# Layer-wise training convolutional neural networks with smaller filters for human activity recognition using wearable sensors

arXiv: https://arxiv.org/abs/2005.03948 · HTML: https://mlsanity.com/paper/2005.03948/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2020.

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

- [Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances](https://mlsanity.com/paper/2111.00418/) (2021)
- [A Close Look into Human Activity Recognition Models using Deep Learning](https://mlsanity.com/paper/2204.13589/) (2022)
- [Compacting Deep Neural Networks for Internet of Things: Methods and Applications](https://mlsanity.com/paper/2103.11083/) (2021)
- [Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks](https://mlsanity.com/paper/2209.00839/) (2022)
- [Module-wise Training of Neural Networks via the Minimizing Movement Scheme](https://mlsanity.com/paper/2309.17357/) (2023)
- [Block-wise Training of Residual Networks via the Minimizing Movement Scheme](https://mlsanity.com/paper/2210.00949/) (2022)
- [Efficient-Husformer: Efficient Multimodal Transformer Hyperparameter Optimization for Stress and Cognitive Loads](https://mlsanity.com/paper/2511.22362/) (2025)

## Abstract (arXiv)

Recently, convolutional neural networks (CNNs) have set latest state-of-the-art on various human activity recognition (HAR) datasets. However, deep CNNs often require more computing resources, which limits their applications in embedded HAR. Although many successful methods have been proposed to reduce memory and FLOPs of CNNs, they often involve special network architectures designed for visual tasks, which are not suitable for deep HAR tasks with time series sensor signals, due to remarkable discrepancy. Therefore, it is necessary to develop lightweight deep models to perform HAR. As filter is the basic unit in constructing CNNs, it deserves further research whether re-designing smaller filters is applicable for deep HAR. In the paper, inspired by the idea, we proposed a lightweight CNN using Lego filters for HAR. A set of lower-dimensional filters is used as Lego bricks to be stacked for conventional filters, which does not rely on any special network structure. The local loss function is used to train model. To our knowledge, this is the first paper that proposes lightweight CNN for HAR in ubiquitous and wearable computing arena. The experiment results on five public HAR datasets, UCI-HAR dataset, OPPORTUNITY dataset, UNIMIB-SHAR dataset, PAMAP2 dataset, and WISDM dataset collected from either smartphones or multiple sensor nodes, indicate that our novel Lego CNN with local loss can greatly reduce memory and computation cost over CNN, while achieving higher accuracy. That is to say, the proposed model is smaller, faster and more accurate. Finally, we evaluate the actual performance on an Android smartphone.