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Review of Computer Engineering Research

June 2021, Volume 8, 1, pp 1-7

Activity Recognition and Creation of Web Service for Activity Recognition using Mobile Sensor Data using Azure Machine Learning Studio

Muhammad Owais Raza

,

Nazia Pathan

,

Aqsa Umar

,

Raheem Bux

Muhammad Owais Raza 1 ,

Nazia Pathan 1 Aqsa Umar 1 Raheem Bux 1 
  1. Department of Software Engineering Mehran UET Jamshoro Pakistan. 1

Pages: 1-7

DOI: 10.18488/journal.76.2021.81.1.7

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Article History:

Received: 21 September, 2020
Revised: 06 November, 2020
Accepted: 08 January, 2021
Published: 04 February, 2021


Abstract:

With the increasing pervasive computation, “Activity Recognition” has become a vast and popular field of research. In the field of automated Activity Recognition, we use multiple sensors in wearable/portable devices in order to recognize the human activities such as standing still, sitting, relaxing, laying, walking, climbing stairs, knee bending cycling jogging etc. The main purpose of this paper is to discuss the field of Activity Recognition for patients and old- age persons or any person in general. This research paper can also be used for telemedicine purposes. Besides, different machine learning algorithm will be applied to achieve Activity Recognition rather precisely. Microsoft Azure ML Studio and a bench marking data set are used for creation as well as evaluation of Machine Learning Model. In addition, a Web Service for Activity Recognition is also developed by using Microsoft Azure ML Studio in order to help the developer and researcher while working on Activity Recognition.
Contribution/ Originality
The main purpose of this paper is to discuss the field of Activity Recognition for patients and old- age persons or any person in general.

Keywords:

Activity recognition, Microsoft azure, Machine learning, Automation, Neural Network, Logistic Regression, Decision Forest, Web service.

Reference:

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[3]          M. Á. Á. De la Concepción, L. M. S. Morillo, J. A. Á. García, and L. González-Abril, "Mobile activity recognition and fall detection system for elderly people using Ameva algorithm," Pervasive and Mobile Computing, vol. 34, pp. 3-13, 2017.Available at: https://doi.org/10.1016/j.pmcj.2016.05.002.

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[9]          M.-C. Kwon and C. Sunwoong, "Recognition of daily human activity using an artificial neural network and smartwatch," Wireless Communications and Mobile Computing, vol. 2018, pp. 81-89, 2018.

[10]        L. Bao and S. I. Stephen, "Activity recognition from user-annotated acceleration data," presented at the International Conference on Pervasive Computing. Springer, Berlin, Heidelberg, 2004.

[11]        UCI, "UCI machine learning repository: Human activity recognition using smartphones data set. Retrieved from: https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones," 2012.

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[13]        Xiaoharper, "Multiclass logistic regression - ML Studio (Classic) - Azure.” ML Studio (Classic) - Azure | Microsoft docs. Retrieved from: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/multiclass-logistic-regression," 2019.

[14]        Xiaoharper, "Multiclass neural network - ML Studio (Classic) - Azure.” ML Studio (Classic) - Azure | Microsoft docs. Retrieved from: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/multiclass-neural-network," 2019.

[15]        Xiaoharper, "Multiclass decision forest - ML Studio (Classic) - Azure.” ML Studio (Classic) - Azure | Microsoft Docs. Retrieved from: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/multiclass-decision-forest," 2019.

Statistics:

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Funding:

This study received no specific financial support.

Competing Interests:

The authors declare that they have no competing interests.

Acknowledgement:

All authors contributed equally to the conception and design of the study.

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