# Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data

arXiv: https://arxiv.org/abs/2011.01776 · HTML: https://mlsanity.com/paper/2011.01776/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

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

- [Towards Generalizable Human Activity Recognition: A Survey](https://mlsanity.com/paper/2508.12213/) (2025)
- [Pain level and pain-related behaviour classification using GRU-based sparsely-connected RNNs](https://mlsanity.com/paper/2212.14806/) (2022)
- [Disentangling Imperfect: A Wavelet-Infused Multilevel Heterogeneous Network for Human Activity Recognition in Flawed Wearable Sensor Data](https://mlsanity.com/paper/2402.09434/) (2024)
- [Advancing Multimodal Data Fusion in Pain Recognition: A Strategy Leveraging Statistical Correlation and Human-Centered Perspectives](https://mlsanity.com/paper/2404.00320/) (2024)
- [CauSkelNet: Causal Representation Learning for Human Behaviour Analysis](https://mlsanity.com/paper/2409.15564/) (2024)
- [Learn2Agree: Fitting with Multiple Annotators without Objective Ground Truth](https://mlsanity.com/paper/2109.03596/) (2021)
- [Decomposing and Fusing Intra- and Inter-Sensor Spatio-Temporal Signal for Multi-Sensor Wearable Human Activity Recognition](https://mlsanity.com/paper/2501.10917/) (2025)
- [L-SFAN: Lightweight Spatially-focused Attention Network for Pain Behavior Detection](https://mlsanity.com/paper/2406.16913/) (2024)
- [Hi-OSCAR: Hierarchical Open-set Classifier for Human Activity Recognition](https://mlsanity.com/paper/2510.08635/) (2025)
- [Bridging the gap between emotion and joint action](https://mlsanity.com/paper/2108.06264/) (2021)
- [STAL: Spike Threshold Adaptive Learning Encoder for Classification of Pain-Related Biosignal Data](https://mlsanity.com/paper/2407.08362/) (2024)

## Abstract (arXiv)

Protective behavior exhibited by people with chronic pain (CP) during physical activities is the key to understanding their physical and emotional states. Existing automatic protective behavior detection (PBD) methods rely on pre-segmentation of activities predefined by users. However, in real life, people perform activities casually. Therefore, where those activities present difficulties for people with chronic pain, technology-enabled support should be delivered continuously and automatically adapted to activity type and occurrence of protective behavior. Hence, to facilitate ubiquitous CP management, it becomes critical to enable accurate PBD over continuous data. In this paper, we propose to integrate human activity recognition (HAR) with PBD via a novel hierarchical HAR-PBD architecture comprising graph-convolution and long short-term memory (GC-LSTM) networks, and alleviate class imbalances using a class-balanced focal categorical-cross-entropy (CFCC) loss. Through in-depth evaluation of the approach using a CP patients' dataset, we show that the leveraging of HAR, GC-LSTM networks, and CFCC loss leads to clear increase in PBD performance against the baseline (macro F1 score of 0.81 vs. 0.66 and precision-recall area-under-the-curve (PR-AUC) of 0.60 vs. 0.44). We conclude by discussing possible use cases of the hierarchical architecture in CP management and beyond. We also discuss current limitations and ways forward.