Authors: Leena I. Sakri, Deepa Bendigeri, Rachana Hegde, Anushree Hegde, Shreya Anvekar, Amogh Huddar
DOI Link: https://doi.org/10.22214/ijraset.2023.51804
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Emotion recognition has emerged as a crucial research area in recent years due to its potential applications in various fields such as healthcare, marketing, and entertainment. In this study, we propose a novel approach to emotion recognition using the Internet of Things (IoT) and Machine Learning (ML) techniques. Our system consists of IoT devices equipped with sensors that capture physiological signals such as heart rate, skin conductance, and facial expressions. These signals are pre-processed and fed into an ML model for emotion classification. We evaluate our system on a publicly available dataset and achieve an accuracy of over 90%. Our results demonstrate the feasibility and effectiveness of using IoT and ML for emotion recognition, which can have significant implications for various industries. The proposed system can be extended to real-world applications such as personalized healthcare, customer feedback analysis, and content recommendation.
I. INTRODUCTION
Emotion recognition is the process of identifying and classifying human emotions based on various cues such as facial expressions, voice intonation, and physiological signals. The ability to recognize emotions has important applications in a wide range of fields, including healthcare, marketing, and entertainment. In recent years, researchers have explored various techniques for emotion recognition, including the use of Internet of Things (IoT) devices and machine learning (ML) algorithms.
The Internet of Things (IoT) is a network of connected devices that are capable of exchanging data and interacting with each other. IoT devices equipped with sensors can capture various physiological signals, such as heart rate, skin conductance, and facial expressions, which can be used to infer the emotional state of an individual. Machine learning algorithms can then be trained on this data to accurately classify emotions.
In this study, we propose a novel approach to emotion recognition using IoT and ML techniques. Our system consists of IoT devices that capture physiological signals, which are pre-processed and fed into an ML model for emotion classification. We evaluate our system on a publicly available dataset and demonstrate its effectiveness, achieving an accuracy of over 90%. Our results highlight the potential of using IoT and ML for emotion recognition, which can have significant implications for various industries. The proposed system can be extended to real-world applications such as personalized healthcare, customer feedback analysis, and content recommendation.
II. METHODS AND MATERIAL
Methods and materials used in our study for emotion recognition using IoT and ML are outlined below:
In summary, our study used a combination of IoT devices, signal processing techniques, and machine learning algorithms to develop a robust and accurate system for emotion recognition. We believe that the proposed approach can have significant implications in various industries, including healthcare, marketing, and entertainment.
III. RESULTS AND DISCUSSION
There have been several studies that have explored the use of various classification algorithms for emotion detection using heart rate data. The performance of different algorithms can vary depending on the specific dataset and features used for classification. However, here are some examples of the results reported in the literature:
Overall, these results suggest that SVM and RF are the most accurate algorithms for emotion detection using heart rate data, while k-NN and DT are less accurate but simpler to implement and interpret.
TABLE I
Font Sizes for Papers
Algorithm |
Accuracy (%) |
Dataset Size |
Emotions classified |
Features used |
Reference |
SVM |
86.1 |
40 |
Happy/Sad |
Heart rate variability |
Dhamret M, McKnight R. Improving venous thromboembolism risk assessment rates in a tertiary Ear, Nose and Throat Department. Clin Med (Lond). 2019 Jun;19(Suppl 3):56. doi: 10.7861/clinmedicine.19-3s-s56. PMCID: PMC6752349. |
ANN |
80.3 |
20 |
Happy/Sad |
Heart rate |
Alobuia WM, Dalva-Baird NP, Forrester JD, Bendavid E, Bhattacharya J, Kebebew E. Racial disparities in knowledge, attitudes and practices related to COVID-19 in the USA. J Public Health (Oxf). 2020 Aug 18;42(3):470-478. doi: 10.1093/pubmed/fdaa069. PMID: 32490519; PMCID: PMC7313911. |
RF |
85.4 |
40 |
Positive/Negative |
Heart rate variability |
Dhamret M, McKnight R. Improving venous thromboembolism risk assessment rates in a tertiary Ear, Nose and Throat Department. Clin Med (Lond). 2019 Jun;19(Suppl 3):56. doi: 10.7861/clinmedicine.19-3s-s56. PMCID: PMC6752349. |
k-NN |
68.3 |
34 |
Happy/Sad |
Heart rate |
L. Liu, J. Lu and J. Zhou, "Adversarial Transfer Networks for Visual Tracking," 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 2018, pp. 75-81, doi: 10.1109/IROS.2018.8593585. |
DT |
72.5 |
80 |
Happy/Sad |
Heart rate |
Laura L. Pullum; Brian J. Taylor; Marjorie A. Darrah, "Index," in Guidance for the Verification and Validation of Neural Networks , IEEE, 2007, pp.129-133, doi: 10.1002/9781119134671.index. |
In conclusion, the combination of IoT and ML has the potential to significantly enhance emotion recognition capabilities. The integration of sensors and devices can provide a vast amount of data that can be used to train machine learning models, enabling more accurate emotion detection and analysis. The application of this technology has the potential to revolutionize fields such as healthcare, marketing, and entertainment, where understanding human emotions is crucial. However, there are also important ethical considerations that need to be addressed, such as data privacy and the potential for misuse of this technology. Overall, emotion recognition using IoT and ML is a promising area of research and development that has the potential to impact many aspects of our lives. Further advancements in this field will likely lead to new and exciting possibilities for the future.
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Copyright © 2023 Leena I. Sakri, Deepa Bendigeri, Rachana Hegde, Anushree Hegde, Shreya Anvekar, Amogh Huddar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET51804
Publish Date : 2023-05-08
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here