MuscleNET: Smart Predictive Analysis for Muscular Activity Using Wearable Sensors

dc.authorscopusid57983665800
dc.authorscopusid57983199400
dc.authorscopusid57982723100
dc.authorscopusid57982723200
dc.authorscopusid57983665900
dc.authorscopusid7801329641
dc.contributor.authorGemici, M.
dc.contributor.authorKorkmaz, K.
dc.contributor.authorAyhan, N.T.
dc.contributor.authorSoylu, S.
dc.contributor.authorGuc, F.
dc.contributor.authorOgrenci, A.S.
dc.date.accessioned2023-10-19T15:05:33Z
dc.date.available2023-10-19T15:05:33Z
dc.date.issued2022
dc.department-tempGemici, M., Kadir Has University, Dept. of Electrical-Electronics Eng., Istanbul, Turkey; Korkmaz, K., Kadir Has University, Dept. of Computer Eng., Istanbul, Turkey; Ayhan, N.T., Kadir Has University, Dept. of Electrical-Electronics Eng., Istanbul, Turkey; Soylu, S., Kadir Has University, Dept. of Electrical-Electronics Eng., Istanbul, Turkey; Guc, F., Kadir Has University, Dept. of Computer Eng., Istanbul, Turkey; Ogrenci, A.S., Kadir Has University, Dept. of Electrical-Electronics Eng., Istanbul, Turkeyen_US
dc.description2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022 --7 September 2022 through 9 September 2022 -- --183936en_US
dc.description.abstractDoing weightlifting training at home has become more popular during the pandemic. Unfortunately, exercising without professional help can lead to dangerous injuries such as muscle tearing. It is possible to create a smart system with machine learning to overcome muscle injuries and suggest an appropriate training program. The use of suitable algorithms enables us to develop programs that can perform predictions based on sEMG (Surface Electromyography) signals. In this study, sEMG signals are collected from the skin surface and features are extracted to be used in deep learning networks. A wearable hardware collects sEMG signals and transfers them to our mobile application via Bluetooth. The mobile application transfers data to the cloud to make predictions based on sEMG signals. We developed MuscleNET for training monitoring, injury prediction/detection, and training quality prediction. Initial measurements indicate that MuscleNET can be used effectively for training quality prediction and real time training monitoring. © 2022 IEEE.en_US
dc.identifier.citation0
dc.identifier.doi10.1109/ASYU56188.2022.9925553en_US
dc.identifier.isbn9781665488945
dc.identifier.scopus2-s2.0-85142727242en_US
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/ASYU56188.2022.9925553
dc.identifier.urihttps://hdl.handle.net/20.500.12469/4946
dc.identifier.wosqualityN/A
dc.khas20231019-Scopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectdeep learningen_US
dc.subjectfeature extractionen_US
dc.subjectmachine learningen_US
dc.subjectmobile applicationen_US
dc.subjectmuscle activityen_US
dc.subjectsignal processingen_US
dc.subjecttraining supporten_US
dc.subjectDeep learningen_US
dc.subjectForecastingen_US
dc.subjectLearning systemsen_US
dc.subjectMobile computingen_US
dc.subjectWearable sensorsen_US
dc.subjectDeep learningen_US
dc.subjectFeatures extractionen_US
dc.subjectMachine-learningen_US
dc.subjectMobile applicationsen_US
dc.subjectMuscle activitiesen_US
dc.subjectPrediction-baseden_US
dc.subjectQuality predictionen_US
dc.subjectSignal-processingen_US
dc.subjectSurface electromyography signalsen_US
dc.subjectTraining supporten_US
dc.subjectMuscleen_US
dc.titleMuscleNET: Smart Predictive Analysis for Muscular Activity Using Wearable Sensorsen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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