# Comparing Human Activity Recognition Models Based on Complexity and Resource Usage
## 2 Related Work
### Deep Learning
The fourth layer on the other hand already introduces too much complexity and probably overfitting which results in a slight decrease in performance [16]. [PERSON] and [PERSON] [17] also use a CNN, as it seems to be the most used deep architecture. Compared to other works using CNNs, they use a novel approach regarding their use of input data. They use all three axes of the gyroscope signal, all three axes of the total acceleration signal (body motion and gravity) and all three axes of the linear acceleration signal (only body motion) to append them as rows of an image. This allows the two dimensional CNN to also learn from relations between those signals [17]. Their algorithm to calculate this image includes all of these signals multiple times per image in differently ordered sequences to make sure the relations of one signal with multiple others can be captured. The magnitude of the Discrete Fourier Transform (DFT) is then used to get the activity image. These activity images (\(36\times 68\) in this case) are fed into two different two-dimensional convolutional layers with subsampling layers in between followed by a fully connected layer and a softmax layer to arrive at the activity predictions. Similar to the findings of [PERSON] and [PERSON] [16], they achieve the best results using two convolutional layers, compared to their other evaluated architectures with only one or three or more convolutional layers. For their best performing model, they report an accuracy of 95.18% for the UCI HAR Smartphone dataset [17]. In their work, [PERSON] and [PERSON] [18] iterate that most of the activity recognition setups treat individual frames of sensor data statistically independent but this actually gets rid of one important component of sensor readings in an activity recognition context, which is the temporal relation of one of these frames with for example its predecessors. This is why they view RNN approaches as a better alternative for the mostly used CNNs. Most of the RNN approaches focus on models with Long Short Term Memory (LSTM) layers, while [PERSON] and [PERSON] focus on an ensemble of such models in order to better deal with their identified issues of current HAR solutions. The first of these identified issues is the data used for HAR, which is mostly recorded in mobile or wearable context, as this data tends to be very noisy with high variations even for one and the same activity. Besides data quality, the quantity of annotated data is an issue, as the recording of sensor data on mobile devices is trivial, but the experiments to record and annotate very big data sets, fitting for deep learning approaches, are challenging to do with good enough class balancing. This can also be seen in available public datasets for HAR that mostly suffer from bad class distribution. Additionally, most approaches are based on sliding window and frame-based predictions that skip the challenge of finding and locating certain relevant sequences of sensor readings in a sequential stream. [PERSON] and [PERSON] want to address these issues by employing their ensemble-based approach. For each training epoch, they train multiple models on randomly sampled sequences of random length of the training data. The best models of these are determined by the validation data and the predictions of those best models are then fused to a single prediction for a specific sample from the test data at a specific point in time. This is essentially bagging, which is normally done with very simple classifiers like trees, with highly complex models. In their results, they observe that this ensemble method mostly has a positive impact on very challenging and similar classes and therefore could indicate better robustness against the previously mentioned issues with real-world data [18]. Additionally to the above mentioned DNNs, RNNs and CNNs, Stacked Autoencoders and Deep Belief Networks (DBNs) are also used for HAR. [PERSON] et al.