# Continual Learning in Human Activity Recognition: an Empirical Analysis of Regularization

arXiv: https://arxiv.org/abs/2007.03032 · HTML: https://mlsanity.com/paper/2007.03032/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2020.

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

- [Class-incremental Learning for Time Series: Benchmark and Evaluation](https://mlsanity.com/paper/2402.12035/) (2024)
- [Lifelong Adaptive Machine Learning for Sensor-based Human Activity Recognition Using Prototypical Networks](https://mlsanity.com/paper/2203.05692/) (2022)
- [Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition](https://mlsanity.com/paper/2306.01108/) (2023)
- [Mining Your Own Secrets: Diffusion Classifier Scores for Continual Personalization of Text-to-Image Diffusion Models](https://mlsanity.com/paper/2410.00700/) (2024)
- [Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning](https://mlsanity.com/paper/2310.20052/) (2023)
- [PTMs-TSCIL Pre-Trained Models Based Class-Incremental Learning](https://mlsanity.com/paper/2503.07153/) (2025)
- [Harnessing the Power of Explanations for Incremental Training: A LIME-Based Approach](https://mlsanity.com/paper/2211.01413/) (2022)

## Abstract (arXiv)

Given the growing trend of continual learning techniques for deep neural networks focusing on the domain of computer vision, there is a need to identify which of these generalizes well to other tasks such as human activity recognition (HAR). As recent methods have mostly been composed of loss regularization terms and memory replay, we provide a constituent-wise analysis of some prominent task-incremental learning techniques employing these on HAR datasets. We find that most regularization approaches lack substantial effect and provide an intuition of when they fail. Thus, we make the case that the development of continual learning algorithms should be motivated by rather diverse task domains.