Context-Aware Activity Recognition Systems

Derniers papiers

• PSEE: Progressive Sensor Event Expansion for Point-Supervised Temporal Action Localization

  Jiaxi Yin, Ge Wang, Han Ding, Fei Wang · 21 septembre 2026

  Temporal action localization (TAL) in wearable sensor streams identifies action classes and temporal boundaries, enabling finer-grained activity understanding than conventional action recognition. However, training typically requires costly start--end annotations for every action instance. To reduce…

• You Don't Need To Train: Agentic Heuristic Learning Studio for Executable Human Activity Recognition

  Siyu Yuan, He Zhang, Sizhen Bian, Bin Guo · 16 septembre 2026

  Human activity recognition (HAR) is usually framed as gradient-based training of neural networks. Agentic Heuristic Learning (AHL) Studio explores a complementary view inspired by human cognitive learning: people learn activities by remembering examples, forming rules, and repairing mistakes, not by…

• Efficient On-Device Agents via Adaptive Context Management

  Sanidhya Vijayvargiya, Rahul Lokesh · 16 septembre 2026

  On-device AI agents offer the potential for personalized, low-latency assistance, but their deployment is fundamentally constrained by limited memory capacity. Context in agentic settings worsens this problem due to large static tool schemas and a growing interaction history that continually expands…

• EdgeHAR: An Edge-Native Compact Sensor Foundation Model for Human Activity Recognition

  He Zhang, Siyu Yuan, Siyu Liu, Sizhen Bian, Bin Guo · 15 septembre 2026

  Sensor-based human activity recognition (HAR) is fundamental to ubiquitous and wearable computing, yet existing foundation models are largely designed for cloud-scale deployment and struggle with real-world sensing shifts, including unseen users, devices, sampling rates, and sensor placements. We pr…

• Domain Generalization for Smartphone-Based Human Activity Recognition: A Systematic Analysis of Components and Interactions

  Ot\'avio Oliveira Napoli, Edson Borin · 15 septembre 2026

  Smartphone-based Human Activity Recognition (HAR) models often degrade under distribution shifts caused by changes in users, devices, sensor placements, environments, and acquisition protocols. Domain Generalization (DG) addresses this problem by learning from source domains without access to target…

• SAFER-Activities: A Dataset for Smart Assessment of Fall Events and Routine Activities

  Diwas Lamsal, Pramod Wickramatilake, Jednipat Moonrinta, Mongkol Ekpanyapong, Matthew N. Dailey · 9 septembre 2026

  Smart healthcare monitoring systems require precise action recognition to ensure well-being and timely intervention in critical situations such as falls, particularly for mobility-challenged individuals. Existing datasets are often clip-based, lacking the frame-level detail needed to recognize actio…

• Quantum-Assisted Memory-Efficient Training for Parameter-Intensive Wi-Fi-Based Human Activity Recognition

  To Truong An, Jie Zhang, Guolin Yin, Junqing Zhang, Yanjiao Li, Trung Q. Duong, Simon L. Cotton · 7 septembre 2026

  Wi-Fi-based human activity recognition (HAR) has become an important part of integrated sensing and communications, paving the way for a range of context-aware services. However, most existing Wi-Fi-based HAR systems rely on deep learning (DL) models that are computationally and memory intensive in …

• WearableQA: A Benchmark for Health Reasoning over Real-World Wearable Data

  Ji Soo Lee, Xilun Chen, Pierce Chuang, Ashish Shenoy, Jason Wei, Dohwan Ko, Hyunwoo J. Kim, Benoit Corda · 7 septembre 2026

  Recent advances in wearable sensing enable continuous monitoring of physiological and behavioral signals, yet existing benchmarks rarely evaluate whether AI systems can reason over a real user's longitudinal wearable record. We introduce WearableQA, a benchmark comprising 4,084 10-option multiple-ch…

• Under-Mattress Temporal Sensing for Next-Day Agitation Risk Scoring in Dementia Wards

  Zhen Liu, Marta Bono, Robbe Decloedt, Ajda Flisar, Maarten Van Den Bossche, Maarten De Vos · 31 août 2026

  Agitation fluctuates over short time horizons in people living with dementia, yet continuous physiological information for anticipating next-day risk is limited. We assessed whether contactless under-mattress signals from the preceding night inform next-day agitation risk and whether preserving minu…

• Dynamic Influence-Weighted Distillation for Single-IMU Activity Recognition

  Bingxuan Xie · 27 août 2026

  Inertial sensors at multiple body locations can improve activity recognition, but requiring every sensor at inference increases the deployment burden. We study whether four synchronized IMUs available during training can improve a student that uses only the right-arm IMU during fitting and inference…

• Evaluating Deep Multivariate Imputation Models on Wearable Device Data

  Skye Goodman, Roussel Desmond Nzoyem, Leandro Junges, Peter Kissack, Yasser Qureshi, Amberly Brigden, Jeff Clark, Nawid Keshtmand · 26 août 2026

  Wearable device data enables continuous health monitoring, but suffers from structured missingness: features sharing a physical sensor drop out together. Deep imputation methods such as BRITS and SAITS have seen limited evaluation on multimodal physiological data under realistic missingness, and exi…

• TransfHAR: Self-Supervised Wrist Representations for On-Demand Activity Recognition

  Aidan Bradshaw, Riku Arakawa, Xin Liu, Karan Ahuja · 18 août 2026

  Fine-grained wrist activity recognition can support applications such as procedural step guidance and context-aware assistance, yet acquiring labeled data for every new task, user, and activity granularity remains a bottleneck. We present TransfHAR, a self-supervised wrist IMU framework for on-deman…

• Rotation-Invariant Multi-IMU Activity Recognition under Independent Per-Location Orientation Shifts

  Seungyeol Baek, Yoonbyung Chai, Yonghyeon Lee, Sungjoon Choi, Sungho Suh · 18 août 2026

  Human Activity Recognition (HAR) with self-administered wearables, such as at-home rehabilitation and exercise monitoring, often requires reattaching inertial measurement units (IMUs) across sessions. In multi-IMU settings, this can induce independent orientation offsets across body locations, a dep…

• Foundation models for movement data: Are they ready for prime-time?

  Alexander Br\"auer, Benjamin Cauchi, Nils Strodthoff · 14 août 2026

  Foundation models (FMs) trained on large-scale accelerometer data have been proposed as general-purpose feature extractors for health monitoring, but systematic evidence of their advantages is lacking. We present the first comprehensive evaluation of four open-source accelerometer FMs against superv…

• Beyond Simulated Benchmarks: Evaluating Motion Representations for Fall Detection Under Real-World Data Scarcity

  Timilehin B. Aderinola, Ilaria D'Ascanio, Luca Palmerini, Lorenzo Chiari, Jochen Klenk, Clemens Becker, Brian Caulfield, Georgiana Ifrim · 14 août 2026

  Falls are a major health concern for older adults, and wearable sensors have been widely explored for detecting falls and enabling timely intervention. However, real-world falls are extremely rare: collecting 100 of them requires an estimated 100,000 days of monitoring, resulting in severely limited…

• P2MFDS: A Privacy-Preserving Multimodal Fall Detection System for Elderly People in Bathroom Environments

  Haitian Wang, Yiren Wang, Xinyu Wang, Yumeng Miao, Yuliang Zhang, Yu Zhang, Atif Mansoor · 13 août 2026

  By 2050, people aged 65 and over are projected to make up 16% of the global population. As aging is closely associated with increased fall risk, particularly in wet and confined environments such as bathrooms where over 80% of falls occur. Although recent research has increasingly focused on non-int…

• Deep Multimodal Wearable Sensor Fusion for Detection of Body-Focused Repetitive Behaviors

  Samaneh Rezaeimanesh, Mohsen Behradfar, Mohammad Fili, Guiping Hu · 11 août 2026

  Body-focused repetitive behaviors, such as hair pulling and skin picking, are compulsive motor actions commonly associated with obsessive-compulsive and anxiety disorders. Their early, objective detection remains difficult because the movements are subtle and overlap with ordinary, non-pathological …

• LITEWAY: LIghtweight HAR via Temporal Efficient highWAY

  Dominique Nshimyimana, Vitor Fortes Rey, Mengxi Liu, Bo Zhou, Paul Lukowicz · 11 août 2026

  Wearable human activity recognition (HAR) remains challenging due to the computational and energy constraints of deep learning models on resource-limited devices. Existing lightweight approaches often rely on recurrent architectures (e.g., GRU and LSTM), limiting parallelism and increasing inference…

• FemWear: A Specialized Wearable Foundation Model for Women's Health

  Yifan Wang, Chenzhong Li · 11 août 2026

  General wearable foundation models are pretrained across broad sensor streams and populations, but are not designed around women's-health tasks. We introduce FemWear, a specialized wearable foundation model that parameter-efficiently repurposes a pretrained multimodal wearable backbone. FemWear reta…

• Democratizing Ski Safety: Real-Time Turn Segmentation with Smartphone IMU and Causal LSTM Networks

  Micha{\l} Szymocha, Piotr Kacprzak, Jakub Robak, Wojciech Turek · 11 août 2026

  Anterior cruciate ligament (ACL) injury is one of the most common and serious injuries in sports, particularly among recreational skiers. Research shows that structured technique awareness and continuous feedback can significantly reduce the risk of such injuries, yet access to professional instruct…

• MAUPITI: On-Device Prototype-Based Learning on a Smart Infrared Sensor

  Beatrice Alessandra Motetti, Tanguy Dugas du Villard, Matteo Risso, Alessio Burrello, Francesco Daghero, Enrico Macii, Massimo Poncino, Marco Castellano, Alfio Basile, Daniele Jahier Pagliari · 10 août 2026

  Low-resolution infrared (IR) array sensors represent an interesting solution for privacy-preserving human sensing in embedded systems. In this letter, we describe a smart multi-pixel IR sensor integrating a 16$\times$16 thermal MOSFET (TMOS) array and a RISC-V microcontroller extended with low-preci…

• Omni-modal decomposition autoencoders learn full-stack wearable disentangled representations

  Ioannis Ziogas, Ensieh Khazaei, Bilal Taha, Aamna Al Shehhi, Ahsan H. Khandoker, Leontios J. Hadjileontiadis, Dimitrios Hatzinakos · 10 août 2026

  Learning disentangled representations is a key requirement for developing versatile, general-purpose, and sustainable models in multi-modal wearable computing. However, existing approaches do not operate as full-stack wearable processors, i.e., they do not simultaneously address task-specific classi…

• Mobile Interaction for Assessing Fatigue, Sleep, and Activity in Neurodegenerative and Chronic Diseases

  Julian Fierrez, Alejandro Pe\~na, Aythami Morales, Ruben Tolosana, Ruben Vera-Rodriguez, Meenakshi Chatterjee, Ahmaniemi Teemu, Wan-Fai Ng, Walter Maetzler, Nikolay V. Manyakov, Jennifer Kudelka, Ralf Reilmann, C. Janneke van der Woude, Kristen Davies, Victoria Macrae, IDEA-FAST Consortium · 10 août 2026

  Fatigue, sleep, or disturbances in daily activities are common symptoms among patients with neurodegenerative disorders (NDD) and immune-mediated inflammatory diseases (IMID). The current assessment of such symptoms is usually conducted using patient reported outcomes (PROs) based on standardized qu…

• From Sports to Safety: Benchmarking Proactive Risk Inference in MLLMs

  Jiawei Qiu, Yichen Xu, Jianzhe Ma, Mingyang Yu, Wenbin Zhu, Yang Han, Pinzheng Lv, Wenxuan Wang · 7 août 2026

  Timely anticipation of physical hazards is essential for real-world safety, yet existing MLLM evaluations focus on harmful content or general risks, leaving proactive physical hazard prediction underexplored. Sports provide a well-suited testbed: accident causes span diverse injury dimensions and pr…

• VSMP-IMU: Video-Grounded Semantic Motion Programs for Sensor-Aware Synthetic IMU Generation

  Lala Shakti Swarup Ray, Vitor Fortes Rey, Mengxi Liu, Paul Lukowicz, Bo Zhou · 7 août 2026

  Wearable human activity recognition (HAR) is often limited by the scarcity of labeled sensor data, especially in low-resource, class-imbalanced, and subject-generalization settings. Synthetic IMU generation can reduce this dependency and enhance HAR machine learning model's performance, but existing…