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Breath monitoring is important for monitoring illnesses, such as sleep apnea, for people of all ages. One cause of concern for parents is sudden infant death syndrome (SIDS), where an infant suddenly passes away during sleep, usually due to complications in breathing. There are a variety of works and products on the market for monitoring breathing, especially for children and infants. Many of these are wearables that require you to attach an accessory onto the child or person, which can be uncomfortable. Other solutions utilize a camera, which can be privacy-intrusive and function poorly during the night, when lighting is poor. In this work, we introduce BuMA, an audio-based, non-intrusive, and contactless, breathing monitoring system. BuMA utilizes a microphone array, beamforming, and audio filtering to enhance the sounds of breathing by filtering out several common noises in or near home environments, such as construction, speech, and music, that could make detection difficult. We show that BuMA improves breathing detection accuracy by up to 12%, within 30cm from a person, over existing audio filtering algorithms or platforms that do not leverage filtering.
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PAMS: Improving Privacy in Audio-Based Mobile Systems
Smartphones and mobile applications have become an integral part of our daily lives. This is reflected by the increase in mobile devices, applications, and revenue generated each year. However, this growth is being met with an increasing concern for user privacy, and there have been many incidents of privacy and data breaches related to smartphones and mobile applications in recent years. In this work, we focus on improving privacy for audio-based mobile systems. These applications will generally listen to all sounds in the environment and may record privacy-sensitive signals, such as speech, that may not be needed for the application. We present PAMS, a software development package for mobile applications. PAMS integrates a novel sound source filtering algorithm called Probabilistic Template Matching to generate a set of privacy-enhancing filters that remove extraneous sounds using learned statistical "templates" of these sounds. We demonstrate the effectiveness of PAMS by integrating it into a sleep monitoring system, with the intent to remove extraneous speech from breathing, snoring, and other sleep sounds that the system is monitoring. By comparing our PAMS enhanced sleep monitoring system with existing mobile systems, we show that PAMS can reduce speech intelligibility by up to 74.3% while maintaining similar performance in detecting sleeping sounds.
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- PAR ID:
- 10232672
- Date Published:
- Journal Name:
- Proceedings of the 2nd International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things
- Page Range / eLocation ID:
- 41 to 47
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3417313.3429383
