ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data

• URL: http://arxiv.org/abs/2008.03230v1
• Date: Fri, 24 Jul 2020 10:41:20 GMT
• Title: ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data
• Authors: Shohreh Deldari, Daniel V. Smith, Amin Sadri, Flora D. Salim
• Abstract summary: We propose ESPRESSO, a hybrid segmentation model for multi-dimensional time-series. ESPRESSO exploits the entropy and temporal shape properties of time-series. It offers superior performance to four state-of-the-art methods across seven public datasets of wearable and wear-free sensing.
• Score: 5.142415132534397
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extracting informative and meaningful temporal segments from high-dimensional wearable sensor data, smart devices, or IoT data is a vital preprocessing step in applications such as Human Activity Recognition (HAR), trajectory prediction, gesture recognition, and lifelogging. In this paper, we propose ESPRESSO (Entropy and ShaPe awaRe timE-Series SegmentatiOn), a hybrid segmentation model for multi-dimensional time-series that is formulated to exploit the entropy and temporal shape properties of time-series. ESPRESSO differs from existing methods that focus upon particular statistical or temporal properties of time-series exclusively. As part of model development, a novel temporal representation of time-series $WCAC$ was introduced along with a greedy search approach that estimate segments based upon the entropy metric. ESPRESSO was shown to offer superior performance to four state-of-the-art methods across seven public datasets of wearable and wear-free sensing. In addition, we undertake a deeper investigation of these datasets to understand how ESPRESSO and its constituent methods perform with respect to different dataset characteristics. Finally, we provide two interesting case-studies to show how applying ESPRESSO can assist in inferring daily activity routines and the emotional state of humans.

Related papers

• Scaling Wearable Foundation Models [54.93979158708164]
  We investigate the scaling properties of sensor foundation models across compute, data, and model size. Using a dataset of up to 40 million hours of in-situ heart rate, heart rate variability, electrodermal activity, accelerometer, skin temperature, and altimeter per-minute data from over 165,000 people, we create LSM. Our results establish the scaling laws of LSM for tasks such as imputation, extrapolation, both across time and sensor modalities.
  arXiv  Detail & Related papers  (2024-10-17T15:08:21Z)
• Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining [5.2863523790908955]
  This paper introduces a novel proposed representation model designed to address the limitations of traditional methods in multidimensional time series (MTS) analysis. The proposed framework has significant potential for applications across various fields, including services for monitoring and optimizing IT infrastructure, medical diagnosis through continuous patient monitoring, trend analysis, and internet businesses for tracking user behavior and forecasting.
  arXiv  Detail & Related papers  (2024-09-22T06:27:07Z)
• Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models [69.06149482021071]
  We propose a novel EHR data generation model called EHRPD. It is a diffusion-based model designed to predict the next visit based on the current one while also incorporating time interval estimation. We conduct experiments on two public datasets and evaluate EHRPD from fidelity, privacy, and utility perspectives.
  arXiv  Detail & Related papers  (2024-06-20T02:20:23Z)
• Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction [27.521188262343596]
  We introduce a novel model named Temporal and Heterogeneous Graph Neural Networks (THGNN) THGNN aggregates historical data from neighboring nodes to accurately capture the temporal dynamics and spatial correlations within the stream of sensor data. We have validated the effectiveness of our approach through comprehensive experiments.
  arXiv  Detail & Related papers  (2024-05-07T14:08:57Z)
• T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression [82.85825388788567]
  We develop a novel temporal clustering method, T-Phenotype, to discover phenotypes of predictive temporal patterns from labeled time-series data. We show that T-Phenotype achieves the best phenotype discovery performance over all the evaluated baselines.
  arXiv  Detail & Related papers  (2023-02-24T13:30:35Z)
• Dataset Bias in Human Activity Recognition [57.91018542715725]
  This contribution statistically curates the training data to assess to what degree the physical characteristics of humans influence HAR performance. We evaluate the performance of a state-of-the-art convolutional neural network on two HAR datasets that vary in the sensors, activities, and recording for time-series HAR.
  arXiv  Detail & Related papers  (2023-01-19T12:33:50Z)
• Self-Attention Neural Bag-of-Features [103.70855797025689]
  We build on the recently introduced 2D-Attention and reformulate the attention learning methodology. We propose a joint feature-temporal attention mechanism that learns a joint 2D attention mask highlighting relevant information.
  arXiv  Detail & Related papers  (2022-01-26T17:54:14Z)
• PSEUDo: Interactive Pattern Search in Multivariate Time Series with Locality-Sensitive Hashing and Relevance Feedback [3.347485580830609]
  PSEUDo is an adaptive feature learning technique for exploring visual patterns in multi-track sequential data. Our algorithm features sub-linear training and inference time. We demonstrate superiority of PSEUDo in terms of efficiency, accuracy, and steerability.
  arXiv  Detail & Related papers  (2021-04-30T13:00:44Z)
• 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)
• 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)
• Human Activity Recognition from Wearable Sensor Data Using Self-Attention [2.9023633922848586]
  We present a self-attention based neural network model for activity recognition from body-worn sensor data. We performed experiments on four popular publicly available HAR datasets: PAMAP2, Opportunity, Skoda and USC-HAD. Our model achieve significant performance improvement over recent state-of-the-art models in both benchmark test subjects and Leave-one-out-subject evaluation.
  arXiv  Detail & Related papers  (2020-03-17T14:16:57Z)

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.