Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding

• URL: http://arxiv.org/abs/2307.00883v1
• Date: Mon, 3 Jul 2023 09:29:27 GMT
• Title: Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding
• Authors: Yidong Zhu, Md Mahmudur Rahman, Mohammad Arif Ul Alam
• Abstract summary: We present a novel modified-recurrent plot-based image representation that seamlessly integrates both temporal and frequency domain information. We evaluate the proposed method using accelerometer-based activity recognition data and a pretrained ResNet model, and demonstrate its superior performance compared to existing approaches.
• Score: 4.458210211781739
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep learning advancements have revolutionized scalable classification in many domains including computer vision. However, when it comes to wearable-based classification and domain adaptation, existing computer vision-based deep learning architectures and pretrained models trained on thousands of labeled images for months fall short. This is primarily because wearable sensor data necessitates sensor-specific preprocessing, architectural modification, and extensive data collection. To overcome these challenges, researchers have proposed encoding of wearable temporal sensor data in images using recurrent plots. In this paper, we present a novel modified-recurrent plot-based image representation that seamlessly integrates both temporal and frequency domain information. Our approach incorporates an efficient Fourier transform-based frequency domain angular difference estimation scheme in conjunction with the existing temporal recurrent plot image. Furthermore, we employ mixup image augmentation to enhance the representation. We evaluate the proposed method using accelerometer-based activity recognition data and a pretrained ResNet model, and demonstrate its superior performance compared to existing approaches.

Related papers

• Time Step Generating: A Universal Synthesized Deepfake Image Detector [0.4488895231267077]
  We propose a universal synthetic image detector Time Step Generating (TSG) TSG does not rely on pre-trained models' reconstructing ability, specific datasets, or sampling algorithms. We test the proposed TSG on the large-scale GenImage benchmark and it achieves significant improvements in both accuracy and generalizability.
  arXiv  Detail & Related papers  (2024-11-17T09:39:50Z)
• Adaptive Domain Learning for Cross-domain Image Denoising [57.4030317607274]
  We present a novel adaptive domain learning scheme for cross-domain image denoising. We use existing data from different sensors (source domain) plus a small amount of data from the new sensor (target domain) The ADL training scheme automatically removes the data in the source domain that are harmful to fine-tuning a model for the target domain. Also, we introduce a modulation module to adopt sensor-specific information (sensor type and ISO) to understand input data for image denoising.
  arXiv  Detail & Related papers  (2024-11-03T08:08:26Z)
• Descriptor: Face Detection Dataset for Programmable Threshold-Based Sparse-Vision [0.8271394038014485]
  This dataset is an annotated, temporal-threshold-based vision dataset for face detection tasks derived from the same videos used for Aff-Wild2. We anticipate that this resource will significantly support the development of robust vision systems based on smart sensors that can process based on temporal-difference thresholds.
  arXiv  Detail & Related papers  (2024-10-01T03:42:03Z)
• Training and Predicting Visual Error for Real-Time Applications [6.687091041822445]
  We explore the abilities of convolutional neural networks to predict a variety of visual metrics without requiring either reference or rendered images. Our solution combines image-space information that is readily available in most state-of-the-art deferred shading pipelines with reprojection from previous frames to enable an adequate estimate of visual errors.
  arXiv  Detail & Related papers  (2023-10-13T14:14:00Z)
• Deep Convolutional Pooling Transformer for Deepfake Detection [54.10864860009834]
  We propose a deep convolutional Transformer to incorporate decisive image features both locally and globally. Specifically, we apply convolutional pooling and re-attention to enrich the extracted features and enhance efficacy. The proposed solution consistently outperforms several state-of-the-art baselines on both within- and cross-dataset experiments.
  arXiv  Detail & Related papers  (2022-09-12T15:05:41Z)
• HyperTime: Implicit Neural Representation for Time Series [131.57172578210256]
  Implicit neural representations (INRs) have recently emerged as a powerful tool that provides an accurate and resolution-independent encoding of data. In this paper, we analyze the representation of time series using INRs, comparing different activation functions in terms of reconstruction accuracy and training convergence speed. We propose a hypernetwork architecture that leverages INRs to learn a compressed latent representation of an entire time series dataset.
  arXiv  Detail & Related papers  (2022-08-11T14:05:51Z)
• Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation [47.984368369734995]
  We introduce a novel recurrent encoding-decoding neural network architecture for event-based optical flow estimation. The network is end-to-end trained with self-supervised learning on the Multi-Vehicle Stereo Event Camera dataset. We have shown that it outperforms all the existing state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2021-09-10T13:37:37Z)
• Time Series Imaging for Link Layer Anomaly Classification in Wireless Networks [0.6015898117103068]
  In this paper, we perform a first time analysis of image-based representation techniques for wireless anomaly detection. We propose a new deep learning architecture enabling accurate anomaly detection. Our results demonstrate the potential of transformation of time series signals to images to improve classification performance.
  arXiv  Detail & Related papers  (2021-04-02T10:23:06Z)
• Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information [52.635997570873194]
  This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information. The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
  arXiv  Detail & Related papers  (2021-01-12T20:08:18Z)
• Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition [131.6328804788164]
  We propose a framework, named Semantics-aware Adaptive Knowledge Distillation Networks (SAKDN), to enhance action recognition in vision-sensor modality (videos) The SAKDN uses multiple wearable-sensors as teacher modalities and uses RGB videos as student modality.
  arXiv  Detail & Related papers  (2020-09-01T03:38:31Z)
• Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms [3.9023554886892438]
  This paper reviews the signal to image encoding approaches found in the literature. We propose modifications to some of their original formulations to make them more robust to the variability in large datasets. The selected encoding methods are Gramian Angular Field, Markov Transition Field, recurrence plot, grey scale encoding, spectrogram, and scalogram.
  arXiv  Detail & Related papers  (2020-05-14T14:42:06Z)

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.