Context-Aware Activity Recognition Systems

Últimos artículos

• RAST: Resolution-Aware Privileged Structure Transfer for Low-Resolution Audio Activity Recognition

  Ji Hwan Park, Gautham Krishna Gudur, Yufei Shen, Dawei Liang, Edison Thomaz · 1 de octubre de 2026

  Audio is increasingly used for human activity recognition (HAR) because it captures object interactions, environmental events, and contextual cues in everyday environments. High-resolution (HR) audio provides rich acoustic information for model development but incurs substantial energy and storage c…

• More Features Are Not More Evidence: Limits of Training-Free Human Activity Recognition with Jev

  Orhan Konak · 30 de septiembre de 2026

  General-purpose models promise sensor-based decisions without training a task-specific classifier, which could reduce the dependence of Human Activity Recognition (HAR) on labeled data. Yet it remains unclear whether such models can directly interpret deterministic descriptions of physical sensor si…

• Federated Multi-Modal Human Activity Recognition using Multi-Agent Reinforcement Learning

  Debasmita Dey, Tanmay Sen, Himel Mallick · 29 de septiembre de 2026

  Human Activity Recognition (HAR) from heterogeneous wearable sensors is fundamental to the Internet of Health Things (IoHT), supporting rehabilitation, elderly care, and smart healthcare. Existing multimodal fusion methods often assign fixed equal weights to sensor streams, overlooking differences i…

• Temporal Graph Learning of Wearable Actigraphy and Sleep Traces for Modelling Adolescent Crystallized Intelligence

  Md. Tanvir Rahman, Nabil Anan Orka, Asaduzzaman Khan, Mohammad Ali Moni · 29 de septiembre de 2026

  Wearable actigraphy offers a scalable, ecologically valid alternative to episodic clinical assessment. However, predicting continuous adolescent crystallized intelligence ($G_c$) from such traces remains challenging due to irregular device adherence and complex behavioral-environmental interactions.…

• SenseAgent: An LLM Agent for Adaptive Cross-Domain IMU Sensing

  Tianya Zhao, Chuan Liu, Xuyu Wang · 29 de septiembre de 2026

  Deep learning has improved inertial measurement unit (IMU) sensing for mobile and wearable applications. However, an IMU model trained in one domain often becomes unreliable when it is used with a new user, device, or body position. Existing methods usually treat this problem as a static model-desig…

• When Temporal Perturbations Act Like Sensor Biases: Label-Free Auditing of Wearable Activity Recognizers

  Qingyu Wu, Yuan Wei, Renju Liu, Hua Cheng · 25 de septiembre de 2026

  Wearable human-activity recognition (HAR) models operate across sensors, subjects, and backbones, yet a smooth waveform may appear temporal while exploiting a persistent sensor offset primarily. We introduce SpectrumAudit, a label-sealed audit that fits a phase-randomized full-window stimulus on cal…

• When Adaptation Hurts: Split Sensitivity and Person-Level Negative Transfer in Federated Wearable Onboarding

  Rahil Aftab, Vineet Kumar Rakesh, Soumya Mazumdar, Tapas Samanta · 24 de septiembre de 2026

  Federated wearable models eventually serve people absent from source training, but favorable average accuracy does not establish that unlabeled onboarding helps each person. We evaluate six core onboarding strategies on five wearable datasets under a leakage-controlled protocol that fixes source che…

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

  Jiaxi Yin, Ge Wang, Han Ding, Fei Wang · 21 de septiembre de 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 de septiembre de 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 de septiembre de 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 de septiembre de 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 de septiembre de 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 de septiembre de 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 de septiembre de 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 de septiembre de 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 de agosto de 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 de agosto de 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 de agosto de 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 de agosto de 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 de agosto de 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 de agosto de 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 de agosto de 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 de agosto de 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 de agosto de 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 de agosto de 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…