Publication
WeDoCare - An IoT system to help vulnerable social groups
| Summary: | Unfortunately, violence against women is a topic we regularly hear on the news. Last year, India was considered the most dangerous country for women due to the high risk of sexual violence, sexual slavery and forced marriages. Although we can identify a rise in the number of movements that empower women, such as the "me too" movement - started in 2006 to help victims of sexual violence – the majority of women who went through domestic or sexual violence are still not reporting their situations to law enforcement. There is a large number of personal alarm systems in the market to combat this situation, most of them based on panic buttons. However, none of them has gotten widespread acceptance. In the context of this thesis, we developed an innovative application that is capable of recognizing a dangerous situation and trigger an alert through four different methods: (i) speech recognition, (ii) gesture recognition, (iii) recognition through the use of an IoT node and (iv) manual pressing of a button in the application’s main screen. Furthermore, the application also has informative videos and tips, so the victims know how to act in dangerous situations. |
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| Main Authors: | Saldanha, Ruben Filipe Gonçalves |
| Subject: | Android Java IoT HitL Violência Doméstica Android Java IoT HitL Domestic Violence |
| Year: | 2019 |
| Country: | Portugal |
| Document type: | master thesis |
| Access type: | open access |
| Associated institution: | Universidade de Coimbra |
| Language: | English |
| Origin: | Estudo Geral - Universidade de Coimbra |
| Summary: | Unfortunately, violence against women is a topic we regularly hear on the news. Last year, India was considered the most dangerous country for women due to the high risk of sexual violence, sexual slavery and forced marriages. Although we can identify a rise in the number of movements that empower women, such as the "me too" movement - started in 2006 to help victims of sexual violence – the majority of women who went through domestic or sexual violence are still not reporting their situations to law enforcement. There is a large number of personal alarm systems in the market to combat this situation, most of them based on panic buttons. However, none of them has gotten widespread acceptance. In the context of this thesis, we developed an innovative application that is capable of recognizing a dangerous situation and trigger an alert through four different methods: (i) speech recognition, (ii) gesture recognition, (iii) recognition through the use of an IoT node and (iv) manual pressing of a button in the application’s main screen. Furthermore, the application also has informative videos and tips, so the victims know how to act in dangerous situations. |
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Funded activities
No funded projects found.