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Recognizing Human Activities from Smartphone Sensor Signals

Authors:

Giuseppe RiccardiAuthors Info & Claims

MM '14: Proceedings of the 22nd ACM international conference on Multimedia

Pages 865 - 868

https://doi.org/10.1145/2647868.2655034

Published: 03 November 2014 Publication History

Abstract

In context-aware computing, Human Activity Recognition (HAR) aims to understand the current activity of users from their connected sensors. Smartphones with their various sensors are opening a new frontier in building human-centered applications for understanding users' personal and world contexts. While in-lab and controlled activity recognition systems have yielded very good results, they do not perform well under in-the-wild scenarios. The objective of this paper is to 1) Investigate how audio signal can complement and improve other on-board sensors (accelerometer and gyroscope) for activity recognition; 2) Design and evaluate the fusion of such multiple signal streams to optimize performance and sampling rate. We show that fusion of these signal streams, including audio, achieves high performance even at very low sampling rates; 3) Evaluate the performance of the multi-stream human activity recognition under different real end-user activity conditions.

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Index Terms

Recognizing Human Activities from Smartphone Sensor Signals
1. Hardware
  1. Communication hardware, interfaces and storage
    1. Signal processing systems
2. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image ACM Conferences

MM '14: Proceedings of the 22nd ACM international conference on Multimedia

November 2014

1310 pages

ISBN:9781450330633

DOI:10.1145/2647868

General Chairs:
Kien A. Hua
University of Central Florida, USA
,
Yong Rui
Microsoft Research, China
,
Ralf Steinmetz
Technische Universitt Darmstadt, Germany
,
Program Chairs:
Alan Hanjalic
Delft University of Technology, Netherlands
,
Apostol (Paul) Natsev
Google, USA
,
Wenwu Zhu
Tsinghua University, China

Copyright © 2014 ACM.

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 ACM 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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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

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Published: 03 November 2014

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MM '14: 2014 ACM Multimedia Conference

November 3 - 7, 2014

Florida, Orlando, USA

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MM '14 Paper Acceptance Rate 55 of 286 submissions, 19%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Sedaghat TLiaqat SLiaqat DWu RGershon ASon TFalk TGabel MMariakakis Ade Lara E(2022)Unobtrusive Monitoring of COPD Patients using Speech Collected from Smartwatches in the Wild2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops53856.2022.9767283(818-823)Online publication date: 21-Mar-2022
https://doi.org/10.1109/PerComWorkshops53856.2022.9767283
Oh SGa DLim J(2021)Mobile Deep Learning System That Calculates UVI Using Illuminance Value of User’s LocationSensors10.3390/s2104122721:4(1227)Online publication date: 9-Feb-2021
https://doi.org/10.3390/s21041227
Wu BMa CPoslad SSelviah D(2021)An Adaptive Human Activity-Aided Hand-Held Smartphone-Based Pedestrian Dead Reckoning Positioning SystemRemote Sensing10.3390/rs1311213713:11(2137)Online publication date: 28-May-2021
https://doi.org/10.3390/rs13112137
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Mughal FRaffe WGarcia J(2020)Emotion Recognition Techniques for Geriatric Users: A Snapshot2020 IEEE 8th International Conference on Serious Games and Applications for Health (SeGAH)10.1109/SeGAH49190.2020.9201749(1-8)Online publication date: Aug-2020
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Basly HOuarda WSayadi FOuni BAlimi A(2020)CNN-SVM Learning Approach Based Human Activity RecognitionImage and Signal Processing10.1007/978-3-030-51935-3_29(271-281)Online publication date: 8-Jul-2020
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Avilés-Cruz CFerreyra-Ramírez AZúñiga-López AVillegas-Cortéz J(2019)Coarse-Fine Convolutional Deep-Learning Strategy for Human Activity RecognitionSensors10.3390/s1907155619:7(1556)Online publication date: 31-Mar-2019
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Tobji RDi WAyoub N(2019)FMnet: Iris Segmentation and Recognition by Using Fully and Multi-Scale CNN for Biometric SecurityApplied Sciences10.3390/app91020429:10(2042)Online publication date: 17-May-2019
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