Online Handwriting Recognition Using an Accelerometer-Based Pen Device

Abstract

Abstract-This paper presents an accelerometer-based pen device for online handwriting recognition applications. The accelerometer-based pen device consists of a triaxial accelerometer, a microcontroller, and an RF wireless transmission module. Users can hold the pen device to write numerals in air without space limitations. The accelerations generated by hand motions are generated by the accelerometer embedded in the pen device, and are transmitted to a personal computer for further signal preprocess via the wireless module. Subsequently, a dynamic time warping (DTW) algorithm is applied to align the accelerations and search class templates for each digit in the training stage. Finally, the accelerations can be recognized via the alignment with the class templates in the testing stage. The average user-dependent and userindependent recognition rates were 90.6% and 84.8%, respectively. Our experimental results have successfully validated the effectiveness of the accelerometer-based pen device for 3-dimensional (3-D) online handwriting recognition. IndexTerms-Onlinehandwriting recognition, accelerometer-based input device, dynamic time warping.

Hand Gesture Recognition on a Resource-Limited Interactive Wristband

“…Most of the widely-used methods, such as support vector machine (SVM) [ 5 , 8 , 9 , 10 ], hidden Markov model (HMM) [ 11 , 12 , 13 ], neural network [ 14 , 15 , 16 , 17 ], and dynamic time warping (DTW) [ 3 , 18 , 19 , 20 , 21 , 22 ] have achieved good results for recognition accuracy, from 90% to 100%. Traditional machine learning (ML)and neural network (deep learning, DL) methods are data-driven.…”

“…However, this level of computing still has relatively high requirements for CPU (Qualcomm Snapdragon 810, for example), which make it difficult to run on a very low-cost MCU, such as Cortex32-M0 as used in this paper. In fact, almost all the studies implement their HGR algorithm on powerful computing devices, such as PC [ 7 , 9 , 10 , 13 , 14 , 18 , 19 , 22 ], smartphone [ 3 , 21 ], or FPGA [ 15 ]. The difficulties lie in compromising both on the hardware cost and the algorithm performance.…”

“…The difficulties lie in compromising both on the hardware cost and the algorithm performance. A conventional way for most studies is to proceed to collect inertial sensor data on a cheap microcontroller and transmit them to a PC for the HGR algorithm, e.g., the inertial pen proposed by Hsu et al in [ 18 , 19 ] and the wrist-worn band proposed by Liu et al in [ 22 ]. In general, the dependence on additional computational equipment presents a cost problem.…”

“…For the user’s experience, wired or wireless and hand-held or wearable also need to be considered. Besides integrated devices such as smartphones, most of the studies transmit sensor data to PCs wirelessly [ 7 , 9 , 10 , 13 , 18 , 19 , 22 ] or via a data cable [ 9 , 14 , 15 ]. Some studies developed hand-held modules, such as inertial pen [ 18 , 19 ], sensing mote [ 13 ], or HGR for smartphones [ 3 , 21 ].…”

“…Besides integrated devices such as smartphones, most of the studies transmit sensor data to PCs wirelessly [ 7 , 9 , 10 , 13 , 18 , 19 , 22 ] or via a data cable [ 9 , 14 , 15 ]. Some studies developed hand-held modules, such as inertial pen [ 18 , 19 ], sensing mote [ 13 ], or HGR for smartphones [ 3 , 21 ]. In most scenarios, wearable devices such as gloves [ 9 ] and wristbands [ 22 ] are more convenient for users.…”