Towards LLM-Powered Ambient Sensor Based Multi-Person Human Activity Recognition

• URL: http://arxiv.org/abs/2407.09529v1
• Date: Tue, 25 Jun 2024 07:41:34 GMT
• Title: Towards LLM-Powered Ambient Sensor Based Multi-Person Human Activity Recognition
• Authors: Xi Chen, Julien Cumin, Fano Ramparany, Dominique Vaufreydaz,
• Abstract summary: Human Activity Recognition (HAR) is one of the central problems in fields such as healthcare, elderly care, and security at home. This paper proposes a system framework called LAHAR, based on large language models.
• Score: 4.187145402358247
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human Activity Recognition (HAR) is one of the central problems in fields such as healthcare, elderly care, and security at home. However, traditional HAR approaches face challenges including data scarcity, difficulties in model generalization, and the complexity of recognizing activities in multi-person scenarios. This paper proposes a system framework called LAHAR, based on large language models. Utilizing prompt engineering techniques, LAHAR addresses HAR in multi-person scenarios by enabling subject separation and action-level descriptions of events occurring in the environment. We validated our approach on the ARAS dataset, and the results demonstrate that LAHAR achieves comparable accuracy to the state-of-the-art method at higher resolutions and maintains robustness in multi-person scenarios.

Related papers

• HRVMamba: High-Resolution Visual State Space Model for Dense Prediction [60.80423207808076]
  State Space Models (SSMs) with efficient hardware-aware designs have demonstrated significant potential in computer vision tasks. These models have been constrained by three key challenges: insufficient inductive bias, long-range forgetting, and low-resolution output representation. We introduce the Dynamic Visual State Space (DVSS) block, which employs deformable convolution to mitigate the long-range forgetting problem. We also introduce High-Resolution Visual State Space Model (HRVMamba) based on the DVSS block, which preserves high-resolution representations throughout the entire process.
  arXiv  Detail & Related papers  (2024-10-04T06:19:29Z)
• Multidimensional Human Activity Recognition With Large Language Model: A Conceptual Framework [0.0]
  In high-stake environments like emergency response or elder care, the integration of large language model (LLM) revolutionizes risk assessment, resource allocation, and emergency responses. We propose a conceptual framework that utilizes various wearable devices, each considered as a single dimension, to support a multidimensional learning approach within Human Activity Recognition (HAR) systems.
  arXiv  Detail & Related papers  (2024-09-16T21:36:23Z)
• A Comprehensive Methodological Survey of Human Activity Recognition Across Divers Data Modalities [2.916558661202724]
  Human Activity Recognition (HAR) systems aim to understand human behaviour and assign a label to each action. HAR can leverage various data modalities, such as RGB images and video, skeleton, depth, infrared, point cloud, event stream, audio, acceleration, and radar signals. This paper presents a comprehensive survey of the latest advancements in HAR from 2014 to 2024.
  arXiv  Detail & Related papers  (2024-09-15T10:04:44Z)
• FFAA: Multimodal Large Language Model based Explainable Open-World Face Forgery Analysis Assistant [59.2438504610849]
  We introduce FFAA: Face Forgery Analysis Assistant, consisting of a fine-tuned Multimodal Large Language Model (MLLM) and Multi-answer Intelligent Decision System (MIDS) Our method not only provides user-friendly and explainable results but also significantly boosts accuracy and robustness compared to previous methods.
  arXiv  Detail & Related papers  (2024-08-19T15:15:20Z)
• MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild [81.32127423981426]
  Multimodal emotion recognition based on audio and video data is important for real-world applications. Recent methods have focused on exploiting advances of self-supervised learning (SSL) for pre-training of strong multimodal encoders. We propose a different perspective on the problem and investigate the advancement of multimodal DFER performance by adapting SSL-pre-trained disjoint unimodal encoders.
  arXiv  Detail & Related papers  (2024-04-13T13:39:26Z)
• FGAHOI: Fine-Grained Anchors for Human-Object Interaction Detection [4.534713782093219]
  A novel end-to-end transformer-based framework (FGAHOI) is proposed to alleviate the above problems. FGAHOI comprises three dedicated components namely, multi-scale sampling (MSS), hierarchical spatial-aware merging (HSAM) and task-aware merging mechanism (TAM)
  arXiv  Detail & Related papers  (2023-01-08T03:53:50Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• Seeing Differently, Acting Similarly: Imitation Learning with Heterogeneous Observations [126.78199124026398]
  In many real-world imitation learning tasks, the demonstrator and the learner have to act in different but full observation spaces. In this work, we model the above learning problem as Heterogeneous Observations Learning (HOIL) We propose the Importance Weighting with REjection (IWRE) algorithm based on the techniques of importance-weighting, learning with rejection, and active querying to solve the key challenge of occupancy measure matching.
  arXiv  Detail & Related papers  (2021-06-17T05:44:04Z)
• Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study [75.42182503265056]
  Multi-Task Learning has emerged as a methodology in which multiple tasks are jointly learned by a shared learning algorithm. We deal with heterogeneous MTL, simultaneously addressing detection, classification & regression problems. We build FaceBehaviorNet, the first framework for large-scale face analysis, by jointly learning all facial behavior tasks.
  arXiv  Detail & Related papers  (2021-05-08T22:26:52Z)
• Personalized Semi-Supervised Federated Learning for Human Activity Recognition [1.9014535120129343]
  We propose FedHAR: a novel hybrid method for human activities recognition. FedHAR combines semi-supervised and federated learning. We show that FedHAR reaches recognition rates and personalization capabilities similar to state-of-the-art FL supervised approaches.
  arXiv  Detail & Related papers  (2021-04-15T10:24:18Z)
• B-HAR: an open-source baseline framework for in depth study of human activity recognition datasets and workflows [1.7639472693362923]
  This paper proposes an open-source framework, named B-HAR, for the definition, standardization, and development of a baseline framework. It implements the most popular data processing methods for data preparation and the most commonly used machine and deep learning pattern recognition models.
  arXiv  Detail & Related papers  (2021-01-23T12:42:41Z)

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.