Real-Time Drink Trigger Detection in Free-living Conditions Using Inertial Sensors

Abstract

Despite the importance of maintaining an adequate hydration status, water intake is frequently neglected due to the fast pace of people’s lives. For the elderly, poor water intake can be even more concerning, not only due to the damaging impact of dehydration, but also since seniors’ hydration regulation mechanisms tend to be less efficient. This work focuses on the recognition of the pre-drinking hand-to-mouth movement (a drink trigger) with two main objectives: predict the occurrence of drinking events in real-time and free-living conditions, and assess the potential of using this method to trigger an external component for estimating the amount of fluid intake. This shall contribute towards the efficiency of more robust multimodal approaches addressing the problem of water intake monitoring. The system, based on a single inertial measurement unit placed on the forearm, is unobtrusive, user-independent, and lightweight enough for real-time mobile processing. Drinking events outside meal periods were detected with an F-score of 97% in an offline validation with data from 12 users, and 85% in a real-time free-living validation with five other subjects, using a random forest classifier. Our results also reveal that the algorithm first detects the hand-to-mouth movement 0.70 s before the occurrence of the actual sip of the drink, proving that this approach can have further applications and enable more robust and complete fluid intake monitoring solutions.

Keywords

• activity recognition
• fluid intake monitoring
• gesture recognition
• inertial sensors