Privacy-Enabled Smart Home Framework with Voice Assistant

Deepika Singh (1, 2) , Ismini Psychoula (3) , Erinc Merdivan (4, 1) , Johannes Kropf (1) , Sten Hanke , Emanuel Sandner (1) , Liming Chen , Andreas Holzinger (2)

1 AIT - Austrian Institute of Technology [Vienna]
2 Medical University Graz
3 DMU - De Montfort University [Leicester, United Kingdom]
4 ABC - Machine Learning and Computational Biology

Smart home environment plays a prominent role in improving the quality of life of the residents by enabling home automation, health care and safety through various Internet of Things (IoT) devices. However, a large amount of data generated by sensors in a smart home environment heighten security and privacy concerns among potential users. Some of the data can be sensitive as it contains information about users’ private activities, location, behavioural patterns and health status. Other concerns of the users are towards the distribution and sharing of data to third parties. In this chapter, we propose privacy-enabled smart home framework consisting of three major components: activity recognition and occupancy detection, privacy-preserving data management and voice assistant. The proposed platform includes unobtrusive sensors for multiple occupancy detection and activity recognition. The privacy-enabled voice assistant performs interaction with smart home. We also present a detailed description of system architecture with service middleware.

Mots clés: Smart home; Activity recognition; Dialogue manager; Privacy-preserving data management; Occupancy detection

Domaines: Machine Learning [stat.ML]

Deepika Singh, Ismini Psychoula, Erinc Merdivan, Johannes Kropf, Sten Hanke, et al.. Privacy-Enabled Smart Home Framework with Voice Assistant. Smart Assisted Living, pp.321-339, 2020, 978-3-030-25589-3. ⟨10.1007/978-3-030-25590-9_16⟩. ⟨hal-03081721⟩