Transition Activity Recognition System Based on Standard Deviation Trend Analysis
Junhao Shi, Decheng Zuo, Zhan Zhang
Abstract
With the development and popularity of micro-electromechanical systems (MEMS) and smartphones, sensor-based human activity recognition (HAR) has been widely applied. Although various kinds of HAR systems have achieved outstanding results, there are still issues to be solved in this field, such as transition activities, which means the transitional process between two different basic activities, discussed in this paper. In this paper, we design an algorithm based on standard deviation trend analysis (STD-TA) for recognizing transition activity. Compared with other methods, which directly take them as basic activities, our method achieves a better overall performance: the accuracy is over 80% on real data.
Topics & Concepts
Standard deviationPopularityActivity recognitionComputer scienceField (mathematics)Process (computing)Transition (genetics)Data miningArtificial intelligenceStatisticsMathematicsOperating systemChemistryPsychologySocial psychologyPure mathematicsGeneBiochemistryContext-Aware Activity Recognition SystemsGreen IT and SustainabilityIoT and Edge/Fog Computing