Activity recognition in planetary navigation field tests using classification algorithms applied to accelerometer data.
Accelerometer data provide useful information about subject activity in many different application scenarios. For this study, single-accelerometer data were acquired from subjects participating in field tests that mimic tasks that astronauts might encounter in reduced gravity environments. The primary goal of this effort was to apply classification algorithms that could identify these tasks based on features present in their corresponding accelerometer data, where the end goal is to establish methods to unobtrusively gauge subject well-being based on sensors that reside in their local environment. In this initial analysis, six different activities that involve leg movement are classified. The k-Nearest Neighbors (kNN) algorithm was found to be the most effective, with an overall classification success rate of 90.8%.
Department of Electrical & Computer Engineering, Kansas State University, Manhattan, KS, USA.
Wen
W
Song
Carl
C
Ade
Ryan
R
Broxterman
Thomas
T
Barstow
Thomas
T
Nelson
Steve
S
Warren
D061725:Accelerometry / Q000295:instrumentation* / Q000379:methods*; D000337:Aerospace Medicine / Q000295:instrumentation*; D000465:Algorithms; D004562:Electrocardiography; D006339:Heart Rate / Q000502:physiology; D006801:Humans; D008962:Models, Theoretical; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods*; D009043:Motor Activity / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D012881:Skin Temperature / Q000502:physiology