# Human Activity Recognition Using Cell Phone-Based Accelerometer and Convolutional Neural Network
## 2 Related Work  
The research in the field of HAR has been going on for more than a decade. One of the reasons is that most mobile phones today have sensors included in them. The accelerometer has been one of the most widely used mobile sensors for HAR. Moreover, some of the earlier research works used more than one accelerometer located on several areas of the subject's body [12]. It was primarily because mobile phones with accelerometers were less prevalent a decade before. [PERSON] and [PERSON] [13] used five biaxial accelerometers in their research. The experiment consisted of 20 persons. To collect data from them, the accelerometers were placed on distinct parts of the body such as the non-dominant and upper arm, dominant ankle and wrist and the subject's right hip. They recognized 20 activities of daily living by creating models using decision models, instance-based learning, C4.5 and Naive Bayes classification algorithms. Their experiment deduced that the most suitable position of the accelerometer to place was on the user's thigh. This finding supports the decision implemented during data collection that the pants pocket has been a suitable position for the subject to carry their phones in. On contrary, [PERSON] et al. [14] criticized the use of accelerometers on thighs as the collected data were inadequate for classifying activities such as walking, sitting, lying down, etc. Therefore, they emphasized the necessity of multiple accelerometers. Although these systems using multiple sensors can identify varied human activities, they lack practicality and convenience as it requires the users to wear several sensors on their bodies. Studies on HAR have also been conducted using a combination of sensors of diverse types. [PERSON] et al. [15] employed a multimodal sensor apparatus consisting of seven types of sensors, viz., visible/IR light sensor, tri-axial accelerometer, humidity/temperature reader, barometer, microphone, visible light phototransistor, and a compass. They were able to identify activities such as brushing teeth, ascending and descending stairs, standing, sitting, walking and an elevator moving up and down. Such a multi-sensor approach indicates that there lies a remarkable capability in mobile sensor data, especially when a wide range of sensors are incorporated. More recent works based on multiple sensors have been implemented in [16; 17]. In these studies, more advanced sensors such as gyroscope, GPS (Global Positioning System), magnetometer, etc., have been used apart from the accelerometers. Long-themed activities can be recognized more precisely and in a realistic scenario with the aid of such advanced sensors. In the knowledge area of HAR, other datasets apart from the one used in this paper have been published previously. The UCI (University of California, Irvine) HAR dataset is one of the notable datasets which has been widely used in different works and comparisons. This dataset was proposed in [18] and it was built from the data gathered from inertial sensors embedded in smartphones. There was a total of 30 participants involved in the experiment. Each person performed six activities, viz., walking downstairs, walking upstairs, standing, sitting, laying down and walking. Using the embedded sensors, linear acceleration and angular velocity associated with the three axes were captured with a frequency of 50 Hz. The size of the sliding windows was 2.56 s, and the signals were sampled with 50% overlap (128 readings/window) between them. For each record in the dataset, the time and frequency domain variables consisted of a 561-feature vector, which consequently yielded reasonable outcomes. However, the dataset is collected in constrained laboratory circumstances with restrictions on a fixed placement and orientation of the device. Due to such a limitation, in a real-time scenario, the results obtained would not be trustworthy as the users could use their phones in suitable ways. The next notable dataset is the HARSense dataset, one which has been used in [19].