring less computational power, making it suitable to be deployed on edge devices. The performance of proposed model is tested on the participants six daily activities data. Results show that the proposed model outperforms many of the existing machine learning and deep learning techniques.