Wearable Sensor-Based Location-Specific Occupancy Detection in Smart Environments
المؤلفون المشاركون
Khan, Md Abdullah Al Hafiz
Roy, Nirmalya
Hossain, H. M. Sajjad
المصدر
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-21، 21ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-04-01
دولة النشر
مصر
عدد الصفحات
21
التخصصات الرئيسية
الملخص EN
Occupancy detection helps enable various emerging smart environment applications ranging from opportunistic HVAC (heating, ventilation, and air-conditioning) control, effective meeting management, healthy social gathering, and public event planning and organization.
Ubiquitous availability of smartphones and wearable sensors with the users for almost 24 hours helps revitalize a multitude of novel applications.
The inbuilt microphone sensor in smartphones plays as an inevitable enabler to help detect the number of people conversing with each other in an event or gathering.
A large number of other sensors such as accelerometer and gyroscope help count the number of people based on other signals such as locomotive motion.
In this work, we propose multimodal data fusion and deep learning approach relying on the smartphone’s microphone and accelerometer sensors to estimate occupancy.
We first demonstrate a novel speaker estimation algorithm for people counting and extend the proposed model using deep nets for handling large-scale fluid scenarios with unlabeled acoustic signals.
We augment our occupancy detection model with a magnetometer-dependent fingerprinting-based localization scheme to assimilate the volume of location-specific gathering.
We also propose crowdsourcing techniques to annotate the semantic location of the occupant.
We evaluate our approach in different contexts: conversational, silence, and mixed scenarios in the presence of 10 people.
Our experimental results on real-life data traces in natural settings show that our cross-modal approach can achieve approximately 0.53 error count distance for occupancy detection accuracy on average.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Khan, Md Abdullah Al Hafiz& Roy, Nirmalya& Hossain, H. M. Sajjad. 2018. Wearable Sensor-Based Location-Specific Occupancy Detection in Smart Environments. Mobile Information Systems،Vol. 2018, no. 2018, pp.1-21.
https://search.emarefa.net/detail/BIM-1204805
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Khan, Md Abdullah Al Hafiz…[et al.]. Wearable Sensor-Based Location-Specific Occupancy Detection in Smart Environments. Mobile Information Systems No. 2018 (2018), pp.1-21.
https://search.emarefa.net/detail/BIM-1204805
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Khan, Md Abdullah Al Hafiz& Roy, Nirmalya& Hossain, H. M. Sajjad. Wearable Sensor-Based Location-Specific Occupancy Detection in Smart Environments. Mobile Information Systems. 2018. Vol. 2018, no. 2018, pp.1-21.
https://search.emarefa.net/detail/BIM-1204805
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1204805
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