Prism: Mining Task-aware Domains in Non-i.i.d. IMU Data for Flexible User Perception

• URL: http://arxiv.org/abs/2501.01598v1
• Date: Fri, 03 Jan 2025 02:07:42 GMT
• Title: Prism: Mining Task-aware Domains in Non-i.i.d. IMU Data for Flexible User Perception
• Authors: Yunzhe Li, Facheng Hu, Hongzi Zhu, Quan Liu, Xiaoke Zhao, Jiangang Shen, Shan Chang, Minyi Guo,
• Abstract summary: We propose a novel scheme, called Prism, which can obtain high FUP accuracy on mobile devices. The core of Prism is to discover task-aware domains embedded in IMU dataset, and to train a domain-aware model on each identified domain. Results demonstrate that Prism can achieve the best FUP performance with a low latency.
• Score: 20.61555898129175
• License:
• Abstract: A wide range of user perception applications leverage inertial measurement unit (IMU) data for online prediction. However, restricted by the non-i.i.d. nature of IMU data collected from mobile devices, most systems work well only in a controlled setting (e.g., for a specific user in particular postures), limiting application scenarios. To achieve uncontrolled online prediction on mobile devices, referred to as the flexible user perception (FUP) problem, is attractive but hard. In this paper, we propose a novel scheme, called Prism, which can obtain high FUP accuracy on mobile devices. The core of Prism is to discover task-aware domains embedded in IMU dataset, and to train a domain-aware model on each identified domain. To this end, we design an expectation-maximization (EM) algorithm to estimate latent domains with respect to the specific downstream perception task. Finally, the best-fit model can be automatically selected for use by comparing the test sample and all identified domains in the feature space. We implement Prism on various mobile devices and conduct extensive experiments. Results demonstrate that Prism can achieve the best FUP performance with a low latency.

Related papers

• PRIMUS: Pretraining IMU Encoders with Multimodal Self-Supervision [7.896850422430362]
  Unlabeled or weakly labeled IMU data can be used to model human motions. We propose PRIMUS: a method for PRetraining IMU encoderS that uses a novel pretraining objective. PRIMUS improves test accuracy by up to 15%, compared to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-11-22T18:46:30Z)
• Neural 5G Indoor Localization with IMU Supervision [63.45775390000508]
  Radio signals are well suited for user localization because they are ubiquitous, can operate in the dark and maintain privacy. Many prior works learn mappings between channel state information (CSI) and position fully-supervised. In this work, this requirement is relaxed by using pseudo-labels during deployment, which are calculated from an inertial measurement unit (IMU)
  arXiv  Detail & Related papers  (2024-02-15T13:51:21Z)
• Machine Learning-based Positioning using Multivariate Time Series Classification for Factory Environments [0.0]
  State-of-the-art solutions heavily rely on external infrastructures and are subject to potential privacy compromises. Recent developments in machine learning (ML) offer solutions to address these limitations relying only on the data from onboard sensors of IoT devices. This paper presents a machine learning-based indoor positioning system, using motion and ambient sensors, to localize a moving entity in privacy concerned factory environments.
  arXiv  Detail & Related papers  (2023-08-22T10:07:19Z)
• IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds [41.8359507387665]
  We explore the feasibility of estimating body pose using IMUs already in devices that many users own. Our pipeline receives whatever subset of IMU data is available, potentially from just a single device, and produces a best-guess pose.
  arXiv  Detail & Related papers  (2023-04-25T02:13:24Z)
• DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization [66.27399823422665]
  Device Model Generalization (DMG) is a practical yet under-investigated research topic for on-device machine learning applications. We propose an efficient Device-cloUd collaborative parametErs generaTion framework DUET.
  arXiv  Detail & Related papers  (2022-09-12T13:26:26Z)
• IMG2IMU: Translating Knowledge from Large-Scale Images to IMU Sensing Applications [6.865654843241631]
  We propose IMG2IMU that adapts pre-trained representation from large-scale images to diverse IMU sensing tasks. We convert the sensor data into visually interpretable spectrograms for the model to utilize the knowledge gained from vision. IMG2IMU outperforms the baselines pre-trained on sensor data by an average of 9.6%p F1-score.
  arXiv  Detail & Related papers  (2022-09-02T11:00:23Z)
• Online Data Selection for Federated Learning with Limited Storage [53.46789303416799]
  Federated Learning (FL) has been proposed to achieve distributed machine learning among networked devices. The impact of on-device storage on the performance of FL is still not explored. In this work, we take the first step to consider the online data selection for FL with limited on-device storage.
  arXiv  Detail & Related papers  (2022-09-01T03:27:33Z)
• SCAI: A Spectral data Classification framework with Adaptive Inference for the IoT platform [0.0]
  We propose a Spectral data Classification framework with Adaptive Inference. Specifically, to allocate different computations for different samples while better exploiting the collaboration among different devices. To the best of our knowledge, this paper is the first attempt to conduct optimization by adaptive inference for spectral detection under the IoT platform.
  arXiv  Detail & Related papers  (2022-06-24T09:22:52Z)
• GAIA: A Transfer Learning System of Object Detection that Fits Your Needs [136.60609274344893]
  Transfer learning with pre-training on large-scale datasets has played an increasingly significant role in computer vision and natural language processing. In this paper, we focus on the area of object detection and present a transfer learning system named GAIA. GAIA is capable of providing powerful pre-trained weights, selecting models that conform to downstream demands such as latency constraints and specified data domains.
  arXiv  Detail & Related papers  (2021-06-21T18:24:20Z)
• Moving Object Classification with a Sub-6 GHz Massive MIMO Array using Real Data [64.48836187884325]
  Classification between different activities in an indoor environment using wireless signals is an emerging technology for various applications. In this paper, we analyze classification of moving objects by employing machine learning on real data from a massive multi-input-multi-output (MIMO) system in an indoor environment.
  arXiv  Detail & Related papers  (2021-02-09T15:48:35Z)
• Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference [49.88536971774444]
  Inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet-of-Things applications and services. We present and release the Oxford Inertial Odometry dataset (OxIOD), a first-of-its-kind public dataset for deep learning based inertial navigation research.
  arXiv  Detail & Related papers  (2020-01-13T04:41:54Z)

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.