Development of a Holistic System for Activity Classification Based on Multimodal Sensor Data

As the world of portable computers has evolved, mobile activity and mobility monitoring has become one of the major trends of recent years using widely used technologies such as smartphone sensors and wearables. These technologies are the basis for a wide range of applications in the areas of health monitoring, fitness games and telematics systems in vehicles. In the resulting use cases, the focus is on recognizing, differentiating and qualitatively evaluating different types of movements. The key factor in this context is a high degree of recognition accuracy in almost real time. Due to the ongoing development of mobile devices and the associated increase in performance, it is now possible to use the interfaces provided in mobile operating systems for the use of deep learning technologies. Due to the high availability of the end devices, new context-sensitive applications can be created, which can adapt the program logic to the current environment of a user.

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Author and article information

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Tony Rolletschke

Rico Thomanek

Christian Roschke

Marc Ritter

Conference

Publication date: July 2018

Publication date (Print): July 2018

Pages: 1-4

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[0001]Hochschule Mittweida

Technikumplatz 17

D-09648 Mittweida

Article

DOI: 10.14236/ewic/HCI2018.167

SO-VID: 31a393fc-e043-49f5-b60c-f2557e783753

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This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: Proceedings of the 32nd International BCS Human Computer Interaction Conference

Conference acronym: HCI

Conference number: 32

Conference location: Belfast, UK

Conference date: 4 - 6 July 2018

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Human Computer Interaction Conference

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1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2018.167

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

Apple 2018 ARKit - Apple Developer May 03 2018 http://developer.apple.com/arkit/
Apple 2018 Machine Learning - Apple Developer May 01 2018 http://developer.apple.com/machine-learning/
Y. ChenC. Shen 2017 Performance Analysis of Smartphone-Sensor Behavior for Human Activity Recognition IEEE Access 5 3095 3110
S. EumH. LeeH. KwonD. Doermann 2017 IOD-CNN: Integrating Object Detection Networks for Event Recognition arXiv.org
A. HenpraserttaeS. ThiemjarusS. Marukatat 2011 Accurate Activity Recognition Using a Mobile Phone Regardless of Device Orientation and Location Bsn 41 46
Hitachi 2017 DFKI and Hitachi jointly develop AI technology for human activity recognition of workers using wearable devices - News Releases June 4 2018 http://www.hitachi.com/New/cnews/month/2017/03/170308.html
A. JalalY. KimY.-J. KimS. KamalD. Kim 2017 Robust human activity recognition from depth video using spatiotemporal multi-fused features Pattern Recognition 61 295 308
J. C. NúñezR. CabidoJ. J. PantrigoA. S. MontemayorJ. F. Vélez 2018 Convolutional Neural Networks and Long Short-Term Memory for skeleton-based human activity and hand gesture recognition Pattern Recognition 76 80 94
F. OrdóñezD. Roggen 2016 Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition Sensors 16 1 115
Turi 2018 turicreate - Turi Create simplifies the development of custom machine learning models May 06 2018 github.com/apple/turicreate

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[1] Apple 2018 ARKit - Apple Developer May 03 2018 http://developer.apple.com/arkit/

[2] Apple 2018 Machine Learning - Apple Developer May 01 2018 http://developer.apple.com/machine-learning/

[3] Y. ChenC. Shen 2017 Performance Analysis of Smartphone-Sensor Behavior for Human Activity Recognition IEEE Access 5 3095 3110

[4] S. EumH. LeeH. KwonD. Doermann 2017 IOD-CNN: Integrating Object Detection Networks for Event Recognition arXiv.org

[5] A. HenpraserttaeS. ThiemjarusS. Marukatat 2011 Accurate Activity Recognition Using a Mobile Phone Regardless of Device Orientation and Location Bsn 41 46

[6] Hitachi 2017 DFKI and Hitachi jointly develop AI technology for human activity recognition of workers using wearable devices - News Releases June 4 2018 http://www.hitachi.com/New/cnews/month/2017/03/170308.html

[7] A. JalalY. KimY.-J. KimS. KamalD. Kim 2017 Robust human activity recognition from depth video using spatiotemporal multi-fused features Pattern Recognition 61 295 308

[8] J. C. NúñezR. CabidoJ. J. PantrigoA. S. MontemayorJ. F. Vélez 2018 Convolutional Neural Networks and Long Short-Term Memory for skeleton-based human activity and hand gesture recognition Pattern Recognition 76 80 94

[9] F. OrdóñezD. Roggen 2016 Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition Sensors 16 1 115

[10] Turi 2018 turicreate - Turi Create simplifies the development of custom machine learning models May 06 2018 github.com/apple/turicreate

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