# Lifelong Adaptive Machine Learning for Sensor-based Human Activity Recognition Using Prototypical Networks

arXiv: https://arxiv.org/abs/2203.05692 · HTML: https://mlsanity.com/paper/2203.05692/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 68 references that are in the map.
- First posted on arXiv 11 Mar 2022.

## Builds on (most cited first, 26 of 26)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence](https://mlsanity.com/paper/1801.10112/) (2018)
- [Meta-Learning for Semi-Supervised Few-Shot Classification](https://mlsanity.com/paper/1803.00676/) (2018)
- [Dynamic Few-Shot Visual Learning without Forgetting](https://mlsanity.com/paper/1804.09458/) (2018)
- [Measuring Catastrophic Forgetting in Neural Networks](https://mlsanity.com/paper/1708.02072/) (2017)
- [Net2Net: Accelerating Learning via Knowledge Transfer](https://mlsanity.com/paper/1511.05641/) (2015)
- [Continual Unsupervised Representation Learning](https://mlsanity.com/paper/1910.14481/) (2019)
- [Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams](https://mlsanity.com/paper/2009.00919/) (2020)
- [A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning](https://mlsanity.com/paper/2001.00689/) (2020)
- [Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks](https://mlsanity.com/paper/1706.00527/) (2017)
- [An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks](https://mlsanity.com/paper/2007.15951/) (2020)
- [Incremental Few-Shot Learning with Attention Attractor Networks](https://mlsanity.com/paper/1810.07218/) (2018)
- [Wandering Within a World: Online Contextualized Few-Shot Learning](https://mlsanity.com/paper/2007.04546/) (2020)
- [Continual Learning in Sensor-based Human Activity Recognition: an Empirical Benchmark Analysis](https://mlsanity.com/paper/2104.09396/) (2021)
- [Incremental Real-Time Personalization in Human Activity Recognition Using Domain Adaptive Batch Normalization](https://mlsanity.com/paper/2005.12178/) (2020)
- [On Class Orderings for Incremental Learning](https://mlsanity.com/paper/2007.02145/) (2020)
- [Continual Learning in Human Activity Recognition: an Empirical Analysis of Regularization](https://mlsanity.com/paper/2007.03032/) (2020)
- [Continual Speaker Adaptation for Text-to-Speech Synthesis](https://mlsanity.com/paper/2103.14512/) (2021)
- [Using Convolutional Variational Autoencoders to Predict Post-Trauma Health Outcomes from Actigraphy Data](https://mlsanity.com/paper/2011.07406/) (2020)

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

- [iKAN: Global Incremental Learning with KAN for Human Activity Recognition Across Heterogeneous Datasets](https://mlsanity.com/paper/2406.01646/) (2024)
- [Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task](https://mlsanity.com/paper/2411.14452/) (2024)
- [Layout Agnostic Human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST)](https://mlsanity.com/paper/2405.12368/) (2024)
- [On-device Learning of EEGNet-based Network For Wearable Motor Imagery Brain-Computer Interface](https://mlsanity.com/paper/2409.00083/) (2024)
- [Efficient Online Continual Learning in Sensor-Based Human Activity Recognition](https://mlsanity.com/paper/2511.05566/) (2025)
- [Gated Adaptation for Continual Learning in Human Activity Recognition](https://mlsanity.com/paper/2603.10046/) (2026)
- [BayaHAR: Lightweight Bayesian Few-Shot User Adaptation for On-Device Personalized Human Activity Recognition](https://mlsanity.com/paper/2606.04798/) (2026)

## Abstract (arXiv)

Continual learning, also known as lifelong learning, is an emerging research topic that has been attracting increasing interest in the field of machine learning. With human activity recognition (HAR) playing a key role in enabling numerous real-world applications, an essential step towards the long-term deployment of such recognition systems is to extend the activity model to dynamically adapt to changes in people's everyday behavior. Current research in continual learning applied to HAR domain is still under-explored with researchers exploring existing methods developed for computer vision in HAR. Moreover, analysis has so far focused on task-incremental or class-incremental learning paradigms where task boundaries are known. This impedes the applicability of such methods for real-world systems since data is presented in a randomly streaming fashion. To push this field forward, we build on recent advances in the area of continual machine learning and design a lifelong adaptive learning framework using Prototypical Networks, LAPNet-HAR, that processes sensor-based data streams in a task-free data-incremental fashion and mitigates catastrophic forgetting using experience replay and continual prototype adaptation. Online learning is further facilitated using contrastive loss to enforce inter-class separation. LAPNet-HAR is evaluated on 5 publicly available activity datasets in terms of the framework's ability to acquire new information while preserving previous knowledge. Our extensive empirical results demonstrate the effectiveness of LAPNet-HAR in task-free continual learning and uncover useful insights for future challenges.