Planetary navigation activity recognition using wearable accelerometer data



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Kansas State University


Activity recognition can be an important part of human health awareness. Many benefits can be generated from the recognition results, including knowledge of activity intensity as it relates to wellness over time. Various activity-recognition techniques have been presented in the literature, though most address simple activity-data collection and off-line analysis. More sophisticated real-time identification is less often addressed. Therefore, it is promising to consider the combination of current off-line, activity-detection methods with wearable, embedded tools in order to create a real-time wireless human activity recognition system with improved accuracy. Different from previous work on activity recognition, the goal of this effort is to focus on specific activities that an astronaut may encounter during a mission. Planetary navigation field test (PNFT) tasks are designed to meet this need. The approach used by the KSU team is to pre-record data on the ground in normal earth gravity and seek signal features that can be used to identify, and even predict, fatigue associated with these activities. The eventual goal is to then assess/predict the condition of an astronaut in a reduced-gravity environment using these predetermined rules. Several classic machine learning algorithms, including the k-Nearest Neighbor, Naïve Bayes, C4.5 Decision Tree, and Support Vector Machine approaches, were applied to these data to identify recognition algorithms suitable for real-time application. Graphical user interfaces (GUIs) were designed for both MATLAB and LabVIEW environments to facilitate recording and data analysis. Training data for the machine learning algorithms were recorded while subjects performed each activity, and then these identification approaches were applied to new data sets with an identification accuracy of around 86%. Early results indicate that a single three-axis accelerometer is sufficient to identify the occurrence of a given PNFT activity. A custom, embedded acceleration monitoring system employing ZigBee transmission is under development for future real-time activity recognition studies. A different GUI has been implemented for this system, which uses an on-line algorithm that will seek to identify activity at a refresh rate of 1 Hz.



Activity recognition, Machine learning, Graphical User Interface, Accelerometer, Feature selection

Graduation Month



Master of Science


Department of Electrical & Computer Engineering

Major Professor

Steven Warren