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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/5599
Title: Kinect sensor-based interaction monitoring system using the BLSTM neural network in healthcare
Authors: Saini R.
Kumar P.
Kaur B.
Pratim Roy, Partha
Dogra D.P.
Santosh K.C.
Published in: International Journal of Machine Learning and Cybernetics
Abstract: Remote monitoring of patients is considered as one of the reliable alternatives to healthcare solutions for elderly and/or chronically ill patients. Further, monitoring interaction with people plays an important role in diagnosis and in managing patients that are suffering from mental illnesses, such as depression and autism spectrum disorders (ASD). In this paper, we propose the Kinect sensor-based interaction monitoring system between two persons using the Bidirectional long short-term memory neural network (BLSTM-NN). Such model can be adopted for the rehabilitation of people (who may be suffering from ASD and other psychological disorders) by analyzing their activities. Medical professionals and caregivers for diagnosing and remotely monitoring the patients suffering from such psychological disorders can use the system. In our study, ten volunteers were involved to create five interactive groups to perform continuous activities, where the Kinect sensor was used to record data. A set of continuous activities was created using random combinations of 24 isolated activities. 3D skeleton of each user was detected and tracked using the Kinect and modeled using BLSTM-NN. We have used a lexicon by analyzing the constraints while performing continuous activities to improve the performance of the system. We have achieved the maximum accuracy of 70.72%. Our results outperformed the previously reported results and therefore the proposed system can further be used in developing internet of things (IoT) Kinect sensor-based healthcare application. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
Citation: International Journal of Machine Learning and Cybernetics (2019), 10(9): 2529-2540
URI: https://doi.org/10.1007/s13042-018-0887-5
http://repository.iitr.ac.in/handle/123456789/5599
Issue Date: 2019
Publisher: Springer Verlag
Keywords: Activity recognition
Autism spectrum disorders
Bidirectional long short-term memory neural network
Depth sensors
Healthcare
Internet of things
ISSN: 18688071
Author Scopus IDs: 57190288840
57212043589
57208659024
56880478500
35408975400
14831502300
Author Affiliations: Saini, R., Department of Computer Science and Engineering, IIT Roorkee, Roorkee, India
Kumar, P., Department of Computer Science and Engineering, IIT Roorkee, Roorkee, India
Kaur, B., Department of Computer Science and Engineering, DCRUST, Sonepat, India
Roy, P.P., Department of Computer Science and Engineering, IIT Roorkee, Roorkee, India
Dogra, D.P., School of Electrical Sciences, IIT Bhubaneshwar, Bhubaneshwar, India
Santosh, K.C., Department of Computer Science, University of South Dakota, Vermillion, SD, United States
Corresponding Author: Santosh, K.C.; Department of Computer Science, University of South DakotaUnited States; email: santosh.kc@usd.edu
Appears in Collections:Journal Publications [CS]

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