# Real-Time Machine Learning for Human Activities Recognition Based on Wrist-Worn Wearable Devices
## 2 Related Work  
Visually representing data from a dataset is quite common. Symbolic representation is used to increase the recognition rate and uses high-performance neural networks specialized in image recognition. A significant method of representing data is Human Activity Recognition on Signal Images (HARSI). As described in [22], this method is very efficient and the research describes the method as converting the raw data into an understandable image. The images were generated by plotting the raw data from an accelerometer. The WISDM data set was used for this work and six types of activities were selected: jogging, moving downstairs, walking, moving upstairs, standing, and sitting. In this study, different types of convolutional neural networks were tested. The experimental algorithms tested the following versions: HARSI-ResNet50, HARSI-ResNet101, HARSI-AlexNet, HARSI-DenseNet121, HARSI-SqueezeNet1.0, HARSI-SqueezeNetv1.1, HARSI-VGG16, and HARSI-VGG19. The most efficient was the HARSI-VGG19 method with a 98% accuracy. Another study using the WISDM data set was described in [23]. Two types of algorithms were tested: Convolution Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs). Through these algorithms, the performance of recognizing hand movements was tested using smartwatch data. The following activities were studied: kicking, stairs, standing, jogging, sitting, walking, sandwiching, chips, drinking, soup, pasta, fold ing, typing, teeth, catching, clapping, dribbling, and writing. The data from 44 subjects were selected from the raw data set. In the preprocessing phase, a time-based sliding window was used with a length of 10 s with 5 s of data overlapping. Three types of data sets were tested: data from the accelerometer, data from the gyroscope, and combined data from both the accelerometer and gyroscope. The best results were obtained from combined data from the accelerometer and the gyroscope with a hybrid CNN-LSTM algorithm with an overall recognition rate of 96.20%. Analyzing the confusion table, we observed that the activities with the lower recognition rate were sitting, standing, sandwiching, and writing. The research described in [24] presents a human activity recognition method based on symbolic representation (HAR-SR). This method has two essential parts: the first is signal segmentation-based data fusion and the second is symbolic representation based on patterns from time series. The steps in symbolic representation presented in this research start with data segmentation using a sliding window based on time with a specific length, after the dimension of the segments database is reduced by symbolic approximation and noise removal. Next, a search table was used for the discretization process to obtain the symbolic representation of the segment and, in the final step, each discretized segment was used to obtain a frequency histogram of the symbols. For classification, a K-Nearest Neighbor (kNN) algorithm was used and the recognition rate obtained was 77.82%. An interesting view of human activity recognition was described in [25]. A new architecture of CNNs network was proposed in this research. A new type of classification was added to the convolutional layer. This layer contains a matching filter (MF) based on adding a signal noise comparison function. The activation function from this method is based on calculating the maximum between the known signal or the template signal and the unknown signal or the signal to be known. This function was included in a layer named Conv1D. The Conv1D layer is followed by a GlobalMaxPooling layer and, in the final step, a fully connected layer with softmax activation was added. For the most common activities (walking, jogging, moving downstairs, moving upstairs, sitting), this research obtained a very good total recognition rate of 97.32%. Analyzing the confusion matrix for this method applied to the WISDM data set (only accelerometer data), we observed a lower recognition rate for moving downstairs and moving upstairs activities.