Activity recognition using smartphones and wearable devices: Traditional approaches, new solutions
PAMUKKALE UNIVERSITY JOURNAL OF ENGINEERING SCIENCES-PAMUKKALE UNIVERSITESI MUHENDISLIK BILIMLERI DERGISI, cilt.25, sa.2, ss.223-239, 2019 (ESCI, TRDizin)
- Yayın Türü: Makale / Derleme
- Cilt numarası: 25 Sayı: 2
- Basım Tarihi: 2019
- Doi Numarası: 10.5505/pajes.2018.84758
- Dergi Adı: PAMUKKALE UNIVERSITY JOURNAL OF ENGINEERING SCIENCES-PAMUKKALE UNIVERSITESI MUHENDISLIK BILIMLERI DERGISI
- Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), TR DİZİN (ULAKBİM)
- Sayfa Sayıları: ss.223-239
- Anahtar Kelimeler: Activity recognition, Smartphone, Wearable device, Deep learning, Dataset, Survey, MOBILE ACTIVITY RECOGNITION, CLASSIFIERS, SYSTEM
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
In recent years, the research on activity recognition has gained speed especially with the development of smart phones and wearable devices. Activities could be categorized into two main groups. simple activities such as walking, running and complex activities such as eating, sleeping, brushing teeth. In this survey paper, articles about activity recognition are examined thoroughly. Sensors and devices used in activity recognition, types of daily activities, application areas, data collection process, training methods, classification algorithms and resource consumption are mentioned in details. The state of the art is elaborated and the existing methods are compared to each other. Later, open data sets are mentioned and studies offering innovative solutions using latest approaches such as deep learning methods are introduced. Finally, still open issues on this area are presented and future work has been discussed.