Handwriting-Assistant

Pen-based handwriting has become one of the major human-computer interaction methods. Traditional approaches either require writing on the specific supporting device like the touch screen, or limit the way of using the pen to pure rotation or translation. In this paper, we propose Handwriting-Assistant, to capture the free handwriting of ordinary pens on regular planes with mm-level accuracy. By attaching the inertial measurement unit (IMU) to the pen tail, we can infer the handwriting on the notebook, blackboard or other planes. Particularly, we build a generalized writing model to correlate the rotation and translation of IMU with the tip displacement comprehensively, thereby we can infer the tip trace accurately. Further, to display the effective handwriting during the continuous writing process, we leverage the principal component analysis (PCA) based method to detect the candidate writing plane, and then exploit the distance variation of each segment relative to the plane to distinguish on-plane strokes. Moreover, our solution can apply to other rigid bodies, enabling smart devices embedded with IMUs to act as handwriting tools. Experiment results show that our approach can capture the handwriting with high accuracy, e.g., the average tracking error is 1.84mm for letters with the size of about 2cmx1cm, and the average character recognition rate of recovered single letters achieves 98.2% accuracy of the ground-truth recorded by touch screen.

Well-Lit: A programmable and customizable assistant for manual multi-well plate pipetting

A very large number of biology and biochemistry laboratory protocols require transferring liquid aliquots from individual containers into individual wells of a multi-well plate, from plates to individual containers, or from one plate to another. Doing this by hand without errors, such as skipping wells, placing two samples in the same well, or swapping sample locations, especially when using plates with 96 wells or more, is difficult and requires enormous operator focus and/or a tedious manual error checking system. We present here a device built to facilitate error-free pipetting of samples from individual barcoded tubes to a multi-well plate or between multi-well plates (both 96 and 384 wells are supported). The device is programmable, modular and easily customizable to accommodate plates with different form-factors, and different protocols. The main components are only a 12.3" touch screen, a small form-factor PC, and a barcode scanner, combined with custom-made parts can be easily fabricated with a laser cutter and a hobby-grade 3D printer. The total cost is between approximately US$550 and US$600, depending on the configuration.

The Effect of the ‘Touch Screen-Based Cognitive Training’ for Children with Severe Cognitive Impairment in Special Education

Traditional education in special schools have some limitations. We aimed to investigate if the ‘touch screen-based cognitive training’ is feasible and effective for children with severe cognitive impairment (developmental age 18–36 months) in special education. In this case, 29 children were randomly allocated to intervention (n = 17, ‘touch screen-based cognitive training’, 30 min/session, 3 times/week, 12 weeks) and control (n = 12, traditional education) groups. Psychoeducational Profile-Revised (PEP-R), Early Childhood Behavior Questionnaire (ECBQ), Sequenced Language Scale for Infants (SELSI), Pediatric Evaluation of Disability Inventory (PEDI), and Goal Attainment Scale (GAS) were measured before and after 12 weeks of education. The ‘touch screen-based cognitive training’ was applicable in special education. When repeated measures analysis of variance (ANOVA) was used, significant groupⅹtime effect was found for GAS, and significant group effect was found for ECBQ (attentional shifting) and GAS. When adjusting for pre-education measurements, the intervention had a significant effect on the post-education measurements of ECBQ (attentional shifting) and GAS (p < 0.05). No relationship existed between the degree of improvements and the severeness of developmental delay in the measurements. ‘Touch screen-based cognitive training’ in special school was feasible and it improved cognition in children with severe cognitive impairment (developmental age 18–36 months), irrespective of the severeness of the developmental delay.

Designing Ray-Pointing using Real hand and Touch-based in Handheld Augmented Reality for Object Selection

Augmented Reality (AR) have been widely explored worldwide for their potential as a technology that enhances information representation. As technology progresses, smartphones (handheld devices) now have sophisticated processors and cameras for capturing static photographs and video, as well as a variety of sensors for tracking the user's position, orientation, and motion. Hence, this paper would discuss a finger-ray pointing technique in real-time for interaction in handheld AR and comparing the technique with the conventional technique in handheld, touch-screen interaction. The aim of this paper is to explore the ray pointing interaction in handheld AR for 3D object selection. Previous works in handheld AR and also covers Mixed Reality (MR) have been recapped.