Bindi: Affective Internet of Things to Combat Gender-Based Violence
| Autor: | Jose A. Miranda Calero, Esther Rituerto-Gonzalez, Clara Luis-Mingueza, Manuel F. Canabal, Alberto Ramirez Barcenas, Jose M. Lanza-Gutierrez, Carmen Pelaez-Moreno, Celia Lopez-Ongil |
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| Přispěvatelé: | Comunidad de Madrid, Ministerio de Ciencia e Innovación (España), Universidad Carlos III de Madrid |
| Rok vydání: | 2022 |
| Předmět: |
Informática
Telecomunicaciones Computer Networks and Communications Edge computing Computer Science Applications Fear recognition Smart sensors Hardware and Architecture Signal Processing Multimodal data fusion Microelectromechanical systems Electrónica Artificial intelligence of things Information Systems |
| Zdroj: | IEEE Internet of Things Journal. 9:21174-21193 |
| ISSN: | 2372-2541 |
| DOI: | 10.1109/jiot.2022.3177256 |
| Popis: | The main research motivation of this article is the fight against gender-based violence and achieving gender equality from a technological perspective. The solution proposed in this work goes beyond currently existing panic buttons, needing to be manually operated by the victims under difficult circumstances. Instead, Bindi, our end-to-end autonomous multimodal system, relies on artificial intelligence methods to automatically identify violent situations, based on detecting fear-related emotions, and trigger a protection protocol, if necessary. To this end, Bindi integrates modern state-of-the-art technologies, such as the Internet of Bodies, affective computing, and cyber-physical systems, leveraging: 1) affective Internet of Things (IoT) with auditory and physiological commercial off-the-shelf smart sensors embedded in wearable devices; 2) hierarchical multisensorial information fusion; and 3) the edge-fog-cloud IoT architecture. This solution is evaluated using our own data set named WEMAC, a very recently collected and freely available collection of data comprising the auditory and physiological responses of 47 women to several emotions elicited by using a virtual reality environment. On this basis, this work provides an analysis of multimodal late fusion strategies to combine the physiological and speech data processing pipelines to identify the best intelligence engine strategy for Bindi. In particular, the best data fusion strategy reports an overall fear classification accuracy of 63.61% for a subject-independent approach. Both a power consumption study and an audio data processing pipeline to detect violent acoustic events complement this analysis. This research is intended as an initial multimodal baseline that facilitates further work with real-life elicited fear in women. This work was supported in part by the Department of Research and Innovation of Madrid Regional Authority, in the EMPATIA-CM Research Project (Reference Y2018/TCS-5046) funded by MCIN/AEI/10.13039/501100011033 under Grant PDC2021-121071-I00; in part by the European Union NextGenerationEU/PRTR in part by the Spanish Ministry of Universities with the FPU under Grant FPU19/00448; and in part by the Madrid Government (Comunidad de Madrid-Spain) through the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M26), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation). |
| Databáze: | OpenAIRE |
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