FlowAR: une plateforme uniformisée pour la reconnaissance des activités humaines à partir de capteurs binaires

• URL: http://arxiv.org/abs/2502.09067v1
• Date: Thu, 13 Feb 2025 08:32:24 GMT
• Title: FlowAR: une plateforme uniformisée pour la reconnaissance des activités humaines à partir de capteurs binaires
• Authors: Ali Ncibi, Luc Bouganim, Philippe Pucheral,
• Abstract summary: This demo showcases a platform for developing human activity recognition (AR) systems. With a data-driven approach, this platform, named FlowAR, features a three-step pipeline (flow): data cleaning, segmentation, and personalized classification.
• Score: 0.716879432974126
• License:
• Abstract: This demo showcases a platform for developing human activity recognition (AR) systems, focusing on daily activities using sensor data, like binary sensors. With a data-driven approach, this platform, named FlowAR, features a three-step pipeline (flow): data cleaning, segmentation, and personalized classification. Its modularity allows flexibility to test methods, datasets, and ensure rigorous evaluations. A concrete use case demonstrates its effectiveness.

Related papers

• Consistency Based Weakly Self-Supervised Learning for Human Activity Recognition with Wearables [1.565361244756411]
  We describe a weakly self-supervised approach for recognizing human activities from sensor-based data. We show that our approach can help the clustering algorithm achieve comparable performance in identifying and categorizing the underlying human activities.
  arXiv  Detail & Related papers  (2024-07-29T06:29:21Z)
• A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition [63.26015736148707]
  This paper introduces a novel methodology to resolve the issue of optimal sensor placement for Human Activity Recognition. The derived skeleton data provides a unique strategy for identifying the optimal sensor location. Our findings indicate that the vision-based method for sensor placement offers comparable results to the conventional deep learning approach.
  arXiv  Detail & Related papers  (2023-07-06T10:38:14Z)
• ALP: Action-Aware Embodied Learning for Perception [60.64801970249279]
  We introduce Action-Aware Embodied Learning for Perception (ALP) ALP incorporates action information into representation learning through a combination of optimizing a reinforcement learning policy and an inverse dynamics prediction objective. We show that ALP outperforms existing baselines in several downstream perception tasks.
  arXiv  Detail & Related papers  (2023-06-16T21:51:04Z)
• infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information [68.76707843019886]
  infoVerse is a universal framework for dataset characterization. infoVerse captures multidimensional characteristics of datasets by incorporating various model-driven meta-information. In three real-world applications (data pruning, active learning, and data annotation), the samples chosen on infoVerse space consistently outperform strong baselines.
  arXiv  Detail & Related papers  (2023-05-30T18:12:48Z)
• Human Activity Recognition Using Self-Supervised Representations of Wearable Data [0.0]
  Development of accurate algorithms for human activity recognition (HAR) is hindered by the lack of large real-world labeled datasets. Here we develop a 6-class HAR model with strong performance when evaluated on real-world datasets not seen during training.
  arXiv  Detail & Related papers  (2023-04-26T07:33:54Z)
• Reinforcement Learning from Passive Data via Latent Intentions [86.4969514480008]
  We show that passive data can still be used to learn features that accelerate downstream RL. Our approach learns from passive data by modeling intentions. Our experiments demonstrate the ability to learn from many forms of passive data, including cross-embodiment video data and YouTube videos.
  arXiv  Detail & Related papers  (2023-04-10T17:59:05Z)
• An adaptive human-in-the-loop approach to emission detection of Additive Manufacturing processes and active learning with computer vision [76.72662577101988]
  In-situ monitoring and process control in Additive Manufacturing (AM) allows the collection of large amounts of emission data. This data can be used as input into 3D and 2D representations of the 3D-printed parts. The aim of this paper is to propose an adaptive human-in-the-loop approach using Machine Learning techniques.
  arXiv  Detail & Related papers  (2022-12-12T15:11:18Z)
• Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection [85.11649974840758]
  3D object detection networks tend to be biased towards the data they are trained on. We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors.
  arXiv  Detail & Related papers  (2021-11-30T18:42:42Z)
• Contrastive Predictive Coding for Human Activity Recognition [5.766384728949437]
  We introduce the Contrastive Predictive Coding framework to human activity recognition, which captures the long-term temporal structure of sensor data streams. CPC-based pre-training is self-supervised, and the resulting learned representations can be integrated into standard activity chains. It leads to significantly improved recognition performance when only small amounts of labeled training data are available.
  arXiv  Detail & Related papers  (2020-12-09T21:44:36Z)
• Federated Learning with Heterogeneous Labels and Models for Mobile Activity Monitoring [0.7106986689736827]
  On-device Federated Learning proves to be an effective approach for distributed and collaborative machine learning. We propose a framework for federated label-based aggregation, which leverages overlapping information gain across activities. Empirical evaluation with the Heterogeneity Human Activity Recognition (HHAR) dataset on Raspberry Pi 2 indicates an average deterministic accuracy increase of at least 11.01%.
  arXiv  Detail & Related papers  (2020-12-04T11:44:17Z)
• Handling Missing Annotations in Supervised Learning Data [0.0]
  Activity of Daily Living (ADL) recognition is an example of systems that exploit very large raw sensor data readings. The size of the generated dataset is so huge that it is almost impossible for a human annotator to give a certain label to every single instance in the dataset. In this work, we propose and investigate three different paradigms to handle these gaps.
  arXiv  Detail & Related papers  (2020-02-17T18:23:56Z)

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.