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

• URL: http://arxiv.org/abs/2609.14498v1
• Date: Sun, 13 Sep 2026 13:15:14 GMT
• Title: EdgeHAR: An Edge-Native Compact Sensor Foundation Model for Human Activity Recognition
• Abstract summary: We present textbfEdgeHAR, an edge-native compact sensor foundation model for wearable intelligence.<n>Unlike conventional models that entangle activity knowledge with acquisition variations, EdgeHAR learns transferable representations by factorizing sensor signals into three latent codes.<n>This disentangled design enables efficient adaptation to new users, devices, placements, and activity classes with limited target-domain data.
• Score: 21.107327725532652
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 present \textbf{EdgeHAR}, an edge-native compact sensor foundation model designed for wearable intelligence. Unlike conventional models that entangle activity knowledge with acquisition variations, EdgeHAR learns transferable representations by factorizing sensor signals into three latent codes: an \textbf{(i)Activity-Semantic Code} capturing reusable activity knowledge, a \textbf{(ii)Motion-Dynamics Code} modeling temporal patterns, and an \textbf{(iii)Acquisition-Context Code} representing sensor-specific variations. This disentangled design enables efficient adaptation to new users, devices, placements, and activity classes with limited target-domain data. By incorporating lightweight adaptation modules, EdgeHAR achieves foundation-model-level transferability while satisfying edge constraints in computation, memory, latency, and privacy. Experiments across heterogeneous HAR datasets demonstrate that EdgeHAR maintains competitive recognition performance under distribution shifts with substantially reduced deployment cost. EdgeHAR establishes a practical paradigm for compact, edge-first foundation models for ubiquitous sensing systems.

Related papers

• CFD-HAR: User-controllable Privacy through Conditional Feature Disentanglement [4.04401169071427]
  We propose a technique for user-controllable privacy through feature disentanglement-based representation learning.<n>We compare the efficacy of our technique against few-shot HAR using autoencoder-based representation learning.<n>Our study shows that CFD-based HAR provides explicit, tunable privacy protection controls by separating activity and sensitive attributes in the latent space.<n>Autoencoder-based few-shot HAR offers superior label efficiency and lightweight adaptability but lacks inherent privacy safeguards.
  arXiv  Detail & Related papers  (2026-03-12T04:29:18Z)
• Can Foundation Models Revolutionize Mobile AR Sparse Sensing? [2.984076446975729]
  We investigate whether foundation models can change the landscape of mobile sparse sensing.<n>Using real-world mobile AR data, our evaluations demonstrate that foundation models offer significant improvements in geometry-aware image warping.<n>Our study demonstrates the scalability of foundation model-based sparse sensing and shows its leading performance in 3D scene reconstruction.
  arXiv  Detail & Related papers  (2025-11-04T03:06:51Z)
• DISCOVER: Data-driven Identification of Sub-activities via Clustering and Visualization for Enhanced Activity Recognition in Smart Homes [46.86909768552777]
  We introduce DISCOVER, a method to discover fine-grained human sub-activities from unlabeled sensor data without relying on pre-segmentation.<n>We demonstrate its effectiveness through a re-annotation exercise on widely used HAR datasets.
  arXiv  Detail & Related papers  (2025-02-11T20:02:24Z)
• Efficient Detection Framework Adaptation for Edge Computing: A Plug-and-play Neural Network Toolbox Enabling Edge Deployment [59.61554561979589]
  Edge computing has emerged as a key paradigm for deploying deep learning-based object detection in time-sensitive scenarios.<n>Existing edge detection methods face challenges: difficulty balancing detection precision with lightweight models, limited adaptability, and insufficient real-world validation.<n>We propose the Edge Detection Toolbox (ED-TOOLBOX), which utilizes generalizable plug-and-play components to adapt object detection models for edge environments.
  arXiv  Detail & Related papers  (2024-12-24T07:28:10Z)
• Large Language Model-Guided Semantic Alignment for Human Activity Recognition [14.934473748133422]
  Human Activity Recognition (HAR) using Inertial Measurement Unit (IMU) sensors is critical for applications in healthcare, safety, and industrial production.<n> variation in activity patterns, device types, and sensor placements create distribution gaps across datasets.<n>We propose LanHAR, a novel system that generates semantic interpretations of sensor readings and activity labels for cross-dataset HAR.
  arXiv  Detail & Related papers  (2024-09-12T22:57:29Z)
• Towards Sustainable Personalized On-Device Human Activity Recognition with TinyML and Cloud-Enabled Auto Deployment [6.9604565273682955]
  This work introduces a wrist-worn smart band designed to address challenges through a novel combination of on-device TinyML-driven computing and cloud-enabled auto-deployment. Users can tailor activity classes to their unique movement styles with minimal calibration. System achieves a 37% increase in accuracy over generalized models in personalized settings.
  arXiv  Detail & Related papers  (2024-08-26T13:28:41Z)
• Layout Agnostic Human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST) [0.22354214294493352]
  We develop a layout-agnostic modeling approach for human activity recognition (HAR) systems in smart homes. We generate Textual Descriptions Of Sensor Triggers (TDOST) that encapsulate the surrounding trigger conditions. We demonstrate the effectiveness of TDOST-based models in unseen smart homes through experiments on benchmarked CASAS datasets.
  arXiv  Detail & Related papers  (2024-05-20T20:37:44Z)
• HARMamba: Efficient and Lightweight Wearable Sensor Human Activity Recognition Based on Bidirectional Mamba [7.412537185607976]
  Wearable sensor-based human activity recognition (HAR) is a critical research domain in activity perception. This study introduces HARMamba, an innovative light-weight and versatile HAR architecture that combines selective bidirectional State Spaces Model and hardware-aware design. HarMamba outperforms contemporary state-of-the-art frameworks, delivering comparable or better accuracy with significantly reducing computational and memory demands.
  arXiv  Detail & Related papers  (2024-03-29T13:57:46Z)
• Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing [74.12670841657038]
  Device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications. Data heterogeneity in wireless signals and data privacy regulation of distributed sensing have been considered as the major challenges that hinder the wide applications of wireless sensing in large area networking systems. We propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data.
  arXiv  Detail & Related papers  (2023-12-08T13:50:30Z)
• AGO-Net: Association-Guided 3D Point Cloud Object Detection Network [86.10213302724085]
  We propose a novel 3D detection framework that associates intact features for objects via domain adaptation. We achieve new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed.
  arXiv  Detail & Related papers  (2022-08-24T16:54:38Z)
• SensiX: A Platform for Collaborative Machine Learning on the Edge [69.1412199244903]
  We present SensiX, a personal edge platform that stays between sensor data and sensing models. We demonstrate its efficacy in developing motion and audio-based multi-device sensing systems. Our evaluation shows that SensiX offers a 7-13% increase in overall accuracy and up to 30% increase across different environment dynamics at the expense of 3mW power overhead.
  arXiv  Detail & Related papers  (2020-12-04T23:06:56Z)
• Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition [131.6328804788164]
  We propose a framework, named Semantics-aware Adaptive Knowledge Distillation Networks (SAKDN), to enhance action recognition in vision-sensor modality (videos) The SAKDN uses multiple wearable-sensors as teacher modalities and uses RGB videos as student modality.
  arXiv  Detail & Related papers  (2020-09-01T03:38:31Z)
• Deep Soft Procrustes for Markerless Volumetric Sensor Alignment [81.13055566952221]
  In this work, we improve markerless data-driven correspondence estimation to achieve more robust multi-sensor spatial alignment. We incorporate geometric constraints in an end-to-end manner into a typical segmentation based model and bridge the intermediate dense classification task with the targeted pose estimation one. Our model is experimentally shown to achieve similar results with marker-based methods and outperform the markerless ones, while also being robust to the pose variations of the calibration structure.
  arXiv  Detail & Related papers  (2020-03-23T10:51:32Z)
• An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices [58.62801151916888]
  We introduce a new sparsity dimension, namely pattern-based sparsity that comprises pattern and connectivity sparsity, and becoming both highly accurate and hardware friendly. Our approach on the new pattern-based sparsity naturally fits into compiler optimization for highly efficient DNN execution on mobile platforms.
  arXiv  Detail & Related papers  (2020-01-20T16:17:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.