Environmental Sensing by Wearable Device for Indoor Activity and Location Estimation

TL;DR

This study develops a sensor fusion method using environmental and acceleration data from wearable devices to accurately recognize indoor activities and locations, aiding energy management and user comfort.

Contribution

It introduces novel features from environmental measurements for activity and location recognition, achieving high classification accuracy through sensor fusion.

Findings

Achieved 99.13% accuracy in activity and location classification.

Environmental features effectively distinguish different activities and locations.

Sensor fusion enhances recognition performance significantly.

Abstract

We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carried multi-purpose sensors. Hypotheses with respect to each type of measurements are verified, including temperature, humidity, and light level collected during eight typical activities: sitting in lab / cubicle, indoor walking / running, resting after physical activity, climbing stairs, taking elevators, and outdoor walking. Our main contribution is the development of features for activity and location recognition based on environmental measurements, which exploit location- and activity-specific characteristics and capture the trends resulted from the underlying physiological process. The features are statistically shown to have good separability and are also information-rich. Fusing environmental sensing together with acceleration is shown to achieve classification accuracy as high as 99.13%. For building applications, this study motivates a sensor fusion paradigm for learning individualized activity, location, and environmental preferences for energy management and user comfort.