# Human Activity Recognition Using Cell Phone-Based Accelerometer and Convolutional Neural Network
## 2 Related Work
For robust comprehensive data collection, two sensors (accelerometers and gyroscopes) were used from two different smartphones and two mounting locations in the subject's body were used viz. front pocket and waist. In total, 16 features were collected from the sensors present in both smartphones. This was carried out with the help of an Android application. Some of the prominent features consisted of gravitational acceleration, gravity, rotational rate, and rotational vectors. The dataset generation adopted a physique-based technique in which the data of multiple subjects were clubbed based on the resemblance of their height and weight. The downside of this dataset generation technique is that it is bound to involve participants with a similar physique. This is likely to result in a lesser number of participants, and hence, fewer data. The Universidade da Coruna (UDC) dataset is yet another promising and flexible dataset used in [1]. The major principle used while collecting data for this dataset is to make it more realistic and applicable in real life which was a lack in dataset developed so far in HAR. The highlighting feature of this dataset is that the smartphone used to collect data can be put in various places of the subject's body and not only in the pockets or around the waist. However, the challenge lies in developing a model that could use such a complex dataset and predict varied states of human activities such as inactive, active, walking and driving. Furthermore, many other works built their own datasets and carried on with their research on HAR. For instance, in [20], the experiment consisted of nine different orientations of the smartphone. They proposed an online support vector machine (SVM) model to solve the problem. They also made comparisons between their approach with some other state-of-the-art techniques such as Random Forest, CNN, etc. Following the same line, Deep Learning methods have also been used to prepare datasets for HAR. The results in [21] show that these methods are futuristic as they can predict non-stationary activities such as running, jogging, or walking more efficiently. Additionally, it has been found that SVM provides better results in predicting activities that take place for shorter periods. Moving on, models using the long short-term memory (LSTM) approach are becoming popular for both HAR and abnormal human activity prediction [5]. The main advantage of LSTM techniques is that they do not require feature extraction in advance to proceed to model training. However, they require huge datasets for accurate classification outcomes. In addition, they need excess time for model training and to avoid overfitting (and underfitting), a suitable stop criterion is also required. For instance, in [22] and [23], we can see that the LSTM model yielded particularly reliable results. In fact, Bi-LSTMs (bidirectional LSTMs-an improved version of regular LSTMs) used in [22] have shown exemplary results as the neural network adopts prospective learning, and thus the results are obtained with accuracy as high as 95%. Moreover, these studies have also used the sliding window approaches in their methods for performing HAR, as can be seen in [24]. Again, this approach depends on the kinds of human activities taken into consideration, placement of the sensor (accelerometer has been used as a sensor), classification methods used and the window size (in seconds). For example, [PERSON] et al. [25] used Frequent Bit Pattern-based Associative Classification (FBPAC) to identify activities such as walking, sitting/standing, lying down and falling. The classification algorithm used a pattern mining based approach to identify the classes corresponding to human activities. The sensor in this study was placed around the chest of the subject. With a window size of 10, they achieved an in-subject classification accuracy of 92%. The aforementioned methods and techniques have their own advantages. Some of their limitations include:  
1.