Wearable Sensor-Based Human Activity Recognition Using Hybrid Deep Learning Techniques
المؤلفون المشاركون
Wang, Kan
Cao, Ting
Tian, Ling
An, Yang
Wang, Huaijun
Li, Junhuai
Tu, Pengjia
Li, Shancang
Zhao, Jing
المصدر
Security and Communication Networks
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-07-27
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
Human activity recognition (HAR) can be exploited to great benefits in many applications, including elder care, health care, rehabilitation, entertainment, and monitoring.
Many existing techniques, such as deep learning, have been developed for specific activity recognition, but little for the recognition of the transitions between activities.
This work proposes a deep learning based scheme that can recognize both specific activities and the transitions between two different activities of short duration and low frequency for health care applications.
In this work, we first build a deep convolutional neural network (CNN) for extracting features from the data collected by sensors.
Then, the long short-term memory (LTSM) network is used to capture long-term dependencies between two actions to further improve the HAR identification rate.
By combing CNN and LSTM, a wearable sensor based model is proposed that can accurately recognize activities and their transitions.
The experimental results show that the proposed approach can help improve the recognition rate up to 95.87% and the recognition rate for transitions higher than 80%, which are better than those of most existing similar models over the open HAPT dataset.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Huaijun& Zhao, Jing& Li, Junhuai& Tian, Ling& Tu, Pengjia& Cao, Ting…[et al.]. 2020. Wearable Sensor-Based Human Activity Recognition Using Hybrid Deep Learning Techniques. Security and Communication Networks،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1208366
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Huaijun…[et al.]. Wearable Sensor-Based Human Activity Recognition Using Hybrid Deep Learning Techniques. Security and Communication Networks No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1208366
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Huaijun& Zhao, Jing& Li, Junhuai& Tian, Ling& Tu, Pengjia& Cao, Ting…[et al.]. Wearable Sensor-Based Human Activity Recognition Using Hybrid Deep Learning Techniques. Security and Communication Networks. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1208366
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1208366
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر