Proximity-Based Active Learning on Streaming Data: A Personalized Eating Moment Recognition

• URL: http://arxiv.org/abs/2003.13098v1
• Date: Sun, 29 Mar 2020 18:17:29 GMT
• Title: Proximity-Based Active Learning on Streaming Data: A Personalized Eating Moment Recognition
• Authors: Marjan Nourollahi, Seyed Ali Rokni, Hassan Ghasemzadeh
• Abstract summary: We propose Proximity-based Active Learning on Streaming data, a novel proximity-based model for recognizing eating gestures. Our analysis on data collected in both controlled and uncontrolled settings indicates that the F-score of PLAS ranges from 22% to 39% for a budget that varies from 10 to 60 query.
• Score: 17.961752949636306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting when eating occurs is an essential step toward automatic dietary monitoring, medication adherence assessment, and diet-related health interventions. Wearable technologies play a central role in designing unubtrusive diet monitoring solutions by leveraging machine learning algorithms that work on time-series sensor data to detect eating moments. While much research has been done on developing activity recognition and eating moment detection algorithms, the performance of the detection algorithms drops substantially when the model trained with one user is utilized by a new user. To facilitate development of personalized models, we propose PALS, Proximity-based Active Learning on Streaming data, a novel proximity-based model for recognizing eating gestures with the goal of significantly decreasing the need for labeled data with new users. Particularly, we propose an optimization problem to perform active learning under limited query budget by leveraging unlabeled data. Our extensive analysis on data collected in both controlled and uncontrolled settings indicates that the F-score of PLAS ranges from 22% to 39% for a budget that varies from 10 to 60 query. Furthermore, compared to the state-of-the-art approaches, off-line PALS, on average, achieves to 40% higher recall and 12\% higher f-score in detecting eating gestures.

Related papers

• Incremental Self-training for Semi-supervised Learning [56.57057576885672]
  IST is simple yet effective and fits existing self-training-based semi-supervised learning methods. We verify the proposed IST on five datasets and two types of backbone, effectively improving the recognition accuracy and learning speed.
  arXiv  Detail & Related papers  (2024-04-14T05:02:00Z)
• Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines [83.65380507372483]
  Large pre-trained models can dramatically reduce the amount of task-specific data required to solve a problem, but they often fail to capture domain-specific nuances out of the box. This paper shows how to leverage recent advances in NLP and multi-modal learning to augment a pre-trained model with search engine retrieval.
  arXiv  Detail & Related papers  (2023-11-29T05:33:28Z)
• Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings [4.4353357514621745]
  In this paper, we consider a fine-grain stress detection problem based on wearable sensors targeting everyday settings. We develop a multilayered sensor-edge-cloud platform to periodically capture physiological signals and process them in real-time. We show that the context-aware active learning technique we propose achieves a desirable detection performance using 88% and 32% fewer queries from users.
  arXiv  Detail & Related papers  (2023-04-28T22:09:19Z)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• Temporal Output Discrepancy for Loss Estimation-based Active Learning [65.93767110342502]
  We present a novel deep active learning approach that queries the oracle for data annotation when the unlabeled sample is believed to incorporate high loss. Our approach achieves superior performances than the state-of-the-art active learning methods on image classification and semantic segmentation tasks.
  arXiv  Detail & Related papers  (2022-12-20T19:29:37Z)
• Responsible Active Learning via Human-in-the-loop Peer Study [88.01358655203441]
  We propose a responsible active learning method, namely Peer Study Learning (PSL), to simultaneously preserve data privacy and improve model stability. We first introduce a human-in-the-loop teacher-student architecture to isolate unlabelled data from the task learner (teacher) on the cloud-side. During training, the task learner instructs the light-weight active learner which then provides feedback on the active sampling criterion.
  arXiv  Detail & Related papers  (2022-11-24T13:18:27Z)
• Stream-based active learning with linear models [0.7734726150561089]
  In production, instead of performing random inspections to obtain product information, labels are collected by evaluating the information content of the unlabeled data. We propose a new strategy for the stream-based scenario, where instances are sequentially offered to the learner. The iterative aspect of the decision-making process is tackled by setting a threshold on the informativeness of the unlabeled data points.
  arXiv  Detail & Related papers  (2022-07-20T13:15:23Z)
• Bayesian Active Learning for Wearable Stress and Affect Detection [0.7106986689736827]
  Stress detection using on-device deep learning algorithms has been on the rise owing to advancements in pervasive computing. In this paper, we propose a framework with capabilities to represent model uncertainties through approximations in Bayesian Neural Networks. Our proposed framework achieves a considerable efficiency boost during inference, with a substantially low number of acquired pool points.
  arXiv  Detail & Related papers  (2020-12-04T16:19:37Z)
• A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches [8.257740966456172]
  This paper presents a complete framework towards the automated i) modeling of in-meal eating behavior and ii) temporal localization of meals. We present an end-to-end Neural Network which detects food intake events (i.e. bites) We show how the distribution of the detected bites throughout the day can be used to estimate the start and end points of meals, using signal processing algorithms.
  arXiv  Detail & Related papers  (2020-10-12T12:35:56Z)
• Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
  We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training. We experimentally verify that the new dataset can significantly improve the ability of the learned FER model. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
  arXiv  Detail & Related papers  (2020-05-18T09:36:51Z)
• Deep ConvLSTM with self-attention for human activity decoding using wearables [0.0]
  We propose a deep neural network architecture that captures features of multiple sensor time-series data but also selects important time points. We show the validity of the proposed approach across different data sampling strategies and demonstrate that the self-attention mechanism gave a significant improvement. The proposed methods open avenues for better decoding of human activity from multiple body sensors over extended periods time.
  arXiv  Detail & Related papers  (2020-05-02T04:30:31Z)

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.