*Autonomous Update of a Dataset for Anomaly Detection Services in Elderly Care Smart House DOI: http://dx.doi.org/10.5772/intechopen.103953*


#### **Figure 3.** *Sample contents of the MobiAct dataset.*


#### **Figure 4.**

*Sample contents of the Ucihar dataset.*

human activities include that were studied include, Standing, Sitting, Laying, walking fast, walking slow, walking downstairs, and walking upstairs [24]. We divided these activities into two groups per dataset. Group 1 contains ADLs and group 0 contains falls. There are four types of fall activities and six types of ADLs. For data preprocessing, an operation is performed to divide records into two groups. In the fall detection procedure, MobiAct and Sisfall datasets will groups ADLs as zeros and all the several types of falls as ones. In the activity recognition procedure, the data in the Ucihar dataset's group consisting of Laying activity (sleeping) is labeled as 1, and all other activities are grouped under label 0. By creating these two groups we can thus use a simpler classifier, binary classification, instead of multiple classifications.
