# Handwriting Recognition Based on 3D Accelerometer Data by Deep Learning
## 2 Related Work  
Motion sensor data, such as accelerometer and gyroscope data, have been used recently for different tasks such as sign language recognition [2], and online handwritten character recognition [12]. To successfully carry out handwriting recognition, it is necessary to synchronize two main steps, the data acquisition, and identification processes. In the former, some works perform data acquisition by using a specially designed device [12; 13; 14; 15]. Nevertheless, nowadays, many wearable devices such as smartphones or smartwatches are equipped with accelerometers, gyroscopes, magnetometers, and other wearables such as the shimmer3 IMU or the MYO armband. In the latter, handwriting recognition takes charge of processing collected data from the motion sensors to recognize handwritten characters correctly. One clue problem found in online handwriting recognition is related to the length of the sequences for characters of the same class, even those produced by the same person. These variations are related to the writing speed of a character, which simultaneously changes the acceleration of the hand movement. On these grounds, to expect sequences with the same length for the same character class is difficult. However, to eliminate unnecessary data in the sequences for each letter, it is convenient to extract or segment only the part of the signal during which the handwriting process is carried out. A segmentation approach was proposed in [15] and tested in [4; 16]. In this process, only the samples belonging to the handwriting should be considered; otherwise, samples not belonging to the written character will be included. Another approach to only collect the signal during the handwriting process is to consider the information of pen-up and pen-down movements. In some approaches, this task has been addressed using a camera to track hand movements. Afterward, the segmentation is conducted by hand or a specialized writing device (e.g., glove, pen, marker). In this regard, [PERSON] et al. [17] presented a solution to handwriting recognition by developing a user interface to compute numeral recognition in air writing by using a Convolutional Neural Network (CNN). First, they used a fixed-color marker to write in front of a camera,which was followed by a color-based segmentation to identify and track the trajectory of the pen-marker. Next, the classification was carried out by a trained CNN. An accelerometer-based digital pen for handwritten digits and gesture trajectory recognition applications was proposed by [PERSON] and [PERSON] [14]. This pen is based on a triaxial accelerometer to collect the acceleration motion data of the hand; time and frequency domain features are extracted from the obtained acceleration signals. Next, most discriminant features were selected using a hybrid method; kernel-based class separability was applied to select significant features, and Linear Discriminant Analysis (LDA) was used to reduce the dimensionality. Both algorithms were dedicated to training a probabilistic neural network for recognition. [PERSON] et al. [15] proposed a wearable input system for handwriting recognition using an accelerometer and a gyroscope to capture the handwriting gestures. Later, these data were processed using Support Vector Machines (SVM) to identify which data segments contain handwriting. Subsequently, Hidden Markov Models (HMMs) were used for recognition. [PERSON] et al. [4] classified 26 lowercase letters of the English alphabet using 3D gyroscope data instead of 3D accelerometer data, and the Dynamic Time-Warping (DTW) algorithm was used for recognition. [PERSON] et al. [18] proposed a PhonePoint Pen system that uses the built-in accelerometers found in mobile phones to recognize handwritten English characters. Additionally, [PERSON] et al. [19] presented a hand gesture recognition based on mobile devices using the accelerometer and gyroscope sensors. Here, the authors applied a filtering process as preprocessing.