Health-risk behaviors, such as overeating, smoking, consuming alcohol, and not adhering to medication, are responsible for increases in morbidity and mortality. To track and intervene during these health-risk behaviors, clinicians traditionally rely on self-reports. However, self-reports are inaccurate and biased. Therefore, we cannot use self-reports to validate health-risk behaviors in free-living conditions. Thus, an automated technique for validating health-risk behaviors is extremely necessary. With the growth and popularity of wearable devices (e.g., smartwatches), automatic monitoring of physical activity is possible. However, the devices often do not provide any visual confirmation, making it challenging to verify activities performed in free-living conditions. Cameras can capture point-of-view videos and can thus be used as a wearable device to capture videos for visual confirmation of activities, including health-risk behaviors. Such recordings can help us better understand health-risk behaviors. Additionally, video information can be automatically processed to confirm and validate health-risk behaviors. Recording videos of sensitive content and bystanders is associated with privacy and ethical concerns. Currently there is no privacy-preserving camera that can automatically detect health-risk behaviors, and most people are unwilling to wear cameras without raising privacy concerns. In addition to privacy concerns, people prefer wearables that are unobtrusive and small and that do not require frequent charging. Thus, a privacy-preserving, unobtrusive wearable camera would increase wearability. Infrared (IR) sensor arrays have the potential to provide independent temperature readings, which allows determining whether an object is near or far. The IR sensor array can help record only the wearer and objects near the wearer, while filtering out distant objects. IR sensor arrays have a small power footprint, thus providing longer battery life. Our project aims to develop a privacy-conscious, unobtrusive, wearable, behavior-detection platform that will make it possible to detect and intervene upon health-risk behaviors in real time. In this project, we will (1) develop the wearable behavior-detection device that allows visual confirmation without burdening the wearer. The device will augment RGB camera data with IR sensor array data for privacy-conscious recording and automatic behavior detection. (2) We will test various designs to determine a user's acceptability to wear the device. Then, we will test various image processing techniques and machine learning algorithms to determine the best algorithm for detecting health-risk behaviors. (3) We will incorporate the best-performing behavior-detection algorithm so that it can run on the developed wearable device. With a behavior-detection algorithm running on an acceptable wearable device, the ability to detect health-risk behaviors in real time will become a reality. Ultimately, our wearable device will allow researchers to test and apply appropriate behavioral interventions in real time, rather than relying on self-reports, whenever health-risk behaviors occur.
Several activities involving hand-to-mouth gestures (e.g., overeating, smoking, consuming alcohol, or non- adherence to medication) are associated with health-risk behaviors that are a leading cause of preventable deaths. Being able to automatically monitor health-risk behaviors using wearable video cameras will improve our understanding of these behaviors and will ultimately enable us to design effective methods to intervene when they occur. We will develop BehaviorSight, a privacy-conscious, unobtrusive, wearable, behavior-detection device that will allow future researchers and behavioral scientists to use the device for monitoring numerous everyday health-risk behaviors in free-living settings.
