Musclenet: Smart Predictive Analysis for Muscular Activity Using Wearable Sensors

dc.authorscopusid 57983665800
dc.authorscopusid 57983199400
dc.authorscopusid 57982723100
dc.authorscopusid 57982723200
dc.authorscopusid 57983665900
dc.authorscopusid 7801329641
dc.contributor.author Gemici, M.
dc.contributor.author Korkmaz, K.
dc.contributor.author Ayhan, N.T.
dc.contributor.author Soylu, S.
dc.contributor.author Guc, F.
dc.contributor.author Ogrenci, A.S.
dc.date.accessioned 2023-10-19T15:05:33Z
dc.date.available 2023-10-19T15:05:33Z
dc.date.issued 2022
dc.department-temp Gemici, 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, Turkey en_US
dc.description 2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022 --7 September 2022 through 9 September 2022 -- --183936 en_US
dc.description.abstract Doing 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.citationcount 0
dc.identifier.doi 10.1109/ASYU56188.2022.9925553 en_US
dc.identifier.isbn 9781665488945
dc.identifier.scopus 2-s2.0-85142727242 en_US
dc.identifier.uri https://doi.org/10.1109/ASYU56188.2022.9925553
dc.identifier.uri https://hdl.handle.net/20.500.12469/4946
dc.khas 20231019-Scopus en_US
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.ispartof Proceedings - 2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022 en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 0
dc.subject deep learning en_US
dc.subject feature extraction en_US
dc.subject machine learning en_US
dc.subject mobile application en_US
dc.subject muscle activity en_US
dc.subject signal processing en_US
dc.subject training support en_US
dc.subject Deep learning en_US
dc.subject Forecasting en_US
dc.subject Learning systems en_US
dc.subject Mobile computing en_US
dc.subject Wearable sensors en_US
dc.subject Deep learning en_US
dc.subject Features extraction en_US
dc.subject Machine-learning en_US
dc.subject Mobile applications en_US
dc.subject Muscle activities en_US
dc.subject Prediction-based en_US
dc.subject Quality prediction en_US
dc.subject Signal-processing en_US
dc.subject Surface electromyography signals en_US
dc.subject Training support en_US
dc.subject Muscle en_US
dc.title Musclenet: Smart Predictive Analysis for Muscular Activity Using Wearable Sensors en_US
dc.type Conference Object en_US
dspace.entity.type Publication

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