Abstract
Background
A wearable sensor (WS) is a prominent technology application that senses and gathers information from a user for analyzing changes in physiological signs. Analyzing the physiological sign differences enables the better healthcare solutions.
Purpose
This paper introduces an unsynchronized sensor data analytics (USDA) model for the effective handling of wearable device data regardless of the time factor. Time-dependent healthcare treatments and diagnosis are the themes on which this analytics model focuses.
Methods
The gathered WS data is classified depending on the time factor and data frequency of occurrence. This occurrence frequency is correlatively analyzed using the diagnosis module to identify defects and to fulfill the missing sensor data consideration. Healthcare diagnoses requiring immediate responses and timely solutions for patients/end-users rely on this model for uncompromising analysis.
Results
The vital changes in WS data and time factors are analyzed using sophisticated machine learning methods for previous diagnosis correlation and effective accuracy.
Conclusion
Responsive healthcare solutions using unsynchronized WS data help to achieve better efficiency and reduce complications in assessing the performance of the healthcare systems.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
An Q, Gan S, Xu J, Bao Y, Wu T, Kong H, Niu L (2019) A multichannel electrochemical all-solid-state wearable potentiometric sensor for real-time sweat ion monitoring. Electrochem Commun 107:106553
Baskar S, Shakeel PM, Kumar R, Burhanuddin MA, Sampath R (2020) A dynamic and interoperable communication framework for controlling the operations of wearable sensors in smart healthcare applications. Comput Commun 149:17–26
Bernal EA, Yang X, Li Q, Kumar J, Madhvanath S, Ramesh P, Bala R (2017) Deep temporal multimodal fusion for medical procedure monitoring using wearable sensors. IEEE Trans Multim 20(1):107–118
Bora DJ (2019) Big Data Analytics in Healthcare: a critical analysis. In: Big Data Analytics for Intelligent Healthcare Management (pp. 43–57). Academic Press
Din S, Paul A (2019) Smart health monitoring and management system: toward autonomous wearable sensing for internet of things using big data analytics. Fut Generat Comput Syst 91:611–619
Fan Y, Jin H, Ge Y, Wang N (2019) Wearable motion attitude dand data analysis based on internet of things. IEEE Access 8:1327–1338
Flachenecker F, Gaßner H, Hannik J, Lee DH, Flachenecker P, Winkler J, Klucken J (2020) Objective sensor-based gait measures reflect motor impairment in multiple sclerosis patients: reliability and clinical validation of a wearable sensor device. Multiple Scler Relat Disord 39:101903
Fouad H, Mahmoud NM, Issawi EMS, Al-Feel H (2020) Distributed and scalable computing framework for improving request processing of wearable IoT assisted medical sensors on pervasive computing system. Comput Commun 151:257–265
Ge C, Yin C, Liu Z, Fang L, Zhu J, Ling H (2020) A privacy preserve big data analysis system for wearable wireless sensor network. Comput Secur:101887
Greco L, Ritrovato P, Xhafa F (2019) An edge-stream computing infrastructure for real-time analysis of wearable sensors data. Fut Generat Comput Syst 93:515–528
Iadanza E, Luschi A (2019) An integrated custom decision-support computer aided facility management informative system for healthcare facilities and analysis. Health Technol:1–11
Ismail WN, Hassan MM, Alsalamah HA, Fortino G (2020) CNN-based health model for regular health factors analysis in internet-of-medical things environment. IEEE Access 8:52541–52549
Kene AP, Choudhury SK (2019) Analytical modeling of tool health monitoring system using multiple sensor data fusion approach in hard machining. Measurement 145:118–129
Kim C, Kim HJ (2019) A study on healthcare supply chain management efficiency: using bootstrap data envelopment analysis. Health Care Manag Sci 22(3):534–548
Kumar MS, Dhulipala VS, Baskar S (2020) Fuzzy unordered rule induction algorithm based classification for reliable communication using wearable computing devices in healthcare. J Ambient Intell Hum Comput:1–12
Li T, Fong S, Wong KK, Wu Y, Yang XS, Li X (2020) Fusing wearable and remote sensing data streams by fast incremental learning with swarm decision table for human activity recognition. Inform Fus 60:41–64
Li X, Huang X, Li C, Yu R, Shu L (2019) EdgeCare: leveraging edge computing for collaborative data management in mobile healthcare systems. IEEE Access 7:22011–22025
Luo L, She X, Cao J, Zhang Y, Li Y, Song PX (2019) Detection and prediction of ovulation from body temperature measured by an in-ear wearable thermometer. IEEE Trans Biomed Eng 67(2):512–522
Mallires KR, Wang D, Tipparaju VV, Tao N (2019) Developing a low-cost wearable personal exposure monitor for studying respiratory diseases using metal–oxide sensors. IEEE Sens J 19(18):8252–8261
Maman ZS, Chen YJ, Baghdadi A, Lombardo S, Cavuoto LA, Megahed FM (2020) A data analytic framework for physical fatigue management using wearable sensors. Expert Syst Appl:113405
Manogaran G, Shakeel PM, Fouad H, Nam Y, Baskar S, Chilamkurti N, Sundarasekar R (2019) Wearable IoT smart-log patch: an edge computing-based Bayesian deep learning network system for multi access physical monitoring system. Sensors 19(13):3030
Pezoulas VC, Kourou KD, Kalatzis F, Exarchos TP, Zampeli E, Gandolfo S, Tzioufas AG (2020) Overcoming the barriers that obscure the interlinking and analysis of clinical data through harmonization and incremental learning. IEEE Open J Eng Med Biol 1:83–90
Puthal D (2018) Lattice-modeled information flow control of big sensing data streams for smart health application. IEEE Internet Things J 6(2):1312–1320
Rizwan A, Zoha A, Zhang R, Ahmad W, Arshad K, Ali NA, Abbasi QH (2018) A review on the role of nano-communication in future healthcare systems: A big data analytics perspective. IEEE Access 6:41903–41920
Shafqat S, Kishwer S, Rasool RU, Qadir J, Amjad T, Ahmad HF (2020) Big data analytics enhanced healthcare systems: a review. J Supercomput 76(3):1754–1799
Sridhar KP, Baskar S, Shakeel PM, Dhulipala VS (2019) Developing brain abnormality recognize system using multi-objective pattern producing neural network. J Ambient Intell Hum Comput 10(8):3287–3295
Todde M, Beltrame M, Marceglia S, Spagno C (2020) Methodology and workflow to perform the data protection impact assessment in healthcare information systems. Inform Med Unlock:100361
Yacchirema DC, Sarabia-Jácome D, Palau CE, Esteve M (2018) A smart system for sleep monitoring by integrating IoT with big data analytics. IEEE Access 6:35988–36001
Zare H, Tavana M, Mardani A, Masoudian S, Saraji MK (2019) A hybrid data envelopment analysis and game theory model for performance measurement in healthcare. Health Care Manag Sci 22(3):475–488
Acknowledgements
This work is funded by the Researchers Supporting Project No. (RSP-2020/102) King Saud University, Riyadh, Saudi Arabia.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Alfarraj, O., Tolba, A. Unsynchronized wearable sensor data analytics model for improving the performance of smart healthcare systems. J Ambient Intell Human Comput 12, 3411–3422 (2021). https://doi.org/10.1007/s12652-020-02576-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-02576-w