A new classification strategy for human activity recognition using cost sensitive support vector machines for imbalanced data

Purpose The task of identifying activity classes from sensor information in smart home is very challenging because of the imbalanced nature of such dataset where some activities occur more frequently than others. Typically probabilistic models such as Hidden Markov Model (HMM) and Conditional Random Fields (CRF) are known as commonly employed for such purpose. Design/methodology/approach In this work, we propose a robust strategy combining the Synthetic Minority Over-sampling Technique (Smote) with Cost Sensitive Support Vector Machines (CS-SVM) with an adaptive tuning of cost parameter in order to handle imbalanced data problem. Findings The results have demonstrated the usefulness of the approach through comparison with state of art of approaches including Hidden Markov Model (HMM), Conditional Random Field (CRF), the traditional C-SVM and the Cost-Sensitive-SVM (CS-SVM) for classifying the activities using binary and ubiquitous sensors. Originality/value Performance metrics in the experiment/simulation include Accuracy, Precision/Recall and F-measure.