Context-Aware Activity Recognition Systems

Derniers papiers

• 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…

• Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model

  Yuliang Chen, Weiwei Shi, Jingjing Zou, Rong Zablocki, Animesh Kumar, Jordan A. Carlson, Sheri J. Hartman, Mikael Anne Greenwood-Hickman, Paul R. Hibbing, Marta Jankowska, Jay Yang, Arun Kumar, Loki Natarajan · 5 août 2026

  Accurate detection of sedentary behavior is important for studying health risks related to prolonged sitting, but posture-based classification remains challenging with wearable sensors, especially at the wrist. We study whether a deep learning model trained on hip-worn accelerometer data can transfe…

• TRACE-TS: Attribution-Grounded and Traceable Sensor-Language Reasoning for Human Activity Understanding

  Sparsh Rastogi, Tanmay Kumar, Baiyu Chen, Jatin Bedi, Zechen Li, Flora D. Salim · 4 août 2026

  Wearable sensors capture fine-grained motion patterns that support rich behavioral understanding, yet most existing methods reduce these signals to activity labels. Recent LM-based approaches generate natural-language explanations for sensor data, but their reasoning is weakly grounded in the underl…

• DeepConvContext: A Multi-Scale Approach to Timeseries Classification in Human Activity Recognition

  Marius Bock, Juergen Gall, Michael Moeller, Kristof Van Laerhoven · 4 août 2026

  Despite recognized limitations in modeling long-range temporal dependencies, Human Activity Recognition (HAR) has traditionally relied on a sliding window approach to segment labeled datasets. Deep learning models like the DeepConvLSTM typically classify each window independently, restricting learna…

• RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment

  Hansi Karunarathna, Nirhoshan Sivaroopan, Chamara Madarasingha, Anura Jayasumana, Kanchana Thilakarathna · 30 juillet 2026

  Human Activity Recognition (HAR) from wearable sensors supports applications in healthcare, rehabilitation, fitness tracking, and smart environments. Yet, existing deep learning approaches require dataset-specific training, large labeled corpora, and repeated adaptation to new sensor settings or act…

• HiMe: Real-Time Self-Hosted Personal Agent Platform for Health Insights with Wearable Devices

  Wei Liu, Siya Qi, Linhai Zhang, Lorainne Tudor Car, Yulan He · 24 juillet 2026

  Traditional approaches to wearable health signal analysis, such as smartwatches, are constrained by rigid analytical frameworks and limited personalisation. The emergence of LLM agents creates a new opportunity for Personal Health Agentic Analysis, where health insights can be generated adaptively a…

• Joint-Embedding Predictive Architecture for Sensor-based Activity Recognition

  Mohd Halim Mohd Noor, Abdulrahman M. A. Baraka · 21 juillet 2026

  Sensor-based human activity recognition (HAR) has achieved significant progressed in fully supervised learning settings. However, these supervised learning models rely on large amount of labeled data, which require labor-intensive collection and meticulous annotation. To address these challenges, th…

• Unsupervised Keypoints for Real-Time Fall Detection: Comparative Analysis Under Real-world Conditions with Predictive Bandwidth Reduction

  Tasmiah Haque, Jacob Kosinski, Sumit Mohan, Srinjoy Das, Mohammad Abdullah Al-Mamun · 20 juillet 2026

  Falls among older adults are a major safety challenge, but continuous monitoring is difficult to sustain. Video captures fall-related posture and motion, yet deployment is limited by privacy, computation, and bandwidth. Supervised pose estimation is anatomically interpretable but vulnerable to occlu…

• Towards Real-World Wearable Motion Reconstruction

  Andrea Boscolo Camiletto, Rishabh Dabral, Eduardo Alvarado, Thabo Beeler, Marc Habermann, Christian Theobalt · 14 juillet 2026

  The modern-day surge in popularity of wearable devices poses a fundamentally unique motion capture problem: reconstructing full-body movement from any set of sensing hardware worn at a given moment. Yet, most research efforts assume fixed sensor configurations (e.g. IMU suits or HMD-centric rigs) an…

• Data-Efficient Deep Learning: Empirical Guidelines for Training Set Size Estimation in Inertial Sensor Classification

  Ofir Kruzel, Itzik Klien · 13 juillet 2026

  Deep learning models dependency on large-scale inertial datasets presents a significant bottleneck in inertial sensor-based classification tasks, such as human activity recognition and smartphone location recognition. In these domains, data collection requires massive recording campaigns that are co…

• Inertia-1: An Open Exploration of Wearable Motion Foundation Models

  Zongzhe Xu, Aakarsh Anand, Sarah Jiang, Chuntung Zhuang, Zitao Shuai, Sriram Sankararaman, Yuzhe Yang · 9 juillet 2026

  Wearable motion sensing provides a continuous and scalable window into human behavior and health, making it a natural fit for foundation models, yet its pretraining and scaling principles remain poorly understood. Prior work studies isolated design choices, such as sensor placement or sampling frequ…

• Physical activities enable scalable foundation modelling for broad-spectrum health prediction

  Zhenghuang Wu, Yuyao Zhu, Songlin Xu · 9 juillet 2026

  Wearable and mobile sensing technologies have demonstrated strong potential for health inference; however, most sensor models are designed for specific disease types, limiting their transferability across different health risks. Wearable foundation models offer a more generalizable approach in diver…

• Representing and Detecting Label Ambiguity in IMU-Based Exercise Evaluation

  Andreas Spilz, Heiko Oppel, Michael Munz · 7 juillet 2026

  Home-based physiotherapy is performed without supervision, which leads to incorrect execution and motivates systems that assess movement automatically from inertial measurement units (IMUs). Such systems assign each repetition to a category, yet a relevant share of repetitions falls near a class bou…

• FedAvg for HAR: Exploring the Tradeoff Between Personalized and Generalization Accuracy

  Andrea De Luna, Susanna Peretti, Chiara Contoli, Alessandro Bogliolo · 7 juillet 2026

  The federated learning (FL) paradigm fosters distributed pervasive computing combined with artificial intelligence techniques, allowing for optimized data usage and improved mitigation of privacy concerns. Indeed, model training occurs on the client's local devices, and model parameters are subseque…

• STELLA: Efficient Sensor-to-LLM Translation for On-Device Human Activity Recognition

  Nirhoshan Sivaroopan, Albert Zomaya, Kanchana Thilakarathna · 7 juillet 2026

  HAR is increasingly expected to run continuously on edge devices, yet recent LLM-based methods remain hard to deploy: raw sensor prompts are long, cloud inference adds latency and privacy risk, and fine-tuned LLM pipelines turn general-purpose models into task-specific classifiers. We present STELLA…

• A Comparison of Fusion Techniques for Multi-Modal Human Activity Recognition on the HARMES Dataset

  Ahmed Mohamady, Robin Burchard, Kristof Van Laerhoven · 29 juin 2026

  Recent advances in Human Activity Recognition (HAR) from wearable sensors have shown that multi-modal deep learning models consistently outperform their uni-modal counterparts. Modalities can include IMUs, RGB cameras, audio signals, and others. One important aspect of multi-modal deep learning is t…

• Autoencoder Architectures for Athlete Performance Scoring from Wearable Telemetry

  Mateusz Kubita, Jan Zubalewicz, Krzysztof Siwek · 29 juin 2026

  Wearable devices produce large, high dimensional training logs for everyday runners, and interpretation rather than data collection is now the limiting step. This paper evaluates five dimensionality reduction models, three autoencoder variants, PCA, and a Variational Autoencoder, on their ability to…

• Assessing Distribution Shift in Human Activity Recognition for Domain Generalization

  Rebecca Adaimi, Edison Thomaz · 24 juin 2026

  While the field of Human Activity Recognition (HAR) continues to draw interest from researchers and advance in important ways, some key challenges remain. One of the most difficult aspects of building HAR models that show good performance in real-world settings is dealing with data diversity from de…

• CLIP-guided Diffusion Model for Backdoor Generation in Sensor-based Human Activity Recognition

  Toby Briston, Illya Kosyk, Kuniyih S · 23 juin 2026

  Sensors are critical components of modern intelligent devices. The proliferation of the Internet of Things (IoT) and wearable mobile devices has enabled the integration of such sensors to monitor the environment and enable users to take predictive actions. Human activity recognition (HAR) is a popul…

• Towards a Bathroom-Centered Human-Building Digital Twin Framework for Indoor Safety Analysis

  Yuanzhi Su (Cynthia), Huiying (Cynthia), Hou · 23 juin 2026

  Bathroom use is a critical safety challenge for older adults because wet surfaces, constrained layouts, limited support, and frequent posture transitions are concentrated within a small domestic space. These conditions create risks that cannot be adequately understood by considering either the bathr…

• Low-Cost Neuromorphic Fall Detection Using Synthetic Event Data and Hybrid SNNs

  Guillermo Rojas, Gonzalo Soto, Daniel Yunge · 18 juin 2026

  This work presents the development of hybrid models that integrate spiking neural networks (SNNs) with components of convolutional neural networks (CNNs) to learn from simulated event-based camera data (Dynamic Vision Sensor, DVS) generated from conventional smartphone videos. Aimed primarily at hum…

• Efficient Temporal Modeling for Mobile Sleep Staging via Lightweight Random Attention

  Guisong Liu, Pengfei Wei, Jainsong Zhang, Martin Dresler · 15 juin 2026

  Mobile sleep staging serves as a foundational infrastructure for in-home sleep monitoring and closed-loop modulation. But existing sequential models such as RNNs and Transformers are computationally expensive for mobile deployment. In this paper, we propose Random Attention (RA), a lightweight tempo…

• Closing the Modality Gap in Zero-Shot HAR: Contrastive Training and Separability-Optimized Prototypes on IMU Data

  Anik Ghosh · 10 juin 2026

  Zero-shot learning (ZSL) for inertial measurement unit (IMU)-based human activity recognition (HAR) faces a central challenge: bridging the gap between sensor embeddings and semantic class representations. We systematically evaluate seven configurations combining three inference methods with two tra…

• EVA: Evolving Semantic Adversaries for Red-Teaming GUI Agents Against Environmental Injection Attacks

  Yijie Lu, Manman Zhao, Tianjie Ju, Zihe Yan, Xinbei Ma, Yuan Guo, Daizong Ding, Gongshen Liu, Zhuosheng Zhang · 8 juin 2026

  Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) are increasingly deployed yet vulnerable to Environmental Injection Attacks (EIAs).However, current red-teaming methods are hindered by prohibitive computational costs and limited adaptability. A fundamental qu…

• Gravity-Aware Hierarchical Routing for Lightweight SensorLLM on Human Activity Recognition

  Hao Li, Mingrui Zheng, Yasuyuki Tahara, Yuichi Sei · 4 juin 2026

  Recent studies on sensor-language alignment have shown that two-stage frameworks can improve the semantic modeling ability of wearable-sensor human activity recognition (HAR), where SensorLLM-style methods first perform motion-to-language alignment and then fine-tune the model for downstream tasks. …