Context-Aware Activity Recognition Systems

Derniers papiers

• Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey

  Hugo Attali, Davide Buscaldi, Nathalie Pernelle, Fragkiskos D. Malliaros · 4 mai 2026

  Graph Neural Networks are powerful models for learning from graph-structured data, yet their effectiveness is often limited by two critical challenges: over-squashing, where information from distant nodes is excessively compressed, and over-smoothing, where repeated propagation makes node representa…

• Contrastive Learning for Multimodal Human Activity Recognition with Limited Labeled Data

  Long Jing, Zhixiong Yang, Yajun Zhang, Xinlong Feng · 28 avril 2026

  Human activity recognition serves as the foundation for various emerging applications. In recent years, researchers have used collaborative sensing of multi-source sensors to capture complex and dynamic human activities. However, multimodal human activity sensing typically encounters highly heteroge…

• Channel-Free Human Activity Recognition via Inductive-Bias-Aware Fusion Design for Heterogeneous IoT Sensor Environments

  Tatsuhito Hasegawa · 24 avril 2026

  Human activity recognition (HAR) in Internet of Things (IoT) environments must cope with heterogeneous sensor settings that vary across datasets, devices, body locations, sensing modalities, and channel compositions. This heterogeneity makes conventional channel-fixed models difficult to reuse acros…

• COMODO: Cross-Modal Video-to-IMU Distillation for Efficient Egocentric Human Activity Recognition

  Baiyu Chen, Wilson Wongso, Zechen Li, Yonchanok Khaokaew, Hao Xue, Flora Salim · 22 avril 2026

  The goal of creating intelligent, human-centered wearable systems for continuous activity understanding faces a fundamental trade-off: Egocentric video-based models capture rich semantic information and have demonstrated strong performance in human activity recognition (HAR), but their high power co…

• Integrating Anomaly Detection into Agentic AI for Proactive Risk Management in Human Activity

  Farbod Zorriassatine, Ahmad Lotfi · 22 avril 2026

  Agentic AI, with goal-directed, proactive, and autonomous decision-making capabilities, offers a compelling opportunity to address movement-related risks in human activity, including the persistent hazard of falls among elderly populations. Despite numerous approaches to fall mitigation through fall…

• An Edge-Cloud Collaborative Architecture for Proactive Elderly Care: Real-Time Risk Assessment and Three-Level Emergency Response

  Lijie Zhou, Luran Wang · 17 avril 2026

  The rapid aging of global populations has created an urgent need for intelligent healthcare monitoring systems to ensure the safety of elderly individuals living independently. Existing cloud-centric platforms face critical limitations, including high latency unsuitable for emergency response, priva…

• SynHAT: A Two-stage Coarse-to-Fine Diffusion Framework for Synthesizing Human Activity Traces

  Rongchao Xu, Lin Jiang, Dahai Yu, Ximiao Li, Guang Wang · 17 avril 2026

  Human activity traces (HATs) are critical for many applications, including human mobility modeling and point-of-interest (POI) recommendation. However, growing privacy concerns have severely limited access to authentic large-scale HAT datasets. Recent advances in generative AI provide new opportunit…

• Explainable Fall Detection for Elderly Care via Temporally Stable SHAP in Skeleton-Based Human Activity Recognition

  Mohammad Saleh, Azadeh Tabatabaei · 16 avril 2026

  Fall detection in elderly care requires not only accurate classification but also reliable explanations that clinicians can trust. However, existing post-hoc explainability methods, when applied frame-by-frame to sequential data, produce temporally unstable attribution maps that clinicians cannot re…

• TaFall: Balance-Informed Fall Detection via Passive Thermal Sensing

  Chengxiao Li, Xie Zhang, Wei Zhu, Yan Jiang, Chenshu Wu · 14 avril 2026

  Falls are a major cause of injury and mortality among older adults, yet most incidents occur in private indoor environments where monitoring must balance effectiveness with privacy. Existing privacy-preserving fall detection approaches, particularly those based on radio frequency sensing, often rely…

• Smartwatch-Based Sitting Time Estimation in Real-World Office Settings

  Olivia Zhang, Zhilin Zhang · 13 avril 2026

  Sedentary behavior poses a major public health risk, being strongly linked to obesity, cardiovascular disease, and other chronic conditions. Accurately estimating sitting time is therefore critical for monitoring and improving individual health. This work addresses the problem in real-world office s…

• AHCQ-SAM: Toward Accurate and Hardware-Compatible Post-Training Segment Anything Model Quantization

  Wenlun Zhang, Yunshan Zhong, Weiqi Yan, Shengchuan Zhang, Shimpei Ando, Kentaro Yoshioka · 9 avril 2026

  The Segment Anything Model (SAM) has revolutionized image and video segmentation with its powerful zero-shot capabilities. However, its massive parameter scale and high computational demands hinder efficient deployment on resource-constrained edge devices. While Post-Training Quantization (PTQ) offe…

• X-BCD: Explainable Sensor-Based Behavioral Change Detection in Smart Home Environments

  Gabriele Civitarese, Claudio Bettini · 9 avril 2026

  Behavioral changes in daily life activities at home can be digital markers of cognitive decline. However, such changes are difficult to assess through sporadic clinical visits and remain challenging to interpret from continuous in-home sensing data. Extensive work has been done in the ubiquitous com…

• Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems

  Biplab Pal, Santanu Bhattacharya, Madanjit Singh · 8 avril 2026

  Blind-spot mass is a Good-Turing framework for quantifying deployment coverage risk in machine learning. In modern ML systems, operational state distributions are often heavy-tailed, implying that a long tail of valid but rare states is structurally under-supported in finite training and evaluation …

• ActivityNarrated: An Open-Ended Narrative Paradigm for Wearable Human Activity Understanding

  Lala Shakti Swarup Ray, Mengxi Liu, Alcina Pinto, Deepika Gurung, Daniel Geissler, Paul Lukowoicz, Bo Zhou · 2 avril 2026

  Wearable HAR has improved steadily, but most progress still relies on closed-set classification, which limits real-world use. In practice, human activity is open-ended, unscripted, personalized, and often compositional, unfolding as narratives rather than instances of fixed classes. We argue that ad…

• HeteroHub: An Applicable Data Management Framework for Heterogeneous Multi-Embodied Agent System

  Xujia Li, Xin Li, Junquan Huang, Beirong Cui, Zibin Wu, Lei Chen · 31 mars 2026

  Heterogeneous Multi-Embodied Agent Systems involve coordinating multiple embodied agents with diverse capabilities to accomplish tasks in dynamic environments. This process requires the collection, generation, and consumption of massive, heterogeneous data, which primarily falls into three categorie…

• Hierarchical and Multimodal Data for Daily Activity Understanding

  Ghazal Kaviani, Yavuz Yarici, Seulgi Kim, Mohit Prabhushankar, Ghassan AlRegib, Mashhour Solh, Ameya Patil · 30 mars 2026

  Daily Activity Recordings for Artificial Intelligence (DARai, pronounced "Dahr-ree") is a multimodal, hierarchically annotated dataset constructed to understand human activities in real-world settings. DARai consists of continuous scripted and unscripted recordings of 50 participants in 10 different…

• SPECTRA: An Efficient Spectral-Informed Neural Network for Sensor-Based Activity Recognition

  Deepika Gurung, Lala Shakti Swarup Ray, Mengxi Liu, Bo Zhou, Paul Lukowicz · 30 mars 2026

  Real time sensor based applications in pervasive computing require edge deployable models to ensure low latency privacy and efficient interaction. A prime example is sensor based human activity recognition where models must balance accuracy with stringent resource constraints. Yet many deep learning…

• FED-HARGPT: A Hybrid Centralized-Federated Approach of a Transformer-based Architecture for Human Context Recognition

  Wandemberg Gibaut, Alexandre Osorio, Amparo Munoz, Sildolfo F. G. Neto, Fabio Grassiotto · 27 mars 2026

  The study explores a hybrid centralized-federated approach for Human Activity Recognition (HAR) using a Transformer-based architecture. With the increasing ubiquity of edge devices, such as smartphones and wearables, a significant amount of private data from wearable and inertial sensors is generate…

• A Multi-Modal CNN-LSTM Framework with Multi-Head Attention and Focal Loss for Real-Time Elderly Fall Detection

  Lijie Zhou, Luran Wang · 25 mars 2026

  The increasing global aging population has intensified the demand for reliable health monitoring systems, particularly those capable of detecting critical events such as falls among elderly individuals. Traditional fall detection approaches relying on single-modality acceleration data suffer from hi…

• Personalized Fall Detection by Balancing Data with Selective Feedback Using Contrastive Learning

  Awatif Yasmin, Tarek Mahmud, Sana Alamgeer, Anne H. H. Ngu · 19 mars 2026

  Personalized fall detection models can significantly improve accuracy by adapting to individual motion patterns, yet their effectiveness is often limited by the scarcity of real-world fall data and the dominance of non-fall feedback samples. This imbalance biases the model toward routine activities …

• Proactive Rejection and Grounded Execution: A Dual-Stage Intent Analysis Paradigm for Safe and Efficient AIoT Smart Homes

  Xinxin Jin, Zhengwei Ni, Zhengguo Sheng, Victor C. M. Leung · 18 mars 2026

  As Large Language Models (LLMs) transition from information providers to embodied agents in the Internet of Things (IoT), they face significant challenges regarding reliability and interaction efficiency. Direct execution of LLM-generated commands often leads to entity hallucinations (e.g., trying t…

• Collaborative Temporal Feature Generation via Critic-Free Reinforcement Learning for Cross-User Sensor-Based Activity Recognition

  Xiaozhou Ye, Feng Jiang, Zihan Wang, Xiulai Wang, Yutao Zhang, Kevin I-Kai Wang · 18 mars 2026

  Human Activity Recognition using wearable inertial sensors is foundational to healthcare monitoring, fitness analytics, and context-aware computing, yet its deployment is hindered by cross-user variability arising from heterogeneous physiological traits, motor habits, and sensor placements. Existing…

• CARE: Contrastive Alignment for ADL Recognition from Event-Triggered Sensor Streams

  Junhao Zhao, Zishuai Liu, Ruili Fang, Jin Lu, Linghan Zhang, Fei Dou · 17 mars 2026

  The recognition of Activities of Daily Living (ADLs) from event-triggered ambient sensors is an essential task in Ambient Assisted Living, yet existing methods remain constrained by representation-level limitations. Sequence-based approaches preserve temporal order of sensor activations but are sens…

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

  Nghi D. Q. Bui · 16 mars 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…

• CFD-HAR: User-controllable Privacy through Conditional Feature Disentanglement

  Alex Gn, Fan Li, S Kuniyilh, Ada Axan · 13 mars 2026

  Modern wearable and mobile devices are equipped with inertial measurement units (IMUs). Human Activity Recognition (HAR) applications running on such devices use machine-learning-based, data-driven techniques that leverage such sensor data. However, sensor-data-driven HAR deployments face two critic…