Context-Aware Activity Recognition Systems

Neueste Paper

• Tureis: Transformer-based Unified Resilience for IoT Devices in Smart Homes

  Alireza Borhani, Vafa Andalibi, Bahar Asgari · 12. März 2026

  Smart-home IoT systems rely on heterogeneous sensor networks whose correctness shapes application behavior and the physical environment. However, these low-cost, resource-constrained sensors are highly prone to failure under real-world stressors. Prior methods often assume single-failure, single-res…

• Gated Adaptation for Continual Learning in Human Activity Recognition

  Reza Rahimi Azghan, Gautham Krishna Gudur, Mohit Malu, Edison Thomaz, Giulia Pedrielli, Pavan Turaga, Hassan Ghasemzadeh · 12. März 2026

  Wearable sensors in Internet of Things (IoT) ecosystems increasingly support applications such as remote health monitoring, elderly care, and smart home automation, all of which rely on robust human activity recognition (HAR). Continual learning systems must balance plasticity (learning new tasks) w…

• Bio-Inspired Self-Supervised Learning for Wrist-worn IMU Signals

  Prithviraj Tarale, Kiet Chu, Abhishek Varghese, Kai-Chun Liu, Maxwell A Xu, Mohit Iyyer, Sunghoon I. Lee · 12. März 2026

  Wearable accelerometers have enabled large-scale health and wellness monitoring, yet learning robust human-activity representations has been constrained by the scarcity of labeled data. While self-supervised learning offers a potential remedy, existing approaches treat sensor streams as unstructured…

• CLAD-Net: Continual Activity Recognition in Multi-Sensor Wearable Systems

  Reza Rahimi Azghan, Gautham Krishna Gudur, Mohit Malu, Edison Thomaz, Giulia Pedrielli, Pavan Turaga, Hassan Ghasemzadeh · 10. März 2026

  The rise of deep learning has greatly advanced human behavior monitoring using wearable sensors, particularly human activity recognition (HAR). While deep models have been widely studied, most assume stationary data distributions - an assumption often violated in real-world scenarios. For example, s…

• FedSCS-XGB -- Federated Server-centric surrogate XGBoost for continual health monitoring

  Felix Walger, Mehdi Ejtehadi, Anke Schmeink, Diego Paez-Granados · 9. März 2026

  Wearable sensors with local data processing can detect health threats early, enhance documentation, and support personalized therapy. In the context of spinal cord injury (SCI), which involves risks such as pressure injuries and blood pressure instability, continuous monitoring can help mitigate the…

• Activity Recognition from Smart Insole Sensor Data Using a Circular Dilated CNN

  Yanhua Zhao · 6. März 2026

  Smart insoles equipped with pressure sensors, accelerometers, and gyroscopes offer a non-intrusive means of monitoring human gait and posture. We present an activity classification system based on a circular dilated convolutional neural network (CDCNN) that processes multi-modal time-series data fro…

• Embedded Inter-Subject Variability in Adversarial Learning for Inertial Sensor-Based Human Activity Recognition

  Francisco M. Calatrava-Nicol\'as, Shoko Miyauchi, Vitor Fortes Rey, Paul Lukowicz, Todor Stoyanov, Oscar Martinez Mozos · 6. März 2026

  This paper addresses the problem of Human Activity Recognition (HAR) using data from wearable inertial sensors. An important challenge in HAR is the model's generalization capabilities to new unseen individuals due to inter-subject variability, i.e., the same activity is performed differently by dif…

• Building AI Coding Agents for the Terminal: Scaffolding, Harness, Context Engineering, and Lessons Learned

  Nghi D. Q. Bui · 6. März 2026

  The landscape of AI coding assistance is undergoing a fundamental shift from complex IDE plugins to versatile, terminal-native agents. Operating directly where developers manage source control, execute builds, and deploy environments, CLI-based agents offer unprecedented autonomy for long-horizon de…

• MuRAL: A Multi-Resident Ambient Sensor Dataset Annotated with Natural Language for Activities of Daily Living

  Xi Chen (M-PSI), Julien Cumin (M-PSI), Fano Ramparany (M-PSI), Dominique Vaufreydaz (M-PSI) · 5. März 2026

  Recent progress in Large Language Models (LLMs) has enabled advanced reasoning and zero-shot recognition for human activity understanding with ambient sensor data. However, widely used multi-resident datasets such as CASAS, ARAS, and MARBLE lack natural language context and fine-grained annotation, …

• S5-HES Agent: Society 5.0-driven Agentic Framework to Democratize Smart Home Environment Simulation

  Akila Siriweera, Janani Rangila, Keitaro Naruse, Incheon Paik, Isuru Jayanada · 3. März 2026

  The smart home is a key domain within the Society 5.0 vision for a human-centered society. Smart home technologies rapidly evolve, and research should diversify while remaining aligned with Society 5.0 objectives. Democratizing smart home research would engage a broader community of innovators beyon…

• Evaluating Few-Shot Temporal Reasoning of LLMs for Human Activity Prediction in Smart Environments

  Maral Doctorarastoo, Katherine A. Flanigan, Mario Berg\'es, Christopher McComb · 13. Februar 2026

  Anticipating human activities and their durations is essential in applications such as smart-home automation, simulation-based architectural and urban design, activity-based transportation system simulation, and human-robot collaboration, where adaptive systems must respond to human activities. Exis…

• The CAPSARII Approach to Cyber-Secure Wearable, Ultra-Low-Power Networked Sensors for Soldier Health Monitoring

  Luciano Bozzi, Christian Celidonio, Umberto Nuzzi, Massimo Biagini, Stefano Cherubin, Asbj{\o}rn Djupdal, Tor Andre Haugdahl, Andrea Aliverti, Alessandra Angelucci, Giovanni Agosta, Gerardo Pelosi, Paolo Belluco, Samuele Polistina, Riccardo Volpi, Luigi Malag\`o, Michael Schneider, Florian Wieczorek, Xabier Eguiluz · 10. Februar 2026

  The European Defence Agency's revised Capability Development Plan (CDP) identifies as a priority improving ground combat capabilities by enhancing soldiers' equipment for better protection. The CAPSARII project proposes in innovative wearable system and Internet of Battlefield Things (IoBT) framewor…

• Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Data

  Thu Hang Phung, Duong M. Nguyen, Thanh Trung Huynh, Quoc Viet Hung Nguyen, Trong Nghia Hoang, Phi Le Nguyen · 10. Februar 2026

  This paper introduces a generalized federated prompt-tuning framework for practical scenarios where local datasets are multi-modal and exhibit different distributional patterns of missing features at the input level. The proposed framework bridges the gap between federated learning and multi-modal p…

• XTransfer: Modality-Agnostic Few-Shot Model Transfer for Human Sensing at the Edge

  Yu Zhang, Xi Zhang, Hualin Zhou, Xinyuan Chen, Shang Gao, Hong Jia, Jianfei Yang, Yuankai Qi, Tao Gu · 9. Februar 2026

  Deep learning for human sensing on edge systems presents significant potential for smart applications. However, its training and development are hindered by the limited availability of sensor data and resource constraints of edge systems. While transferring pre-trained models to different sensing ap…

• CMD-HAR: Cross-Modal Disentanglement for Wearable Human Activity Recognition

  Ying Yu, Siyao Li, Yixuan Jiang, Hang Xiao, Jingxi Long, Haotian Tang, Hanyu Liu, Chao Li · 6. Februar 2026

  Human Activity Recognition (HAR) is a fundamental technology for numerous human - centered intelligent applications. Although deep learning methods have been utilized to accelerate feature extraction, issues such as multimodal data mixing, activity heterogeneity, and complex model deployment remain …

• DISCOVER: Identifying Patterns of Daily Living in Human Activities from Smart Home Data

  Alexander Karpekov, Archith Iyer, Sourish Gunesh Dhekane, Sonia Chernova, Thomas Pl\"otz · 5. Februar 2026

  Smart homes equipped with ambient sensors offer a transformative approach to continuous health monitoring and assisted living. Traditional research in this domain primarily focuses on Human Activity Recognition (HAR), which relies on mapping sensor data to a closed set of predefined activity labels.…

• Thermal Imaging-based Real-time Fall Detection using Motion Flow and Attention-enhanced Convolutional Recurrent Architecture

  Christopher Silver, Thangarajah Akilan · 5. Februar 2026

  Falls among seniors are a major public health issue. Existing solutions using wearable sensors, ambient sensors, and RGB-based vision systems face challenges in reliability, user compliance, and practicality. Studies indicate that stakeholders, such as older adults and eldercare facilities, prefer n…

• Auto-Augmentation Contrastive Learning for Wearable-based Human Activity Recognition

  Qingyu Wu, Jianfei Shen, Feiyi Fan, Yang Gu, Chenyang Xu, Yiqiang Chen · 4. Februar 2026

  For low-semantic sensor signals from human activity recognition (HAR), contrastive learning (CL) is essential to implement novel applications or generic models without manual annotation, which is a high-performance self-supervised learning (SSL) method. However, CL relies heavily on data augmentatio…

• Real-Time Human Activity Recognition on Edge Microcontrollers: Dynamic Hierarchical Inference with Multi-Spectral Sensor Fusion

  Boyu Li, Kuangji Zuo, Lincong Li, Yonghui Wu · 3. Februar 2026

  The demand for accurate on-device pattern recognition in edge applications is intensifying, yet existing approaches struggle to reconcile accuracy with computational constraints. To address this challenge, a resource-aware hierarchical network based on multi-spectral fusion and interpretable modules…

• CoMeT: Collaborative Memory Transformer for Efficient Long Context Modeling

  Runsong Zhao, Shilei Liu, Jiwei Tang, Langming Liu, Haibin Chen, Weidong Zhang, Yujin Yuan, Tong Xiao, Jingbo Zhu, Wenbo Su, Bo Zheng · 3. Februar 2026

  The quadratic complexity and indefinitely growing key-value (KV) cache of standard Transformers pose a major barrier to long-context processing. To overcome this, we introduce the Collaborative Memory Transformer (CoMeT), a novel architecture that enables LLMs to handle arbitrarily long sequences wi…

• Mobile Exergames: Activity Recognition Based on Smartphone Sensors

  David Craveiro, Hugo Silva · 3. Februar 2026

  Smartphone sensors can be extremely useful in providing information on the activities and behaviors of persons. Human activity recognition is increasingly used for games, medical, or surveillance. In this paper, we propose a proof-of-concept 2D endless game called Duck Catch & Fit, which implements …

• DomusFM: A Foundation Model for Smart-Home Sensor Data

  Michele Fiori, Gabriele Civitarese, Flora D. Salim, Claudio Bettini · 3. Februar 2026

  Smart-home sensor data holds significant potential for several applications, including healthcare monitoring and assistive technologies. Existing approaches, however, face critical limitations. Supervised models require impractical amounts of labeled data. Foundation models for activity recognition …

• Privacy-Preserving Sensor-Based Human Activity Recognition for Low-Resource Healthcare Using Classical Machine Learning

  Ramakant Kumar, Pravin Kumar · 2. Februar 2026

  Limited access to medical infrastructure forces elderly and vulnerable patients to rely on home-based care, often leading to neglect and poor adherence to therapeutic exercises such as yoga or physiotherapy. To address this gap, we propose a low-cost and automated human activity recognition (HAR) fr…

• Meta Context Engineering via Agentic Skill Evolution

  Haoran Ye, Xuning He, Vincent Arak, Haonan Dong, Guojie Song · 30. Januar 2026

  The operational efficacy of large language models relies heavily on their inference-time context. This has established Context Engineering (CE) as a formal discipline for optimizing these inputs. Current CE methods rely on manually crafted harnesses, such as rigid generation-reflection workflows and…

• HERMES: A Unified Open-Source Framework for Realtime Multimodal Physiological Sensing, Edge AI, and Intervention in Closed-Loop Smart Healthcare Applications

  Maxim Yudayev, Juha Carlon, Diwas Lamsal, Vayalet Stefanova, Benjamin Filtjens · 21. Januar 2026

  Intelligent assistive technologies are increasingly recognized as critical daily-use enablers for people with disabilities and age-related functional decline. Longitudinal studies, curation of quality datasets, live monitoring in activities of daily living, and intelligent intervention devices, shar…