TabIe III : TA accuracies for all scenarios using Dataset 1 .
4.2 Model Performance Based on Data Pre-processing Approach For this dataset we made an initial experiment using the TA approach in which the results were well below those from CASAS experiments . They used a baseline of three different models , Naive Bayes , HMM and CRF , getting accuracies of 80.33 %, 75.55 % and 90.76 % respectively . The accuracy for our SVM combined with the TA approach was of only 51 %, which indicated that the TA preprocessing was not the appropriate choice for this dataset . Following the procedures used by the CASAS researchers , we applied the CDA preprocessing to the dataset , and the accuracy of our SVM model increased to 91.77 %, better than any other result so far .
Table IV : The results using Dataset 2 and TA approach showed a poor performance . However , the same data combined with the CDA approach , showed high performance specially when using discriminative models . The multi-class SVM model achieved the best performance of 91.67 %.
As happened with Dataset 1 results , SVM again outperforms all other models considered for CDA processing approach , that is , both models we proposed ( HMM and KNN ) and the results obtained by CASAS algorithm which best result was 90.77 % with a CRF approach . Although when using the TA ( 60 seconds timeslice ) approach HMM performs better ( 51.60 % vs . 59.85 %) than SVM , it is clear that , regardless the algorithm used , this TA is not the best approach to process this data , which is clearly meant to be processed using CDA .
5 Conclusion
In this work , we have presented an activity pattern recognition model using a non-linear multi-class SVM approach for detecting daily activities based on two public available datasets . We have compared our methods with other state of the art machine learning approaches using the same datasets . The result demonstrates the proposed method outperforms other methods . We have developed two data preprocessing techniques including TA ( time slice ) and CDA ( chunk data ). It is noted that appropriate data preprocessing techniques can significantly improve the accuracy of the model . Future work will be to test the models using a wider variety of datasets , data processing approaches and new mathematical modelling approaches to establish more comprehensive model performance baselines .
252 ZEMCH 2015 | International Conference | Bari - Lecce , Italy