Abstract: A system is presented for detecting gestures. The system is comprised of: a contact microphone and a signal processor. The contact microphone is attached to a surface of interest of an objection and is configured to measure surface acoustic waves traveling along the surface of interest. The signal processor is interfaced with the contact microphone and is configured to receive a signal from the contact microphone, where the signal is indicative of surface acoustic waves traveling along the surface of interest. The signal processor operates to identify a gesture made by a person on the surface of interest using the signal.

Abstract: Cameras provide an easy-to-deploy and information-rich datastream for a wide range of ubiquitous sensing and health monitoring applications. However, their unrestricted operation often captures personally identifiable information (PII), preventing their use in privacy-sensitive settings, such as the home, workplace, and hospitals. This disclosure proposes pairing RGB and thermal imaging to robustly detect and remove PII (e.g., an individual's face, skin color, gender, body shape, etc.,) from images before they are stored or sent off the device. A dual camera prototype includes an onboard embedded GPU capable of performing real-time privacy sanitization tasks at 8FPS at under 5 W power consumption. Results show that in the most fail safe settings the system completely removes all PII.

Abstract: Sound presents an invaluable signal source that enables computing systems to perform daily activity recognition. However, microphones are optimized for human speech and hearing ranges: capturing private content, such as speech, while omitting useful, inaudible information that can aid in acoustic recognition tasks. This disclosure presents an activity recognition system that recognizes activities using sounds with frequencies inaudible to humans for preserving privacy. Real-world activity recognition performance of the system is comparable to simulated results, with over 95% classification accuracy across all environments, suggesting immediate viability in performing privacy-preserving daily activity recognition.