ADLGen: Synthesizing Symbolic, Event-Triggered Sensor Sequences for Human Activity Modeling

• URL: http://arxiv.org/abs/2505.17987v1
• Date: Fri, 23 May 2025 14:52:48 GMT
• Title: ADLGen: Synthesizing Symbolic, Event-Triggered Sensor Sequences for Human Activity Modeling
• Authors: Weihang You, Hanqi Jiang, Zishuai Liu, Zihang Xie, Tianming Liu, Jin Lu, Fei Dou,
• Abstract summary: ADLGen is a generative framework designed to synthesize realistic, event triggered, and symbolic sensor sequences.<n>ADLGen is shown to outperform baseline generators in verifying statistical fidelity, semantic richness, and downstream activity recognition.
• Score: 9.526073030523733
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real world collection of Activities of Daily Living data is challenging due to privacy concerns, costly deployment and labeling, and the inherent sparsity and imbalance of human behavior. We present ADLGen, a generative framework specifically designed to synthesize realistic, event triggered, and symbolic sensor sequences for ambient assistive environments. ADLGen integrates a decoder only Transformer with sign based symbolic temporal encoding, and a context and layout aware sampling mechanism to guide generation toward semantically rich and physically plausible sensor event sequences. To enhance semantic fidelity and correct structural inconsistencies, we further incorporate a large language model into an automatic generate evaluate refine loop, which verifies logical, behavioral, and temporal coherence and generates correction rules without manual intervention or environment specific tuning. Through comprehensive experiments with novel evaluation metrics, ADLGen is shown to outperform baseline generators in statistical fidelity, semantic richness, and downstream activity recognition, offering a scalable and privacy-preserving solution for ADL data synthesis.

Related papers

• Zero-Shot EEG-to-Gait Decoding via Phase-Aware Representation Learning [9.49131859415923]
  We propose NeuroDyGait, a domain-generalizable EEG-to-motion decoding framework.<n>It uses structured contrastive representation learning and relational domain modeling to achieve semantic alignment between EEG and motion embeddings.<n>It achieves zero-shot motion prediction for unseen individuals without requiring adaptation and superior performance in cross-subject gait decoding on benchmark datasets.
  arXiv  Detail & Related papers  (2025-06-24T06:03:49Z)
• Make It Efficient: Dynamic Sparse Attention for Autoregressive Image Generation [8.624395048491275]
  We propose a novel training-free context optimization method called Adaptive Dynamic Sparse Attention (ADSA)<n>ADSA identifies historical tokens crucial for maintaining local texture consistency and those essential for ensuring global semantic coherence, thereby efficiently streamlining attention.<n>We also introduce a dynamic KV-cache update mechanism tailored for ADSA, reducing GPU memory consumption during inference by approximately $50%$.
  arXiv  Detail & Related papers  (2025-06-23T01:27:06Z)
• EASE: Embodied Active Event Perception via Self-Supervised Energy Minimization [6.249768559720122]
  Active event perception is essential for embodied intelligence in tasks such as human-AI collaboration, assistive robotics, and autonomous navigation.<n>We propose EASE, a self-supervised framework that unifies representation learning and embodied control through free energy.
  arXiv  Detail & Related papers  (2025-06-20T23:45:51Z)
• Bounding Box-Guided Diffusion for Synthesizing Industrial Images and Segmentation Map [50.21082069320818]
  We propose a novel diffusion-based pipeline for generating high-fidelity industrial datasets with minimal supervision.<n>Our approach conditions the diffusion model on enriched bounding box representations to produce precise segmentation masks.<n>Results demonstrate that diffusion-based synthesis can bridge the gap between artificial and real-world industrial data.
  arXiv  Detail & Related papers  (2025-05-06T15:21:36Z)
• STaRFormer: Semi-Supervised Task-Informed Representation Learning via Dynamic Attention-Based Regional Masking for Sequential Data [4.351581973358463]
  Transformer-based approach, STaRFormer, serves as a universal framework for sequential modeling.<n> STaRFormer employs a novel, dynamic attention-based regional masking scheme combined with semi-supervised contrastive learning to enhance task-specific latent representations.
  arXiv  Detail & Related papers  (2025-04-14T11:03:19Z)
• CAD-VAE: Leveraging Correlation-Aware Latents for Comprehensive Fair Disentanglement [24.818829983471765]
  Deep generative models may inherit or amplify biases and fairness issues by encoding sensitive attributes alongside predictive features.<n>We propose CAD-VAE (Correlation-Aware Disentangled VAE), which introduces a correlated latent code to capture the shared information between target and sensitive attributes.<n>Experiments on benchmark datasets demonstrate that CAD-VAE produces fairer representations, realistic counterfactuals, and improved fairness-aware image editing.
  arXiv  Detail & Related papers  (2025-03-11T00:32:56Z)
• Agent-driven Generative Semantic Communication with Cross-Modality and Prediction [57.335922373309074]
  We propose a novel agent-driven generative semantic communication framework based on reinforcement learning. In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling. The effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework.
  arXiv  Detail & Related papers  (2024-04-10T13:24:27Z)
• Synthetic location trajectory generation using categorical diffusion models [50.809683239937584]
  Diffusion models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data. We propose using DPMs for the generation of synthetic individual location trajectories (ILTs) which are sequences of variables representing physical locations visited by individuals.
  arXiv  Detail & Related papers  (2024-02-19T15:57:39Z)
• Reliability in Semantic Segmentation: Can We Use Synthetic Data? [69.28268603137546]
  We show for the first time how synthetic data can be specifically generated to assess comprehensively the real-world reliability of semantic segmentation models. This synthetic data is employed to evaluate the robustness of pretrained segmenters. We demonstrate how our approach can be utilized to enhance the calibration and OOD detection capabilities of segmenters.
  arXiv  Detail & Related papers  (2023-12-14T18:56:07Z)
• Are Synthetic Data Useful for Egocentric Hand-Object Interaction Detection? [12.987587227876565]
  We investigate the effectiveness of synthetic data in enhancing egocentric hand-object interaction detection. By leveraging only 10% of real labeled data, we achieve improvements in Overall AP compared to baselines trained exclusively on real data.
  arXiv  Detail & Related papers  (2023-12-05T11:29:00Z)
• Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences [118.91584633024907]
  A novel joint sensing, communication, and artificial intelligence (AI) framework is proposed so as to optimize extended reality (XR) experiences over terahertz (THz) wireless systems.
  arXiv  Detail & Related papers  (2023-04-29T00:39:50Z)
• Towards Robust Unsupervised Disentanglement of Sequential Data -- A Case Study Using Music Audio [17.214062755082065]
  Disentangled sequential autoencoders (DSAEs) represent a class of probabilistic graphical models. We show that the vanilla DSAE suffers from being sensitive to the choice of model architecture and capacity of the dynamic latent variables. We propose TS-DSAE, a two-stage training framework that first learns sequence-level prior distributions.
  arXiv  Detail & Related papers  (2022-05-12T04:11:25Z)
• CAFE: Learning to Condense Dataset by Aligning Features [72.99394941348757]
  We propose a novel scheme to Condense dataset by Aligning FEatures (CAFE) At the heart of our approach is an effective strategy to align features from the real and synthetic data across various scales. We validate the proposed CAFE across various datasets, and demonstrate that it generally outperforms the state of the art.
  arXiv  Detail & Related papers  (2022-03-03T05:58:49Z)
• Unsupervised Domain Adaptive Salient Object Detection Through Uncertainty-Aware Pseudo-Label Learning [104.00026716576546]
  We propose to learn saliency from synthetic but clean labels, which naturally has higher pixel-labeling quality without the effort of manual annotations. We show that our proposed method outperforms the existing state-of-the-art deep unsupervised SOD methods on several benchmark datasets.
  arXiv  Detail & Related papers  (2022-02-26T16:03:55Z)
• Latent Event-Predictive Encodings through Counterfactual Regularization [0.9449650062296823]
  We introduce a SUrprise-GAted Recurrent neural network (SUGAR) using a novel form of counterfactual regularization. We test the model on a hierarchical sequence prediction task, where sequences are generated by alternating hidden graph structures.
  arXiv  Detail & Related papers  (2021-05-12T18:30:09Z)

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.