# Personalizing human activity recognition models using incremental learning

arXiv: https://arxiv.org/abs/1905.12628 · HTML: https://mlsanity.com/paper/1905.12628/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2019.

## Built on by (most cited first, 4 of 4)

- [Subject Invariant Contrastive Learning for Human Activity Recognition](https://mlsanity.com/paper/2507.03250/) (2025)
- [Incremental Learning Techniques for Online Human Activity Recognition](https://mlsanity.com/paper/2109.09435/) (2021)
- [Feature Relevance Analysis to Explain Concept Drift -- A Case Study in Human Activity Recognition](https://mlsanity.com/paper/2301.08453/) (2023)
- [Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review](https://mlsanity.com/paper/2311.06311/) (2023)

## Abstract (arXiv)

In this study, the aim is to personalize inertial sensor data-based human activity recognition models using incremental learning. At first, the recognition is based on user-independent model. However, when personal streaming data becomes available, the incremental learning-based recognition model can be updated, and therefore personalized, based on the data without user-interruption. The used incremental learning algorithm is Learn++ which is an ensemble method that can use any classifier as a base classifier. In fact, study compares three different base classifiers: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and classification and regression tree (CART). Experiments are based on publicly open data set and they show that already a small personal training data set can improve the classification accuracy. Improvement using LDA as base classifier is 4.6 percentage units, using QDA 2.0 percentage units, and 2.3 percentage units using CART. However, if the user-independent model used in the first phase of the recognition process is not accurate enough, personalization cannot improve recognition accuracy.