{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T10:26:30Z","timestamp":1756635990768,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"print","value":"9781643684000"},{"type":"electronic","value":"9781643684017"}],"license":[{"start":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:00Z","timestamp":1687996800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,29]]},"abstract":"<jats:p>In this study, we analyzed the utility of electromyogram (EMG) signals recorded from the zygomaticus major (zEMG), the trapezius (tEMG), and the corrugator supercilii (cEMG) for emotion detection. We computed eleven-time domain features from the EMG signals to classify the emotions such as amusing, boring, relaxing, and scary. The features were fed to the logistic regression, support vector machine, and multilayer perceptron classifiers, and model performance was evaluated. We achieved an average 10-fold cross-validation classification accuracy of 67.29%. 67.92% and 64.58% by LR using the features extracted from the EMG signals recorded from the zEMG, tEMG, and cEMG, respectively. The classification accuracy improved to 70.6% while combining features from the zEMG and cEMG for the LR model. However, the performance dropped while including the features of EMG from all three locations. Our study shows the importance of utilizing the zEMG and cEMG combination for emotion recognition.<\/jats:p>","DOI":"10.3233\/shti230429","type":"book-chapter","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T07:52:03Z","timestamp":1688111523000},"source":"Crossref","is-referenced-by-count":1,"title":["Identifying the Optimal Location of Facial EMG for Emotion Detection Using Logistic Regression"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4175-2943","authenticated-orcid":false,"given":"Vinay Kumar","family":"Barigala","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India"}]},{"family":"Sriram Kumar P","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India"}]},{"given":"Praveen Kumar","family":"Govarthan","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India"}]},{"given":"Swarubini","family":"PJ","sequence":"additional","affiliation":[{"name":"Department of Sensor and Biomedical Technology, VIT University, Vellore campus, Vellore, India"}]},{"given":"Mythili","family":"Aasaithambi","sequence":"additional","affiliation":[{"name":"Department of Sensor and Biomedical Technology, VIT University, Vellore campus, Vellore, India"}]},{"given":"Nagarajan","family":"Ganapathy","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Indian Institute of Technology Hyderabad, Kandi, Telangana, India"}]},{"given":"Karthik","family":"Pa","sequence":"additional","affiliation":[{"name":"Department of Instrumentation Engineering, National Institute of Technology, Tiruchirappalli, India"}]},{"given":"Deepesh","family":"Kumar","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India"}]},{"given":"Jac Fredo","family":"Agastinose Ronickom","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Healthcare Transformation with Informatics and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI230429","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T07:52:03Z","timestamp":1688111523000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI230429"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,29]]},"ISBN":["9781643684000","9781643684017"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti230429","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2023,6,29]]}}}