Inertial sensor based human behavior recognition in modal testing using machine learning approach

Abstract

Adaptive phase control impact device (APCID) was developed for performing in-service modal analysis using impact synchronous modal analysis. However, this device is large and heavy, making it unsuitable for real world applications. This automated impact device can be replaced with human hand but the randomness in human behavior can reduce the accuracy of APCID control scheme. To replace APCID with a smart semi-automated device while still using APCID control scheme, machine learning models are presented in this paper to recognize human behavior by classifying 13 different impact types and predicting impact time using the impact classification. The impact classification model gave classification accuracy of over 96% with 130 real time impacts. With successful classification of different impact types, randomness in human behavior can be reduced by two to three times by associating a range of impact time with each impact type. However, the impact time ranges may differ person to person. To address this issue and to further reduce variations in impact time, a time prediction machine learning model was developed to make compensations in the control scheme of APCID by predicting impact time. The model gave reasonable accuracy with mean prediction errors of 5.2% in real time testing compared to measured time for 100 impacts.