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 thes
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