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Title: SmartARM: A smartphone-based group activity recognition and monitoring scheme for military applications
Authors: Mukherjee, A.
Misra, Sudip
Mangrulkar, P.
Rajarajan, Muttukrishnan
Rahulamathavan, Yogachandran
Keywords: Smartphone
Activity recognition
Sensors
Classifier
Group activity learning
Issue Date: 2018
Publisher: IEEE
Citation: MUKHERJEE, A. ... et al., 2018. SmartARM: A smartphone-based group activity recognition and monitoring scheme for military applications. Presented at the 11th IEEE International Conference on Advanced Networks and Telecommunications Systems, (ANTS 2017), Bhubaneswar, India, 17-20 Dec.
Abstract: © 2017 IEEE. In this paper we propose SmartARM-A Smartphone-based group Activity Recognition and Monitoring (ARM) scheme, which is capable of recognizing and centrally monitoring coordinated group and individual group member activities of soldiers in the context of military excercises. In this implementation, we specifically consider military operations, where the group members perform similar motions or manoeuvres on a mission. Additionally, remote administrators at the command center receive data from the smartphones on a central server, enabling them to visualize and monitor the overall status of soldiers in situations such as battlefields, urban operations and during soldier's physical training. This work establishes-(a) the optimum position of smartphone placement on a soldier, (b) the optimum classifier to use from a given set of options, and (c) the minimum sensors or sensor combinations to use for reliable detection of physical activities, while reducing the data-load on the network. The activity recognition modules using the selected classifiers are trained on available data-sets using a test-train-validation split approach. The trained models are used for recognizing activities from live smartphone data. The proposed activity detection method puts forth an accuracy of 80% for real-time data.
Description: Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Version: Accepted for publication
DOI: 10.1109/ANTS.2017.8384149
URI: https://dspace.lboro.ac.uk/2134/36323
Publisher Link: https://doi.org/10.1109/ANTS.2017.8384149
ISBN: 9781538623473
Appears in Collections:Conference Papers and Presentations (Loughborough University London)

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