Parameter-Efficient Fine-Tuning for HAR: Integrating LoRA and QLoRA into Transformer Models

TL;DR

This paper explores parameter-efficient fine-tuning methods, LoRA and QLoRA, for transformer-based human activity recognition models, achieving comparable accuracy to full fine-tuning with reduced resource requirements.

Contribution

It introduces an adaptation framework using LoRA and QLoRA for HAR, demonstrating their effectiveness and efficiency across multiple datasets.

Findings

LoRA and QLoRA match full fine-tuning performance

Significant reduction in trainable parameters and memory usage

Robust performance under limited supervision

Abstract

Human Activity Recognition is a foundational task in pervasive computing. While recent advances in self-supervised learning and transformer-based architectures have significantly improved HAR performance, adapting large pretrained models to new domains remains a practical challenge due to limited computational resources on target devices. This papers investigates parameter-efficient fine-tuning techniques, specifically Low-Rank Adaptation (LoRA) and Quantized LoRA, as scalable alternatives to full model fine-tuning for HAR. We propose an adaptation framework built upon a Masked Autoencoder backbone and evaluate its performance under a Leave-One-Dataset-Out validation protocol across five open HAR datasets. Our experiments demonstrate that both LoRA and QLoRA can match the recognition performance of full fine-tuning while significantly reducing the number of trainable parameters, memory usage, and training time. Further analyses reveal that LoRA maintains robust performance even under limited supervision and that the adapter rank provides a controllable trade-off between accuracy and efficiency. QLoRA extends these benefits by reducing the memory footprint of frozen weights through quantization, with minimal impact on classification quality.