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Continual Learning in Sensor-based Human Activity Recognition: an Empirical Benchmark Analysis

arXiv 2104.09396 ↗First posted 19 Apr 2021eess.SP cs.HC cs.LGcited by 21

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Abstract (arXiv)

Sensor-based human activity recognition (HAR), i.e., the ability to discover human daily activity patterns from wearable or embedded sensors, is a key enabler for many real-world applications in smart homes, personal healthcare, and urban planning. However, with an increasing number of applications being deployed, an important question arises: how can a HAR system autonomously learn new activities over a long period of time without being re-engineered from scratch? This problem is known as continual learning and has been particularly popular in the domain of computer vision, where several techniques to attack it have been developed. This paper aims to assess to what extent such continual learning techniques can be applied to the HAR domain. To this end, we propose a general framework to evaluate the performance of such techniques on various types of commonly used HAR datasets. We then present a comprehensive empirical analysis of their computational cost and effectiveness of tackling HAR-specific challenges (i.e., sensor noise and labels' scarcity). The presented results uncover useful insights on their applicability and suggest future research directions for HAR systems. Our code, models and data are available at https://github.com/srvCodes/continual-learning-benchmark.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 43 of its 89 references that are in the map. First posted on arXiv 19 Apr 2021.

Builds on

43 of 89 references.

References · 43

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31 more
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  6. 2019Orthogonal Gradient Descent for Continual Learningcited by 361
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 4; 2023: 4; 2024: 3; 2025: 6; 2026: 2.

21 papers.

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  1. 2024CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Modelscited by 25
  2. 2022The Neural Process Family: Survey, Applications and Perspectivescited by 23
  3. 2021Exploring System Performance of Continual Learning for Mobile and Embedded Sensing Applicationscited by 17
  4. 2023LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platformscited by 11
  5. 2024Class-incremental Learning for Time Series: Benchmark and Evaluationcited by 9
  6. 2022Lifelong Adaptive Machine Learning for Sensor-based Human Activity Recognition Using Prototypical Networkscited by 7
  7. 2023Evaluation of Regularization-based Continual Learning Approaches: Application to HARcited by 3
  8. 2023Hadamard Domain Training with Integers for Class Incremental Quantized Learningcited by 3
  9. 2024Balancing Continual Learning and Fine-tuning for Human Activity Recognitioncited by 3
  10. 2025MAC-Gaze: Motion-Aware Continual Calibration for Mobile Gaze Trackingcited by 3
  11. 2021Multimedia Datasets for Anomaly Detection: A Reviewcited by 2
  12. 2022Continual Learning for Human State Monitoringcited by 2
9 more
  1. 2023Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learningcited by 2
  2. 2025PTMs-TSCIL Pre-Trained Models Based Class-Incremental Learningcited by 1
  3. 2025CLAD-Net: Continual Activity Recognition in Multi-Sensor Wearable Systemscited by 1
  4. 2022Improving Feature Generalizability with Multitask Learning in Class Incremental Learningcited by 0
  5. 2025MetaCLBench: Meta Continual Learning Benchmark on Resource-Constrained Edge Devicescited by 0
  6. 2025TaskVAE: Task-Specific Variational Autoencoders for Exemplar Generation in Continual Learning for Human Activity Recognitioncited by 0
  7. 2025Efficient Online Continual Learning in Sensor-Based Human Activity Recognitioncited by 0
  8. 2026Gated Adaptation for Continual Learning in Human Activity Recognitioncited by 0
  9. 2026WHAR Arena: Benchmarking the State of the Art in Efficient Wearable Human Activity Recognitioncited by 0