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An IoT-based context-aware model for danger situations detection

Tundis, Andrea ; Uzair, Muhammad ; Mühlhäuser, Max (2021):
An IoT-based context-aware model for danger situations detection.
In: Computers & Electrical Engineering, 96 (Part B), Elsevier, ISSN 0045-7906,
DOI: 10.1016/j.compeleceng.2021.107571,
[Article]

Abstract

On a daily basis, people perform planned or routine activities related to their needs, such as going to the office, playing sports and so on. Alongside them, unpleasant unforeseen situations can take place such as being robbed on the street or even being taken hostage. Providing information related to the crime scene or requesting help from the competent authorities is difficult. That is why, mechanisms to support users in such situations, based on their physical status, would be of great importance. Based on such idea, a context-aware model for detecting specific situations of danger is proposed. It is characterized by a set of defined features related to the body posture, the stress level and geolocation whose values are gathered through a smartphone and a smartwatch, as enabling technologies for the local computation. A machine learning technique was adopted for supporting body posture recognition, whereas a threshold-based approach was used to detect the stress level and to evaluate of user�s location. After the description of the proposed model, the achieved results as well as current limits are also discussed.

Item Type: Article
Erschienen: 2021
Creators: Tundis, Andrea ; Uzair, Muhammad ; Mühlhäuser, Max
Title: An IoT-based context-aware model for danger situations detection
Language: English
Abstract:

On a daily basis, people perform planned or routine activities related to their needs, such as going to the office, playing sports and so on. Alongside them, unpleasant unforeseen situations can take place such as being robbed on the street or even being taken hostage. Providing information related to the crime scene or requesting help from the competent authorities is difficult. That is why, mechanisms to support users in such situations, based on their physical status, would be of great importance. Based on such idea, a context-aware model for detecting specific situations of danger is proposed. It is characterized by a set of defined features related to the body posture, the stress level and geolocation whose values are gathered through a smartphone and a smartwatch, as enabling technologies for the local computation. A machine learning technique was adopted for supporting body posture recognition, whereas a threshold-based approach was used to detect the stress level and to evaluate of user�s location. After the description of the proposed model, the achieved results as well as current limits are also discussed.

Journal or Publication Title: Computers & Electrical Engineering
Journal volume: 96
Number: Part B
Publisher: Elsevier
Uncontrolled Keywords: Internet of Things, Feature engineering, Machine learning, Criminal event identification, Human safety
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Telecooperation
LOEWE
LOEWE > LOEWE-Zentren
LOEWE > LOEWE-Zentren > emergenCITY
Date Deposited: 21 Dec 2021 12:14
DOI: 10.1016/j.compeleceng.2021.107571
Official URL: https://www.sciencedirect.com/science/article/pii/S004579062...
Additional Information:

Art.No.: 107571

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