TARU PUBLICATIONS
Journal of Interdisciplinary Mathematics cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-012X·ISSN (Print): 0972-0502

Monthly Journal: Publishes the methodological and theoretical role of mathematics and mathematical applications underpinning scientific research.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
submissions@tarupublications.com
Open Access Research Article

Improve QoS for multi-body sensor analytics in smart healthcare system using machine learning algorithm

* , , , , ,

* Corresponding author · click or hover a name for details

pp. 393–405Vol. 26Issue 3April 2023DOI: 10.47974/JIM-1670XML
Published Online:
01 Apr 2023
Article type:
Research Article
Language:
EN
Article no.:
JIM-1670
Pages:
393–405

Abstract

Embracing significant learning methods for human lead affirmation has shown suitable in taking out discriminants from the coarse information packs obtained from body-mounted sensors. But human headway is ideal coded in a movement of moderate models, the standard AI strategy is to finished certification obligations without taking advantage of the normal relationship between analysis information tests. This paper proposes the use of (DRNN) to manufacture a psychological model that can get critical distance conditions with factor-length input position. We present unidirectional, bidirectional, and comfortable models concerning DRNNs with LSTM and finding parameters using sporadic benchmark datasets. Exploratory results show that the proposed model is superior to a standard AI-based system. SVM and Nearest Neighbour Method (KNN). Moreover, In this Paper implementation smart system runs in tendency to other significant learning techniques like Deep Trust Organization (DBN) and CNN. Human Action Acknowledgment (HAR) assignments were consistently made using arranged highlights got by heuristic cycles.

Keywords

Subject Classifications

97R60

References

[1] Rashidi, P.; Cook, D.J. The resident in the loop: Adapting the smart home to the user. IEEE Trans. Syst. Man. Cybern. J. Part A, 39, 949-959 (2009).
[2] Patel, S.; Park, H.; Bonato, P.; Chan, L.; Rodgers, M. A review of wearable sensors and systems with application in rehabilitation. J. NeuroEng. Rehabil, 9 (2012), doi:10.1186/1743-0003-9-21.
[3] Avci, A.; Bosch, S.; Marin-Perianu, M.; Marin-Perianu, R.; Havinga, P. Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey. In Proceedings of the 23rd International Conference on Architecture of Computing Systems (ARCS), Hannover, Germany, pp. 1-10 (22–23 Febuary 2010).
[4] Mazilu, S.; Blanke, U.; Hardegger, M.; Tröster, G.; Gazit, E.; Hausdorff,
[5] J.M. GaitAssist: A Daily-Life Support and Training System for Parkinson’s Disease Patients with Freezing of Gait. In Proceedings of the ACM Conference on Human Factors in Computing Systems (SIGCHI), Toronto, ON, Canada, 26 April 2014.
[6] Kranz, M.; Möller, A.; Hammerla, N.; Diewald, S.; Plötz, T.; Olivier, P.; Roalter, L. The mobile fitness coach: Towards individualized skill assessment using personalized mobile devices. Perv. Mob. Comput., 9, 203-215 (2013).
[7] Stiefmeier, T.; Roggen, D.; Ogris, G.; Lukowicz, P.; Tröster, G. Wearable Activity Tracking in Car Manufacturing. IEEE Perv. Comput. Mag., 7, 42-50 (2008).
[8] Chavarriaga, R.; Sagha, H.; Calatroni, A.; Digumarti, S.; Millán, J.; Roggen, D.; Tröster, G. The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition. Pattern Recognit. Lett. 34, 2033-2042 (2013).
[9] Bulling, A.; Blanke, U.; Schiele, B. A Tutorial on Human Activity Recognition Using Body-worn Inertial Sensors. ACM Comput. Surv. (2014).
[10] Roggen, D.; Cuspinera, L.P.; Pombo, G.; Ali, F.; Nguyen-Dinh, L. Limited- Memory Warping LCSS for Real-Time Low-Power Pattern Recognition in Wireless Nodes. In Proceedings of the 12th European Conference Wireless Sensor Networks (EWSN), Porto, Portugal, 9–11; pp. 151-167 (February 2015).
[11] Ordonez, F.J.; Englebienne, G.; de Toledo, P.; van Kasteren, T.; Sanchis, A.; Krose, B. In-Home Activity Recognition: Bayesian Inference for Hidden Markov Models. Perv. Comput. IEEE, 13, 67-75 (2014).
[12] Preece, S.J.; Goulermas, J.Y.; Kenney, L.P.J.; Howard, D.; Meijer, K.; Crompton, R. Activity identification using body-mounted sensors: A review of classification techniques. Physiol. Meas., 30, 21-27 (2009).
[13] Figo, D.; Diniz, P.C.; Ferreira, D.R.; Cardoso, J.M.P. Preprocessing techniques for context recognition from accelerometer data. Perv. Mob. Comput., 14, 645-662 (2010).
[14] Lee, H.; Grosse, R.; Ranganath, R.; Ng, A.Y. Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations. In Proceedings of the 26th Annual International Conference on Machine Learning (ICML), Montreal, QC, Canada, pp. 609-616 (14–18 June 2009).
[15] Graves, A.; Mohamed, A.; Hinton, G. Speech recognition with deep recurrent neural networks. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada (26–31 May 2013).
[16] Sundermeyer, M.; Schlüter, R.; Ney, H. LSTM Neural Networks for Language Modeling. In Proceedings of the Thirteenth Annual Conference of the International Speech Communication Association, Portland, OR, USA (9-13 September 2012).

Views: 304Downloads: 77Citations: 0