# Few-Shot Learning-Based Human Activity Recognition

arXiv: https://arxiv.org/abs/1903.10416 · HTML: https://mlsanity.com/paper/1903.10416/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2019.

## Built on by (most cited first, 11 of 11)

- [IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity Recognition](https://mlsanity.com/paper/2402.01049/) (2024)
- [An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence](https://mlsanity.com/paper/2103.15990/) (2021)
- [Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Framework](https://mlsanity.com/paper/2305.04401/) (2023)
- [Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review](https://mlsanity.com/paper/2403.15422/) (2024)
- [Babel: A Scalable Pre-trained Model for Multi-Modal Sensing via Expandable Modality Alignment](https://mlsanity.com/paper/2407.17777/) (2024)
- [Dynamic User-controllable Privacy-preserving Few-shot Sensing Framework](https://mlsanity.com/paper/2508.03989/) (2025)
- [Worker Activity Recognition in Manufacturing Line Using Near-body Electric Field](https://mlsanity.com/paper/2308.03514/) (2023)
- [Practical Insights on Incremental Learning of New Human Physical Activity on the Edge](https://mlsanity.com/paper/2308.11691/) (2023)
- [Generalization of Fitness Exercise Recognition from Doppler Measurements by Domain-adaption and Few-Shot Learning](https://mlsanity.com/paper/2311.11910/) (2023)
- [Few-Shot Optimization for Sensor Data Using Large Language Models: A Case Study on Fatigue Detection](https://mlsanity.com/paper/2505.18754/) (2025)
- [CFD-HAR: User-controllable Privacy through Conditional Feature Disentanglement](https://mlsanity.com/paper/2603.11526/) (2026)

## Abstract (arXiv)

Few-shot learning is a technique to learn a model with a very small amount of labeled training data by transferring knowledge from relevant tasks. In this paper, we propose a few-shot learning method for wearable sensor based human activity recognition, a technique that seeks high-level human activity knowledge from low-level sensor inputs. Due to the high costs to obtain human generated activity data and the ubiquitous similarities between activity modes, it can be more efficient to borrow information from existing activity recognition models than to collect more data to train a new model from scratch when only a few data are available for model training. The proposed few-shot human activity recognition method leverages a deep learning model for feature extraction and classification while knowledge transfer is performed in the manner of model parameter transfer. In order to alleviate negative transfer, we propose a metric to measure cross-domain class-wise relevance so that knowledge of higher relevance is assigned larger weights during knowledge transfer. Promising results in extensive experiments show the advantages of the proposed approach.