Giyilebilir Sensörler ile Insan Hareketlerini Izleme

Mucahit Altintas, Nilay Tufek, Murat Yalcin, Yi Li, Senem Kursun Bahadir

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

Özet

In recent years, the use of wearable devices in many areas has emerged. Recognition of human behavior and movements has become almost the essential component of wearable devices. Monitoring human behavior enhances human life in many fields, especially in the health sector. Wearable sensors are preferred for motion tracking because they can work independently of the location, cause less discomfort to users in terms of privacy than other sensing devices, and are inexpensive. In this study, using data from wearable sensors, human behavior has been predicted with deep learning methods. The contributions of the spectrogram, wavelet transform and time-based feature spaces to the prediction performance are analyzed. The prediction performance of our developed model is comparable to the state-of-art studies in the literature.

Tercüme edilen katkı başlığıGiyilebilir Sensörler ile Insan Hareketlerini Izleme Human Activity Monitoring via Wearable Sensors
Orijinal dilTürkçe
Ana bilgisayar yayını başlığı2022 30th Signal Processing and Communications Applications Conference, SIU 2022
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9781665450928
DOI'lar
Yayın durumuYayınlandı - 2022
Etkinlik30th Signal Processing and Communications Applications Conference, SIU 2022 - Safranbolu, Turkey
Süre: 15 May 202218 May 2022

Yayın serisi

Adı2022 30th Signal Processing and Communications Applications Conference, SIU 2022

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???event.eventtypes.event.conference???30th Signal Processing and Communications Applications Conference, SIU 2022
Ülke/BölgeTurkey
ŞehirSafranbolu
Periyot15/05/2218/05/22

Bibliyografik not

Publisher Copyright:
© 2022 IEEE.

Keywords

  • deep learning
  • human activity recognition
  • spectrogram
  • wavelet transform
  • wearable sensors

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