Measuring and Classifying Land-Based and Water-Based Daily Living Activities Using Inertial Sensors

Abstract

This study classified motions of typical daily activities in both environments using inertial sensors attached at the chest and thigh to determine the optimal site to attach the sensors. Walking, chair standing and sitting, and step climbing were conducted both in water and on land. A mean, variance and skewness for acceleration data was calculated. A Neural Network and Decision Tree algorithm was applied for classifying each motion in both environments. In total, 126 and 144 samples of thigh and chest data sets were obtained for analysis in each condition. For the chest data, the algorithm correctly classified 80% of the water-based activities, and 90% of the land-based. Whilst the thigh sensor correctly classified 97% of water-based and 100% of land-based activities. The inertial sensor placed on the thigh provided the most appropriate protocol for classifying motions for land-based and water-based typical daily life activities.

Keywords

• underwater
• immersion
• accelerometer
• gyroscope
• ADL