Sensor Type, Axis, and Position-Based Fusion and Feature Selection for Multimodal Human Daily Activity Recognition in Wearable Body Sensor Networks
المؤلفون المشاركون
Badawi, Abeer A.
Al-Kabbany, Ahmad
Shaban, Heba A.
المصدر
Journal of Healthcare Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-06-08
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
This research addresses the challenge of recognizing human daily activities using surface electromyography (sEMG) and wearable inertial sensors.
Effective and efficient recognition in this context has emerged as a cornerstone in robust remote health monitoring systems, among other applications.
We propose a novel pipeline that can attain state-of-the-art recognition accuracies on a recent-and-standard dataset—the Human Gait Database (HuGaDB).
Using wearable gyroscopes, accelerometers, and electromyography sensors placed on the thigh, shin, and foot, we developed an approach that jointly performs sensor fusion and feature selection.
Being done jointly, the proposed pipeline empowers the learned model to benefit from the interaction of features that might have been dropped otherwise.
Using statistical and time-based features from heterogeneous signals of the aforementioned sensor types, our approach attains a mean accuracy of 99.8%, which is the highest accuracy on HuGaDB in the literature.
This research underlines the potential of incorporating EMG signals especially when fusion and selection are done simultaneously.
Meanwhile, it is valid even with simple off-the-shelf feature selection methods such the Sequential Feature Selection family of algorithms.
Moreover, through extensive simulations, we show that the left thigh is a key placement for attaining high accuracies.
With one inertial sensor on that single placement alone, we were able to achieve a mean accuracy of 98.4%.
The presented in-depth comparative analysis shows the influence that every sensor type, position, and placement can have on the attained recognition accuracies—a tool that can facilitate the development of robust systems, customized to specific scenarios and real-life applications.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Badawi, Abeer A.& Al-Kabbany, Ahmad& Shaban, Heba A.. 2020. Sensor Type, Axis, and Position-Based Fusion and Feature Selection for Multimodal Human Daily Activity Recognition in Wearable Body Sensor Networks. Journal of Healthcare Engineering،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1186385
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Badawi, Abeer A.…[et al.]. Sensor Type, Axis, and Position-Based Fusion and Feature Selection for Multimodal Human Daily Activity Recognition in Wearable Body Sensor Networks. Journal of Healthcare Engineering No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1186385
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Badawi, Abeer A.& Al-Kabbany, Ahmad& Shaban, Heba A.. Sensor Type, Axis, and Position-Based Fusion and Feature Selection for Multimodal Human Daily Activity Recognition in Wearable Body Sensor Networks. Journal of Healthcare Engineering. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1186385
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1186385
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