research-article

A Multi-Sensor Setting Activity Recognition Simulation Tool

Authors:

Shingo Takeda,

Tsuyoshi Okita,

Paula Lago,

Sozo InoueAuthors Info & Claims

UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers

Pages 1444 - 1448

https://doi.org/10.1145/3267305.3267509

Published: 08 October 2018 Publication History

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Abstract

Motion capture generates data which are often more accurate than those captured by multiple of accelerometer sensors by their physical specification. Based on the observation that accelerometer data can be obtained by the second derivation of position data from motion capture, we propose a simulator, called MEASURed, for activity recognition classifiers. MEASURed can accommodate any number of virtual accelerometer sensors on the body based on some given motion capture data. Therefore, MEASURed can evaluate activity recognition classifiers in settings with different number, placement, and sampling rate of accelerometer sensors. Our results show that the F1-Score estimated by MEASURed is close to that obtained with the real accelerometer data.

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O. Banos, A. Calatroni, and et al. 2012. Kinect=IMU? Learning MIMO Signal Mappings to Automatically Translate Activity Recognition Systems Across Sensor Modalities. In Proc. of the 16th ISWC. IEEE Comp. Soc., Washington, DC, USA, 92--99.

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Yi XZhou YXu F(2024)Physical Non-inertial Poser (PNP): Modeling Non-inertial Effects in Sparse-inertial Human Motion CaptureACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657436(1-11)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657436
Hao YLou XWang BZheng R(2024)CROMOSim: A Deep Learning-Based Cross-Modality Inertial Measurement SimulatorIEEE Transactions on Mobile Computing10.1109/TMC.2022.323037023:1(302-312)Online publication date: Jan-2024
https://doi.org/10.1109/TMC.2022.3230370
Xue HCao QMiao CJu YHu HZhang ASu LCosta XHassanieh HAsadi ACox LPerino DWidmer JGiustiniano D(2023)Towards Generalized mmWave-based Human Pose Estimation through Signal AugmentationProceedings of the 29th Annual International Conference on Mobile Computing and Networking10.1145/3570361.3613302(1-15)Online publication date: 2-Oct-2023
https://dl.acm.org/doi/10.1145/3570361.3613302
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Index Terms

A Multi-Sensor Setting Activity Recognition Simulation Tool
1. Computing methodologies
  1. Modeling and simulation

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UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers

October 2018

1881 pages

ISBN:9781450359665

DOI:10.1145/3267305

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 08 October 2018

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UbiComp '18: The 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing

October 8 - 12, 2018

Singapore, Singapore

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Yi XZhou YXu F(2024)Physical Non-inertial Poser (PNP): Modeling Non-inertial Effects in Sparse-inertial Human Motion CaptureACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657436(1-11)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657436
Hao YLou XWang BZheng R(2024)CROMOSim: A Deep Learning-Based Cross-Modality Inertial Measurement SimulatorIEEE Transactions on Mobile Computing10.1109/TMC.2022.323037023:1(302-312)Online publication date: Jan-2024
https://doi.org/10.1109/TMC.2022.3230370
Xue HCao QMiao CJu YHu HZhang ASu LCosta XHassanieh HAsadi ACox LPerino DWidmer JGiustiniano D(2023)Towards Generalized mmWave-based Human Pose Estimation through Signal AugmentationProceedings of the 29th Annual International Conference on Mobile Computing and Networking10.1145/3570361.3613302(1-15)Online publication date: 2-Oct-2023
https://dl.acm.org/doi/10.1145/3570361.3613302
Xia CSugiura Y(2022)Virtual IMU Data Augmentation by Spring-Joint Model for Motion Exercises Recognition without Using Real DataProceedings of the 2022 ACM International Symposium on Wearable Computers10.1145/3544794.3558460(79-83)Online publication date: 11-Sep-2022
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Hao YWang BZheng R(2022)CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator2022 2nd International Workshop on Cyber-Physical-Human System Design and Implementation (CPHS)10.1109/CPHS56133.2022.9804560(1-6)Online publication date: May-2022
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Kwon HAbowd GPlötz T(2021)Complex Deep Neural Networks from Large Scale Virtual IMU Data for Effective Human Activity Recognition Using WearablesSensors10.3390/s2124833721:24(8337)Online publication date: 13-Dec-2021
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Fortes Rey VGarewal KLukowicz P(2021)Translating Videos into Synthetic Training Data for Wearable Sensor-Based Activity Recognition Systems Using Residual Deep Convolutional NetworksApplied Sciences10.3390/app1107309411:7(3094)Online publication date: 31-Mar-2021
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Kwon HTong CHaresamudram HGao YAbowd GLane NPloetz T(2021)Can You See It?GetMobile: Mobile Computing and Communications10.1145/3486880.348689125:2(38-42)Online publication date: 15-Sep-2021
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Liu YZhang SGowda M(2021)When Video meets Inertial SensorsProceedings of the International Conference on Internet-of-Things Design and Implementation10.1145/3450268.3453537(182-194)Online publication date: 18-May-2021
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Improving Sensor-based Activity Recognition Using Motion Capture as Additional Information

Activity Recognition using Head Worn Inertial Sensors

Body sensor networks to evaluate standing balance: interpreting muscular activities based on inertial sensors

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Improving Sensor-based Activity Recognition Using Motion Capture as Additional Information

Activity Recognition using Head Worn Inertial Sensors

Body sensor networks to evaluate standing balance: interpreting muscular activities based on inertial sensors

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