Polymeric Foam Pressure-sensing Pens for Measuring Written Language Production

To understand human cognition, cognitive and behavioral scientists measure external behavior using a variety of tools. However, many of these tools are not sensitive enough to detect small changes in behavior, they are too costly, or they can only be used in dedicated lab space, thus limiting behavioral science from studying many populations. Here, we present a reliable, robust, cost-effective device that can measure small modulations in human handwriting behavior through pressure sensing on the writing instrument itself. This is made possible through a cross-disciplinary approach, combining advantages of new, high-sensitivity pressure sensors and experimental psycholinguistics. We show that this instrument is reliable and sensitive to the typical pressure range in writing. Then, we present a proof of concept from an experimental replication and demonstrate the utility of handwriting pressure measurement in a classic experimental paradigm, thus opening new research directions in psycholinguistics, cognitive science, and psychology.

Online Signature Recognition Based on Pseudo-Inked Signature Image Template

As human–robot interaction is widely and increasingly used, automated user verification has become a necessary condition for system access. Signature recognition is one of the representative methods for user verification. In this paper, a novel method using Pseudo-Inked Signature for online signature recognition is proposed. Pseudo-Inked Signature consists of three types of information of pen pressure value, pen tilting angle, and pen theta angle during online signature writing. We propose a fusion method for three different types of information by mimicking the inked effect of real pen writing. Besides a style of penmanship, Pseudo-Inked Signature reflects the characteristics of handwriting behavior. Therefore, it can make different Pseudo-Inked Signature even though the original signature images from different users look very similar to each other. Similarly, it can also make more similar Pseudo-Inked Signatures even though the original signature images from the same user look somewhat different to each other. In addition, since only one gray-scale image is dealt with to represent the signature style of a person by Pseudo-Inked Signature image, it is efficient and very easy to handle. Finally, we tested user verification experiments using [Formula: see text]-NN classifier. The experimental results show that Pseudo-Inked Signature is good enough for the real application.

Development of a Hand-Arm Model to Emulate Tremor Handwriting Behavior

Patients with hand tremor may find difficulties in performing their routine task especially writing. The worse when the tremor becomes severe. In this paper, a hand-arm model has been developed to study the behavior of tremor while performing handwriting. Regarding the study, the hand of patient oscillates in a perpendicular direction to the forearm rather than other directions due to hand support while the handwriting is performed. A miniature accelerometer patched to the writing device and acceleration data recorded have then been examined by power spectral density (PSD). For hand-arm model setup, the DC motor was used to emulate the hand movement to draw a straight line by connecting the linear screw thread to the writing mechanism. For writing mechanism, there are two parts, holder to hold the pen and Linear Voice Coil Actuator (LVCA) to resemble hand tremor conditions. By injecting tremor data to the LVCA the acceleration is measured once again by attaching a miniature accelerometer to the writer device. The findings show that the model can emulates hand tremor by considering its acceleration and frequency range from PSD based on record actual tremor data as references. The hand-arm model furthers can be a research conduct to design a writing device that can be cancelled or at least suppress the tremor.

Developmental Trends of Visual Feedback Control in Copying and Handwriting Performances of Children Aged 7 through 11 Years

According to a neurogeometric motion theory, S would experience changes in normal feedback information as perturbations in executing copying and handwriting activities. Sensory feedback mechanisms in such psycho-motor skill performances seem to depend on perceptual-motor abilities and thus play an important role in such behavior. Hence, a parallel developmental trend regarding feedback control in copying/handwriting behavior was expected. A study organized as a two-way design with 48 students aged 7 to 11 yr. was carried out. The results showed that different forms of displaced feedback had different negative effects on accuracy and speed in copying/handwriting performances, whereas an improved feedback control in the same functions could be observed from CA 7 to 11. No interaction effects were found between the two independent factors under research.