Context-Aware Activity Recognition Systems

Últimos artículos

• MiCU: End-to-End Smart Home Command Understanding with Large Language Model

  Haowei Han, Kexin Hu, Weiwei Cai, Debiao Zhang, Bin Qin, Yuxiang Wang, Jiawei Jiang, Xiao Yan, Bo Du · 2 de junio de 2026

  Command understanding systems in smart home ecosystems can automate device control and substantially improve user experience. However, while they perform well on precise utterances (e.g., "turn on the bedroom light"), they struggle with ambiguous or misaligned commands (e.g., "make the bedroom cozy"…

• A Foundation Model for Wearable Movement Data in Mental Health Research

  Franklin Y. Ruan, Aiwei Zhang, Jenny Y. Oh, SouYoung Jin, Nicholas C. Jacobson · 2 de junio de 2026

  Wearable movement data is collected by nearly all commercially available smartwatches and is a valuable resource for mental health research, reflecting fine-grained temporal behavioral trends. Despite its promise, the development of foundation models for health wearable modeling remains limited when…

• Longitudinal Multimodal Sensing of Physical Activity and Well-Being in Older Adults

  Flavio Di Martino, Mattia G. Campana, Marcello Magno, Lorenza Pratali, Franca Delmastro · 2 de junio de 2026

  Wearable and mobile sensing technologies enable continuous monitoring of human behavior and health in real-world settings. However, predictive modeling in longitudinal multimodal data remains challenging, particularly when targeting complex or clinically derived outcomes. In this work, we present a …

• LastAct: Trajectory-Guided Latest-Activity Localization for Real-Time Smart-Home Activity Recognition

  Zishuai Liu, Ruili Fang, Jin Lu, Fei Dou · 2 de junio de 2026

  Human Activity Recognition (HAR) from ambient sensors enables smart-home applications such as health monitoring and assisted living. In realistic deployments, however, sensor events arrive as a continuous stream and activity boundaries are unknown. Sliding-window inference therefore produces many wi…

• Learning Agent-Compatible Context Management for Long-Horizon Tasks

  Lu Yi, Runlin Lei, Liuyi Yao, Yuexiang Xie, Yuyang Li, Wenhao Zhang, Zhewei Wei, Yaliang Li, Jian-Yun Nie · 1 de junio de 2026

  LLM agents increasingly face long-horizon tasks such as web search and deep research in real-world applications, where accumulated context can cause long-context degradation and reasoning failures. Prior work mitigates this through context management with agent-side context control or fixed strategi…

• Position: The Turing-Completeness of Autoregressive Transformers Relies Heavily on Context Management

  Guanyu Cui, Zhewei Wei, Kun He · 28 de mayo de 2026

  Many works make the eye-catching claim that Transformers are Turing-complete. However, the literature often conflates two distinct settings: (i) a fixed Transformer system setting, in which a fixed autoregressive Transformer is coupled with a fixed context-management method to process inputs of diff…

• Bio-Inspired Self-Supervised Learning for Wrist-worn Accelerometer Data

  Prithviraj Tarale, Kiet Chu, Abhishek Varghese, Kai-Chun Liu, Maxwell A. Xu, Mohit Iyyer, Sunghoon I. Lee · 28 de mayo de 2026

  Wearable accelerometers enable large-scale health monitoring, yet learning robust human-activity representations has been constrained by scarce labeled data. While self-supervised learning offers a remedy, existing methods treat sensor streams as unstructured time series, overlooking the underlying …

• CHESTNUT: A QoS Dataset for Mobile Edge Environments

  Guobing Zou, Fei Zhao, Shengxiang Hu · 26 de mayo de 2026

  Quality of Service (QoS) is an important metric to measure the performance of network services. Nowadays, it is widely used in mobile edge environments to evaluate the quality of service when mobile devices request services from edge servers. QoS usually involves multiple dimensions, such as bandwid…

• You Don't Need Attention: Gated Convolutional Modeling for Watch-Based Fall Detection

  Sana Alamgeer, Ronish Kumar, Awatif Yasmin, Muhammad Irshad, Anne H. H. Ngu · 22 de mayo de 2026

  Existing deep learning approaches for wearable fall detection systems rely on self-attention mechanisms that impose quadratic computational overhead, distributing weights across all time steps. This global weight distribution impairs the precise localization of the brief impact signatures that chara…

• Dywave: Event-Aligned Dynamic Tokenization for Heterogeneous IoT Sensing Signals

  Tomoyoshi Kimura, Denizhan Kara, Jinyang Li, Hongjue Zhao, Yigong Hu, Yizhuo Chen, Xiaomin Ouyang, Shengzhong Liu, Tarek Abdelzaher · 20 de mayo de 2026

  Internet of Things (IoT) systems continuously collect heterogeneous sensing signals from ubiquitous sensors to support intelligent applications such as human activity analysis, emotion monitoring, and environmental perception. These signals are inherently non-stationary and multi-scale, posing uniqu…

• KAN-MLP-Mixer: A comprehensive investigation of the usage of Kolmogorov-Arnold Networks (KANs) for improving IMU-based Human Activity Recognition

  Mengxi Liu, Sizhen Bian, Vitor Fortes, Francisco Calatrava Nicolas, Daniel Gei{\ss}ler, Maximilian Kiefer-Emmanouilidis, Bo Zhou, Paul Lukowicz · 20 de mayo de 2026

  Kolmogorov-Arnold Networks (KANs) have demonstrated an exceptional ability to learn complex functions on clean, low-dimensional data but struggle to maintain performance on noisy and imperfect real-world datasets. In contrast, conventional multi-layer perceptrons (MLPs) are far more tolerant to nois…

• Position: The Turing-Completeness of Real-World Autoregressive Transformers Relies Heavily on Context Management

  Guanyu Cui, Zhewei Wei, Kun He · 20 de mayo de 2026

  Many works make the eye-catching claim that Transformers are Turing-complete. However, the literature often conflates two distinct settings: (i) a fixed Transformer system setting, in which a fixed autoregressive Transformer is coupled with a fixed context-management method to process inputs of diff…

• Dywave: Event-Aligned Dynamic Tokenization for Heterogeneous IoT Sensing Signal

  Tomoyoshi Kimura, Denizhan Kara, Jinyang Li, Hongjue Zhao, Yigong Hu, Yizhuo Chen, Xiaomin Ouyang, Shengzhong Liu, Tarek Abdelzaher · 15 de mayo de 2026

  Internet of Things (IoT) systems continuously collect heterogeneous sensing signals from ubiquitous sensors to support intelligent applications such as human activity analysis, emotion monitoring, and environmental perception. These signals are inherently non-stationary and multi-scale, posing uniqu…

• Efficient and Adaptive Human Activity Recognition via LLM Backbones

  Aleksandr Bredikhin, Philippe Lalanda, German Vega · 13 de mayo de 2026

  Human Activity Recognition (HAR) is a core task in pervasive computing systems, where models must operate under strict computational constraints while remaining robust to heterogeneous and evolving deployment conditions. Recent advances based on Transformer architectures have significantly improved …

• UNCOM: Zero-shot Context-Aware Command Understanding for Tabletop Scenarios

  Antonio Galiza Cerdeira Gonzalez, Pawe{\l} Gajewski, Bipin Indurkhya · 11 de mayo de 2026

  This paper presents UNCOM, a novel hybrid framework for interpreting natural human commands in tabletop scenarios. The system integrates multiple sources of information -- speech, gestures, and scene context -- to extract structured, actionable instructions for robots. Addressing the need for genera…

• Temporal Structure Matters for Efficient Test-Time Adaptation in Wearable Human Activity Recognition

  Zishu Zhou, Zaipeng Xie, Xuanyao Jie · 7 de mayo de 2026

  Wearable human activity recognition (WHAR) models often suffer from performance degradation under real-world cross-user distribution shifts. Test-time adaptation (TTA) mitigates this degradation by adapting models online using unlabeled test streams, yet existing methods largely inherit assumptions …

• SensingAgents: A Multi-Agent Collaborative Framework for Robust IMU Activity Recognition

  Naiyu Zheng, Tianlong Yu, Haochen Yin, Xiaoyi Fan, Xiping Hu, Zhimeng Yin · 7 de mayo de 2026

  Human Activity Recognition (HAR) using Inertial Measurement Unit (IMU) sensors is a cornerstone of mobile health, smart environments, and human-computer interaction. However, current deep learning-based HAR models often struggle with heavy reliance on labeled data, position-specific ambiguity, and a…

• LongSeeker: Elastic Context Orchestration for Long-Horizon Search Agents

  Yijun Lu, Rui Ye, Yuwen Du, Jiajun Wang, Songhua Liu, Siheng Chen · 7 de mayo de 2026

  Long-horizon search agents must manage a rapidly growing working context as they reason, call tools, and observe information. Naively accumulating all intermediate content can overwhelm the agent, increasing costs and the risk of errors. We propose that effective context management should be adaptiv…

• Triple Spectral Fusion for Sensor-based Human Activity Recognition

  Ye Zhang, Longguang Wang, Qing Gao, Chaocan Xiang, Mohammed Bennamoun, Yulan Guo · 6 de mayo de 2026

  The field of sensor-based human activity recognition (HAR) mainly uses posture, motion and context data of Inertial Measurement Units (IMUs) to identify daily activities. Despite the advancements in learning-based methods, it is challenging to perform information fusion from the temporal perspective…

• Leveraging Imperfect Medical Data: A Manifold-Consistent Spatio-Temporal Network for Sensor-based Human Activity Recognition

  Jiangtao Fan, Anish Jindal, Amir Atapour-Abarghouei · 6 de mayo de 2026

  Sensor-based Human Activity Recognition (HAR) has attracted increasing attention in medical and healthcare monitoring, particularly with the growth of Internet of Medical Things (IoMT). However, in real-world wearable sensing scenarios, IoMT signals are often corrupted by missing measurements, senso…

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

  Mohammad Saleh, Azadeh Tabatabaei · 6 de mayo de 2026

  Reliable fall detection in elderly care requires monitoring systems that are not only accurate but also capable of producing stable, interpretable explanations of motion dynamics, a requirement that existing post hoc explainability methods rarely satisfy when applied to sequential biosignals. This s…

• HARMES: A Multi-Modal Dataset for Wearable Human Activity Recognition with Motion, Environmental Sensing and Sound

  Robin Burchard, Pascal-Andr\'e Br\"uckner, Marius Bock, Juergen Gall, Kristof Van Laerhoven · 5 de mayo de 2026

  With each sensing modality exhibiting inherent strengths and limitations, multi-modal approaches for wearable Human Activity Recognition (HAR) are becoming increasingly relevant -- particularly for recognizing Activities of Daily Living (ADLs), where individual modalities often produce ambiguous sig…

• An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications

  Sara Rimoldi, Arianna De Vecchi, Hazem Hesham Yousef Shalby, Federica Villa · 5 de mayo de 2026

  Wearable devices running Human Activity Recognition(HAR) on Inertial Measurement Units~(IMUs) waste energy by performing continuous classification for each window, even during long periods of unchanged activity. We address this with a lightweight change-detection gate: a non-parametric algorithm bas…

• Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation

  Eun Som Jeon, Jisoo Lee, Huisu Lim, Omik M. Save, Hyunglae Lee, Pavan Turaga · 5 de mayo de 2026

  Wearable sensor-based human gait analysis holds great promise in healthcare, rehabilitation, clinical diagnosis and monitoring, and sports activities. Specifically, ground reaction force (GRF) provides essential insights into the body's interaction with the ground during movement and is typically me…

• Class-Aware Adaptive Differential Privacy in Deep Learning for Sensor-Based Fall Detection

  Joydeb Kumar Sana · 5 de mayo de 2026

  Fall detection is a critical task in healthcare, particularly for elderly people. Timely fall detection and treatment can prevent severe injuries. Sensor-based activity data can be used to detect fall. However, this data are highly sensitive and raises significant privacy concerns. Existing privacy …