## 4 Experiments
### Application and Datasets  
For the Human Activity Recognition (HAR) tasks using EI-RNNs, we used eight static time-series datasets for our experiments. The datasets are captured from accelerometers, gyroscopes, temperature sensors, smartphones with sensors, and magnetometers. The details of the dataset are given in Tables 3 and 4. In the tables, input features represent the total input dimension, which is equal to the total time steps times the number of features from the sensors. We applied the hold-out protocol to the datasets described in Table 3, where they underwent train-validation-test splits for RNN training (see Figure 8a). We applied the k-fold cross-validation protocol to the datasets described in Table 4.  
Figure 7: Recurrent Neural Network-based application-to-device edge-mapping workflow.  
There are a few challenges in the datasets retrieved. Few datasets are balanced, and categories are definite, while a few are unbalanced (see Figure 8b) and have the null class problem (Opportunity dataset). Imbalances occur when few activities take place for a longer duration (and so are recorded several times), and few activities take place rarely (and so are recorded fewer times). Typically, only a few parts of the data are relevant, and irrelevant information constitute the null class [48].  
Next, there can be large number of sensor modalities, and the number of participants and trial records may differ in each dataset. Both these factors influence the performance of the model under study. (Ref. [49], Figure 1 shows how the accelerometer dataset is annotated for the HAR task).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**SI No.**} & \multirow{2}{*}{**Dataset**} & **Input** & **No. of** & **No. of** & **No. of** & **Sampling** & **No. of** & **Balanced** \\  & & **Sensor** & **Samples (SNOW)** & **Samples (FNOW)** & **Classes** & **Frequency (Hz)** & **Features** & **Balanced** \\ \hline
1 & MHEALTH [53] & A, G, M & 2555 & 1335 & 12 & 50 & 5750 & True \\ \hline
2 & USCHAD [54] & A, G & 9824 & 5038 & 12 & 100 & 3000 & False \\ \hline
3 & WHARF [55] & A & 3880 & 2146 & 12 & 32 & 480 & False \\ \hline
4 & WISDM [56] & A & 20,846 & 10,516 & 6 & 20 & 300 & False \\ \hline \hline \end{tabular}
\end{table}
Table 4: Details of the datasets under k-fold cross validation protocol. A = accelerometer, G = gyroscope, M = magnetometer.  
Figure 8: (a) Train–val–test split ratios are uneven across datasets; (b) an example for imbalanced classes in Opportunity dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**SI No.**} & \multirow{2}{*}{**Dataset**} & **Input** & **Train** & **Val** & **Test** & **Time** & **Input** & **Output** & **Freq.** \\  & & **Sensor** & **Samples** & **Samples** & **Samples** & **Steps** & **Features** & **Labels** & **(Hz)** \\ \hline
1 & DSA [49] (B) & A, M & 6976 & 1232 & 912 & 125 & 5625 & 19 & 25 \\ \hline
2 & SPHAR [50] (B) & A, G & 7878 & 1391 & 1030 & 128 & 1152 & 6 & 50 \\ \hline
3 & Opportunity [51] (N) & A, G, M & 54,246 & 9894 & 2684 & 24 & 1896 & 18 & 30 \\ \hline
4 & Pamap2 [52] (B) & A, G, M & 39,452 & 7566 & 6946 & 24 & 1248 & 12 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Details of the datasets where train, validation, and test samples were generated. The semi non-overlapping windowing technique was used for generation of Opportunity and Pamap2. A = accelerometer, G = gyroscope, M = magnetometer, B = balanced dataset, N = null categories present.