VAX: Using Existing Video and Audio-based Activity Recognition Models to Bootstrap Privacy-Sensitive Sensors
Prasoon Patidar, Mayank Goel, Yuvraj Agarwal
The use of audio and video modalities for Human Activity Recognition (HAR) is common, given the richness of the data and the availability of pre-trained ML models using a large corpus of labeled training data. However, audio and video sensors also lead to significant consumer privacy concerns. A key limitation of prior approaches is that most of them do not readily generalize across environments and require significant in-situ training data. In this paper, we generalize this concept to create a novel system called VAX (Video/Audio to ‘X’), where training labels acquired from existing Video/Audio ML models are used to train ML models for a wide range of ‘X’ privacy-sensitive sensors. Once the ML models for the privacy-sensitive sensors are trained, with little to no user involvement, the Audio/Video sensors can be removed altogether to protect the user’s privacy better. We built and deployed VAX in ten participants’ homes while they performed 17 common activities of daily living.