Abstract
Healthcare in the current day must be sophisticated and interactive. Several issues need to be addressed when creating a complete healthcare environment, including accurate diagnoses, inexpensive modeling, simple design, reliable data transfer, and enough storage capacity. This research article proposes an efficient approach focusing on speech recognition using soft margin formulation and kernel trick to provide a simple and easily accessible monitoring system to elderly and impaired people. The objective is to develop a low-cost speech recognition system that enables easy access to the deployed Internet of Things devices in the smart assisted living facilities (smart hospital or home) through distributive supervisory mode. The suggested work utilized a Banana Pi M3 and a BPI-AI-Voice (Microsemi) module to enable remote connectivity of home appliances through mobile phones. This framework is developed to help elderly/disabled people communicate with home appliances using voice commands. The suggested platform intervention strengthens the speech recognition procedure by including advanced Support Vector Machines principles such as soft margin formulation and kernel trick with a Globally Optimal Reparameterization Algorithm. The suggested approach is indeed a Machine learning—based technology that uses voice commands to manage smart medical devices with 96.09% accuracy. The nominated speech recognition technology is flexible enough to use with scalability and ensures the privacy of the deployed devices. Thus, the proposed work helps to integrate our scheme of medical facilities to assist the senior citizens and others who are sick (physically disabled) in a most effective way.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Basheer S, Anbarasi M, Sakshi DG, Vinoth Kumar V (2020) Efficient text summarization method for blind people using text mining techniques. Int J Speech Technol 23(4):713–725. https://doi.org/10.1007/s10772-020-09712-z
Choi D, Choi H, Shon D (2019) Future changes to smart home based on AAL healthcare service. J Asian Archit Build Eng 18(3):190–199. https://doi.org/10.1080/13467581.2019.1617718
De Brouwer M, Ongenae F, Bonte P, De Turck F (2018) Towards a cascading reasoning framework to support responsive ambient-intelligent healthcare interventions. Sensors 18(10):3514
Dhiman G, Vinoth Kumar V, Kaur A, Sharma A (2021) DON: deep learning and optimization-based framework for detection of novel coronavirus disease using X-ray images. Interdiscip Sci Comput Life Sci 13(2):260–272. https://doi.org/10.1007/s12539-021-00418-7
Ding I-J, Yen C-T, Ou D-C (2014) A method to integrate GMM, SVM and DTW for speaker recognition. Int J Eng Technol Innov 4(1):38–47
Essa EM, Tolba AS, Elmougy S (2008) A comparison of combined classifier architectures for Arabic Speech Recognition. In: 2008 International Conference on Computer Engineering & Systems. https://doi.org/10.1109/icces.2008.4772985
Fleury A, Vacher M, Portet F, Chahuara P, Noury N (2012) A french corpus of audio and multimodal interactions in a health smart home. J Multimodal User Interfaces 7(1–2):93–109. https://doi.org/10.1007/s12193-012-0104-x
Gomez C, Chessa S, Fleury A, Roussos G, Preuveneers D (2019) Internet of Things for enabling smart environments: a technology-centric perspective. J Ambient Intell Smart Environ 11(1):23–43. https://doi.org/10.3233/ais-180509
Gonzalez R, Muñoz J, Salazar J, Duque N (2016) Voice recognition system to support learning platforms oriented to people with visual disabilities. Lect Notes Comput Sci. https://doi.org/10.1007/978-3-319-40238-3_7
Helal S, Bull CN (2019) From smart homes to smart-ready homes and communities. Dement Geriatr Cogn Disord 47(3):157–163. https://doi.org/10.1159/000497803
Irfan M, Mutaqin IZ, Utomo RG (2016) Implementation of dynamic time warping algorithm on an android based application to write and pronounce Hijaiyah letters. In: 2016 4th International Conference on Cyber and IT Service Management. https://doi.org/10.1109/citsm.2016.7577529
Kong X, Choi JY, Shattuck-Hufnagel S (2017) Evaluating automatic speech recognition systems in comparison with human perception results using distinctive feature measures. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp.2017.7953270
Koskela T, Mattila K (2004) Evolution towards smart home environments: empirical evaluation of three user interfaces. Person Ubiquitous Comput. https://doi.org/10.1007/s00779-004-0283-x
Kouser RR, Manikandan T, Kumar VV (2018) Heart disease prediction system using artificial neural network, radial basis function and case based reasoning. J Comput Theor Nanosci 15(9):2810–2817. https://doi.org/10.1166/jctn.2018.7543
Kumar VV, Raghunath KMK, Rajesh N, Venkatesan M, Joseph RB, Thillaiarasu N (2021) Paddy plant disease recognition, risk analysis, and classification using deep convolution neuro-fuzzy network. J Mobile Multimed. https://doi.org/10.13052/jmm1550-4646.1829
Lecouteux B, Vacher M, Portet F (2018) Distant speech processing for smart home: comparison of ASR approaches in scattered microphone network for voice command. Int J Speech Technol 21(3):601–618. https://doi.org/10.1007/s10772-018-9520-y
Liao L, Noble WS (2003) Combining pairwise sequence similarity and support vector machines for detecting remote protein evolutionary and structural relationships. J Comput Biol 10(6):857–868. https://doi.org/10.1089/106652703322756113
Lytras M, Visvizi A (2018) Who uses smart city services and what to make of it: toward interdisciplinary smart cities research. Sustainability 10(6):1998. https://doi.org/10.3390/su10061998
Mahesh TR, Dhilip Kumar V, Vinoth Kumar V, Asghar J, Geman O, Arulkumaran G, Arun N (2022a) AdaBoost ensemble methods using K-fold cross validation for survivability with the early detection of heart disease. Comput Intell Neurosci 2022:1–11. https://doi.org/10.1155/2022/9005278
Mahesh TR, Vivek V, Kumar VV, Natarajan R, Sathya S, Kanimozhi S (2022b) A comparative performance analysis of machine learning approaches for the early prediction of diabetes disease. In: 2022b International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI). https://doi.org/10.1109/accai53970.2022b.9752543
Mittal Y, Toshniwal P, Sharma S, Singhal D, Gupta R, Mittal VK (2015) A voice-controlled multi-functional Smart Home Automation System. In: 2015 Annual IEEE India Conference (INDICON). https://doi.org/10.1109/indicon.2015.7443538
Park G, Kim H (2018) Low-cost implementation of a named entity recognition system for voice-activated human-appliance interfaces in a smart home. Sustainability 10(2):488. https://doi.org/10.3390/su10020488
Peetoom KKB, Lexis MAS, Joore M, Dirksen CD, De Witte LP (2014) Literature review on monitoring technologies and their outcomes in independently living elderly people. Disabil Rehabil Assist Technol 10(4):271–294. https://doi.org/10.3109/17483107.2014.961179
Raghunath KMK, Rengarajan N (2018) Evolving optimal response time and synchronized communication on integrating fuzzy logic using infrared sensor and sound detecting sensor in WSN. Sens Lett 16(8):606–613. https://doi.org/10.1166/sl.2018.3993
Rahman MM, Roy Dipta D, Hasan MM (2018) Dynamic Time Warping Assisted SVM Classifier for Bangla Speech Recognition. 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2). https://doi.org/10.1109/ic4me2.2018.8465640
Rashidi P, Cook DJ (2009) Keeping the resident in the loop: adapting the smart home to the user. IEEE Trans Syst Man Cybern Part A Syst Hum 39(5):949–959. https://doi.org/10.1109/TSMCA.2009.2025137
Zhu M, Wu X, Lu Z, Wang T, Zhu X (2019) Long-term speech information based threshold for voice activity detection in massive microphone network. Digit Signal Process 94:156–164. https://doi.org/10.1016/j.dsp.2019.05.012
Funding
No funding has been provided for the completion of this research paper.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors doesn't have any conflict of interest.
Human or animals rights
The authors assured there is no animals and humans involved in this research.
Informed consent
No consent.
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
Alshudukhi, J.S. Smart and interactive healthcare system based on speech recognition using soft margin formulation and kernel trick. Int J Syst Assur Eng Manag 15, 324–333 (2024). https://doi.org/10.1007/s13198-022-01728-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-022-01728-9