Environmental Sensing by Wearable Device for Indoor Activity and
  Location Estimation

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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.

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Optimal Placement of Accelerometers for the Detection of Everyday Activities

Ian Cleland, Basel Kikhia, Chris Nugent … (2013)

This article describes an investigation to determine the optimal placement of accelerometers for the purpose of detecting a range of everyday activities. The paper investigates the effect of combining data from accelerometers placed at various bodily locations on the accuracy of activity detection. Eight healthy males participated within the study. Data were collected from six wireless tri-axial accelerometers placed at the chest, wrist, lower back, hip, thigh and foot. Activities included walking, running on a motorized treadmill, sitting, lying, standing and walking up and down stairs. The Support Vector Machine provided the most accurate detection of activities of all the machine learning algorithms investigated. Although data from all locations provided similar levels of accuracy, the hip was the best single location to record data for activity detection using a Support Vector Machine, providing small but significantly better accuracy than the other investigated locations. Increasing the number of sensing locations from one to two or more statistically increased the accuracy of classification. There was no significant difference in accuracy when using two or more sensors. It was noted, however, that the difference in activity detection using single or multiple accelerometers may be more pronounced when trying to detect finer grain activities. Future work shall therefore investigate the effects of accelerometer placement on a larger range of these activities.

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Sensing meets mobile social networks

Emiliano Miluzzo, Nicholas Lane, Kristóf Fodor … (2008)

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A real-time articulated human motion tracking using tri-axis inertial/magnetic sensors package.

Cai-Rong Zhu, Zhaoying Zhou (2004)

A basic requirement in virtual environments is the tracking of objects, especially humans. A real time motion-tracking system was presented and evaluated in this paper. System sensors were built using tri-axis microelectromechanical accelerometers, rate gyros, and magnetometers. A Kalman-based fusion algorithm was applied to obtain dynamic orientations and further positions of segments of the subject's body. The system with the proposed algorithm was evaluated via dynamically measuring Euler orientation and comparing with other two conventional methods. An arm motion experiment was demonstrated using the developed system and algorithm. The results validated the effectiveness of the proposed method.