Feature Extraction and Evaluation for Classification Models of Injurious Falls Based on Surface Electromyography

Abstract

Classification Datamining Electromyography Falls Injurious falls Muscle activationBackground: Only 2% of falls in older adults result in serious injuries (i.e., hip fracture).Therefore, it is important to differentiate injurious versus non-injurious falls, which is critical to develop effective interventions for injury prevention. Objects:The purpose of this study was to a. extract the best features of surface electromyography (sEMG) for classification of injurious falls, and b. find a best model provided by data mining techniques using the extracted features.Methods: Twenty young adults self-initiated falls and landed sideways. Falling trials were consisted of three initial fall directions (forward, sideways, or backward) and three knee positions at the time of hip impact (the impacting-side knee contacted the other knee ("knee together") or the mat ("knee on mat"), or neither the other knee nor the mat was contacted by the impacting-side knee ("free knee"). Falls involved "backward initial fall direction" or "free knee" were defined as "injurious falls" as suggested from previous studies. Nine features were extracted from sEMG signals of four hip muscles during a fall, including integral of absolute value (IAV), Wilson amplitude (WAMP), zero crossing (ZC), number of turns (NT), mean of amplitude (MA), root mean square (RMS), average amplitude change (AAC), difference absolute standard deviation value (DASDV). The decision tree and support vector machine (SVM) were used to classify the injurious falls.Results: For the initial fall direction, accuracy of the best model (SVM with a DASDV) was 48%. For the knee position, accuracy of the best model (SVM with an AAC) was 49%. Furthermore, there was no model that has sensitivity and specificity of 80% or greater. Conclusion:Our results suggest that the classification model built upon the sEMG features of the four hip muscles are not effective to classify injurious falls. Future studies should consider other data mining techniques with different muscles.

Application of Decision Tree to Classify Fall Risk Using Inertial Measurement Unit Sensor Data and Clinical Measurements

“…Furthermore, Lim and Choi [49] suggested that SVM showed higher accuracy than DT for 2 impurity criteria. Therefore, future studies, including various machine learning algorithms (i.e., Naïve Bayes, K-nearest neighbor, Random Forest, Convolutional Neural Network), might help find optimal algorithms for fall-risk classification models using IMU sensors.…”