Context-Aware Activity Recognition Systems

Latest papers

• Knowledge Distillation for LLM-Based Human Activity Recognition in Homes

  Julien Cumin (UGA), Oussama Er-Rahmany (UGA), Xi Chen (UGA) · 13 January 2026

  Human Activity Recognition (HAR) is a central problem for context-aware applications, especially for smart homes and assisted living. A few very recent studies have shown that Large Language Models (LLMs) can be used for HAR at home, reaching high performance and addressing key challenges. In this p…

• GNN-XAR: A Graph Neural Network for Explainable Activity Recognition in Smart Homes

  Michele Fiori, Davide Mor, Gabriele Civitarese, Claudio Bettini · 6 January 2026

  Sensor-based Human Activity Recognition (HAR) in smart home environments is crucial for several applications, especially in the healthcare domain. The majority of the existing approaches leverage deep learning models. While these approaches are effective, the rationale behind their outputs is opaque…

• LLM-Guided Exemplar Selection for Few-Shot Wearable-Sensor Human Activity Recognition

  Elsen Ronando, Sozo Inoue · 30 December 2025

  In this paper, we propose an LLM-Guided Exemplar Selection framework to address a key limitation in state-of-the-art Human Activity Recognition (HAR) methods: their reliance on large labeled datasets and purely geometric exemplar selection, which often fail to distinguish similar weara-ble sensor ac…

• Reducing Label Dependency in Human Activity Recognition with Wearables: From Supervised Learning to Novel Weakly Self-Supervised Approaches

  Taoran Sheng, Manfred Huber · 24 December 2025

  Human activity recognition (HAR) using wearable sensors has advanced through various machine learning paradigms, each with inherent trade-offs between performance and labeling requirements. While fully supervised techniques achieve high accuracy, they demand extensive labeled datasets that are costl…

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

  Irina Seregina, Philippe Lalanda, German Vega · 23 December 2025

  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 co…

• Chorus: Harmonizing Context and Sensing Signals for Data-Free Model Customization in IoT

  Liyu Zhang, Yejia Liu, Kwun Ho Liu, Runxi Huang, Xiaomin Ouyang · 18 December 2025

  In real-world IoT applications, sensor data is usually collected under diverse and dynamic contextual conditions where factors such as sensor placements or ambient environments can significantly affect data patterns and downstream performance. Traditional domain adaptation or generalization methods …

• HAROOD: A Benchmark for Out-of-distribution Generalization in Sensor-based Human Activity Recognition

  Wang Lu, Yao Zhu, Jindong Wang · 12 December 2025

  Sensor-based human activity recognition (HAR) mines activity patterns from the time-series sensory data. In realistic scenarios, variations across individuals, devices, environments, and time introduce significant distributional shifts for the same activities. Recent efforts attempt to solve this ch…

• AgentProg: Empowering Long-Horizon GUI Agents with Program-Guided Context Management

  Shizuo Tian, Hao Wen, Yuxuan Chen, Jiacheng Liu, Shanhui Zhao, Guohong Liu, Ju Ren, Yunxin Liu, Yuanchun Li · 12 December 2025

  The rapid development of mobile GUI agents has stimulated growing research interest in long-horizon task automation. However, building agents for these tasks faces a critical bottleneck: the reliance on ever-expanding interaction history incurs substantial context overhead. Existing context manageme…

• Quantifying Uncertainty in Machine Learning-Based Pervasive Systems: Application to Human Activity Recognition

  Vladimir Balditsyn, Philippe Lalanda, German Vega, St\'ephanie Chollet · 11 December 2025

  The recent convergence of pervasive computing and machine learning has given rise to numerous services, impacting almost all areas of economic and social activity. However, the use of AI techniques precludes certain standard software development practices, which emphasize rigorous testing to ensure …

• RAG-HAR: Retrieval Augmented Generation-based Human Activity Recognition

  Nirhoshan Sivaroopan, Hansi Karunarathna, Chamara Madarasingha, Anura Jayasumana, Kanchana Thilakarathna · 11 December 2025

  Human Activity Recognition (HAR) underpins applications in healthcare, rehabilitation, fitness tracking, and smart environments, yet existing deep learning approaches demand dataset-specific training, large labeled corpora, and significant computational resources.We introduce RAG-HAR, a training-fre…

• A Scene-aware Models Adaptation Scheme for Cross-scene Online Inference on Mobile Devices

  Yunzhe Li, Hongzi Zhu, Zhuohong Deng, Yunlong Cheng, Zimu Zheng, Liang Zhang, Shan Chang, Minyi Guo · 8 December 2025

  Emerging Artificial Intelligence of Things (AIoT) applications desire online prediction using deep neural network (DNN) models on mobile devices. However, due to the movement of devices, unfamiliar test samples constantly appear, significantly affecting the prediction accuracy of a pre-trained DNN. …

• Multi-Frequency Federated Learning for Human Activity Recognition Using Head-Worn Sensors

  Dario Fenoglio, Mohan Li, Davide Casnici, Matias Laporte, Shkurta Gashi, Silvia Santini, Martin Gjoreski, Marc Langheinrich · 4 December 2025

  Human Activity Recognition (HAR) benefits various application domains, including health and elderly care. Traditional HAR involves constructing pipelines reliant on centralized user data, which can pose privacy concerns as they necessitate the uploading of user data to a centralized server. This wor…

• DySTAN: Joint Modeling of Sedentary Activity and Social Context from Smartphone Sensors

  Aditya Sneh, Nilesh Kumar Sahu, Snehil Gupta, Haroon R. Lone · 3 December 2025

  Accurately recognizing human context from smartphone sensor data remains a significant challenge, especially in sedentary settings where activities such as studying, attending lectures, relaxing, and eating exhibit highly similar inertial patterns. Furthermore, social context plays a critical role i…

• MOTION: ML-Assisted On-Device Low-Latency Motion Recognition

  Veeramani Pugazhenthi, Wei-Hsiang Chu, Junwei Lu, Jadyn N. Miyahira, Soheil Salehi · 2 December 2025

  The use of tiny devices capable of low-latency gesture recognition is gaining momentum in everyday human-computer interaction and especially in medical monitoring fields. Embedded solutions such as fall detection, rehabilitation tracking, and patient supervision require fast and efficient tracking o…

• HoWDe: a validated algorithm for Home and Work location Detection

  S\'ilvia De Sojo, Lorenzo Lucchini, Ollin D. Langle-Chimal, Samuel P. Fraiberger, Laura Alessandretti · 2 December 2025

  Smartphone location data have become a key resource for understanding urban mobility, yet extracting actionable insights requires robust and reproducible preprocessing pipelines. A central step is the identification of individuals' home and work locations, which underpins analyses of commuting, empl…

• MMA: A Momentum Mamba Architecture for Human Activity Recognition with Inertial Sensors

  Thai-Khanh Nguyen, Uyen Vo, Tan M. Nguyen, Thieu N. Vo, Trung-Hieu Le, Cuong Pham · 27 November 2025

  Human activity recognition (HAR) from inertial sensors is essential for ubiquitous computing, mobile health, and ambient intelligence. Conventional deep models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformers have advanced HAR but remain limited by van…

• D-GARA: A Dynamic Benchmarking Framework for GUI Agent Robustness in Real-World Anomalies

  Sen Chen, Tong Zhao, Yi Bin, Fei Ma, Wenqi Shao, Zheng Wang · 21 November 2025

  Developing intelligent agents capable of operating a wide range of Graphical User Interfaces (GUIs) with human-level proficiency is a key milestone on the path toward Artificial General Intelligence. While most existing datasets and benchmarks for training and evaluating GUI agents are static and id…

• Dynamic User-controllable Privacy-preserving Few-shot Sensing Framework

  Ajesh Koyatan Chathoth, Shuhao Yu, Stephen Lee · 19 November 2025

  User-controllable privacy is important in modern sensing systems, as privacy preferences can vary significantly from person to person and may evolve over time. This is especially relevant in devices equipped with Inertial Measurement Unit (IMU) sensors, such as smartphones and wearables, which conti…

• Toward Dignity-Aware AI: Next-Generation Elderly Monitoring from Fall Detection to ADL

  Xun Shao, Aoba Otani, Yuto Hirasuka, Runji Cai, Seng W. Loke · 18 November 2025

  This position paper envisions a next-generation elderly monitoring system that moves beyond fall detection toward the broader goal of Activities of Daily Living (ADL) recognition. Our ultimate aim is to design privacy-preserving, edge-deployed, and federated AI systems that can robustly detect and u…

• Models Got Talent: Identifying High Performing Wearable Human Activity Recognition Models Without Training

  Richard Goldman, Varun Komperla, Thomas Ploetz, Harish Haresamudram · 11 November 2025

  A promising alternative to the computationally expensive Neural Architecture Search (NAS) involves the development of \textit{Zero Cost Proxies (ZCPs)}, which correlate well to trained performance, but can be computed through a single forward/backward pass on a randomly sampled batch of data. In thi…

• MARAuder's Map: Motion-Aware Real-time Activity Recognition with Layout-Based Trajectories

  Zishuai Liu, Weihang You, Jin Lu, Fei Dou · 11 November 2025

  Ambient sensor-based human activity recognition (HAR) in smart homes remains challenging due to the need for real-time inference, spatially grounded reasoning, and context-aware temporal modeling. Existing approaches often rely on pre-segmented, within-activity data and overlook the physical layout …

• Towards Large-Scale In-Context Reinforcement Learning by Meta-Training in Randomized Worlds

  Fan Wang, Pengtao Shao, Yiming Zhang, Bo Yu, Shaoshan Liu, Ning Ding, Yang Cao, Yu Kang, Haifeng Wang · 4 November 2025

  In-Context Reinforcement Learning (ICRL) enables agents to learn automatically and on-the-fly from their interactive experiences. However, a major challenge in scaling up ICRL is the lack of scalable task collections. To address this, we propose the procedurally generated tabular Markov Decision Pro…

• Learning with Category-Equivariant Architectures for Human Activity Recognition

  Yoshihiro Maruyama · 4 November 2025

  We propose CatEquiv, a category-equivariant neural network for Human Activity Recognition (HAR) from inertial sensors that systematically encodes temporal, amplitude, and structural symmetries. In particular, we introduce the categorical symmetry product where cyclic time shifts, positive gains and …

• Learning with Category-Equivariant Representations for Human Activity Recognition

  Yoshihiro Maruyama · 4 November 2025

  Human activity recognition is challenging because sensor signals shift with context, motion, and environment; effective models must therefore remain stable as the world around them changes. We introduce a categorical symmetry-aware learning framework that captures how signals vary over time, scale, …

• STAR: A Privacy-Preserving, Energy-Efficient Edge AI Framework for Human Activity Recognition via Wi-Fi CSI in Mobile and Pervasive Computing Environments

  Kexing Liu · 31 October 2025

  Human Activity Recognition (HAR) via Wi-Fi Channel State Information (CSI) presents a privacy-preserving, contactless sensing approach suitable for smart homes, healthcare monitoring, and mobile IoT systems. However, existing methods often encounter computational inefficiency, high latency, and limi…