Puspa Inayat Khalid

Extraction of dynamic features from hand drawn data for the identification of children with handwriting difficulty

Studies have shown that differences between children with and without handwriting difficulties lie not only in the written product (static data) but also in dynamic data of handwriting process. Since writing system varies among countries and individuals, this study was conducted to determine the feasibility of using quantitative outcome measures of childrens drawing to identify children who are at risk of handwriting difficulties. A sample of 143 first graders of a normal primary school was investigated regarding their handwriting ability. The children were divided into two groups: test and control. Ten children from test group and 40 children from control group were individually tested for their Visual Motor Integration skills. Analysis on dynamic data indicated significant differences between the two groups in temporal and spatial measures of the drawing task performance. Thus, kinematic analysis of childrens drawing is feasible to provide performance characteristic of handwriting ability, supporting its use in screening for handwriting difficulty.

The use of graphic rules in grade one to help identify children at risk of handwriting difficulties.

Previous researches on elementary grade handwriting revealed that pupils employ certain strategy when writing or drawing. The relationship between this strategy and the use of graphic rules has been documented but very little research has been devoted to the connection between the use of graphic rules and handwriting proficiency. Thus, this study was conducted to investigate the relative contribution of the use of graphic rules to the writing ability. A sample of 105 first graders who were average printers and 65 first graders who might experience handwriting difficulty, as judged by their teachers, of a normal primary school were individually tested on their use of graphic rules. It has been found that pupils who are below average printers use more non-analytic strategy than average printers to reproduce the figures. The results also reveal that below average printers do not acquire the graphic principles that foster an analytic approach to production skills. Although the findings are not sufficient to allow definitive conclusions about handwriting ability, it can be considered as one of the screening measures in identifying pupils who are at risk of handwriting difficulties.

Automated syntax analyses of drawing two tangent patterns in children with low and average handwriting ability

Sequential strategies of digitized tablet drawings by 6–7–yr.-old children (N = 203) of average and below-average handwriting ability were analyzed. A Beery Visual Motor Integration (BVMI) and a Bender-Gestalt (BG) pattern, each composed of two tangential shapes, were predefined into area sectors for automatic analysis and adaptive mapping of the drawings. Girls more often began on the left side and used more strokes than boys. The below-average handwriting group showed more directional diversity and idiosyncratic strategies.

Analyses of pupils' polygonal shape drawing strategy with respect to handwriting performance

Polygonal shape drawing tasks are commonly used in psychological, clinical and standard handwriting tests to evaluate children’s development. Early detection of physical/mental disorders within subjects therefore requires objective analysis of the drawing tasks. This analysis would help to identify specific rehabilitation needs and accurate detection of disorders. Herein, the aim is to determine the correlation between the performance of polygonal shape drawing and levels in handwriting performance. In the reported experimentation two groups of participants aged between 6 and 7 were studied. The first group was identified by educational experts as being below-average writers within their age group whilst the second group was age-matched controls of average and above. Subjects were required to draw an isosceles triangle within a novel computer-based framework founded on a pen-based graphic tablet capture device. Subsequently, a sequential feature vector containing performance values relating to the order in which they drew the triangle was extracted from tablet data and compared against one another when presented in constructional strategy models. Statistical analyses and automated classification were applied to sequences to infer handwriting level based on the triangle drawing strategy. From our experiments drawing strategies showed significant differences in drawing end-point position, number of strokes used, and the frequency of particular drawing strategies amongst average and below-average handwriting groups. Additionally, a support vector machine classifier was used to detect group membership based on the triangle drawing strategy. From this exemplar polygonal shape drawing study it is revealed that there are details in children’s drawing strategy which considerably differs in grouping based on handwriting performance.

Dynamic features of handwriting and cortico-cortical functional connectivity during basic geometric drawing based on gender

The aim of the study is to investigate the dynamic features of handwriting and the directional connectivity in brain among young children during basic drawing task. Seven children participated in the study where four of them were female. To exercise motor ability, three different unlined shapes were selected which the subject must gaze and trace on WACOM digitizing tablet. While doing the basic drawing task, brain signals (EEG) were recorded to analyze the information pathway based on partial directed coherence (PDC) method. Result showed that all subjects regardless of gender performed the basic drawing task with preferred rule. Again, regardless of gender, PDC showed that most information sources came from parietal, frontal and occipital areas even-though dynamic features of handwriting (pressure and altitude) showed gender preferences. It is found also that gazing while planning for tracing and actually doing the tracing activity shows almost similar result, i.e. similar sources of information. Based from the pattern of information pathway in the brain among the subjects during gazing, the tracing activity is thought to be well planned. Most of the subjects make use of areas where visual processing, pattern recognition, motor planning and perception midline and route finding are executed during the performances.

Identification of phoneme and its distribution of malay language derived from Friday sermon transcripts

Lack of text data is one of the main issues encountered by Malay speech researchers. Currently, there are few established Malay text corpora to aid in their research. Text corpora are essential due to its ability to provide empirical data for researchers in the field of linguistics and are useful to construct word lists for speech intelligibility test, speech analysis across genders and automatic speech recognition. The text corpora also need to mimic the natural phoneme of the language it represents. To accomplish this, we need to know the phonetic distribution of the language. The purpose of this research is to devise a phoneme distribution for the Malay language based on the transcripts obtained from fifty two Friday sermons. The Friday sermon transcripts were obtained through the official government website and then standardized by removing images and foreign letters; expanding acronyms and short forms; converting numbers and symbols to appropriate Malay words. The transcripts were then phonetically transcribed by first identifying the language rules and wrote a program based on those rules. The program was written using Personal Home Page (PHP) and the data were then stored into MySQL (Sequential Query Language). The data were then retrieved and compared to the Malay words used in news broadcast. In conclusion, the Malay used in Friday sermon and news broadcast differs in the usage of the phonemes /a/, /e/, /o/, /d/, /p/, /t∫/, /n/, /l/, /h/ and /r/.

Derivation of simple muscle fatigue index for biceps muscle based on surface electromyography temporal characteristics

Biceps brachii muscle which attached to the forearm bone is one of the important muscles to the athletes who involve in sports like badminton, tennis and volleyball. Repetition of the arm such as throwing and hitting can lead to muscle fatigue. This physiological phenomenon needs to be monitored and well controlled especially in athletes training. The purpose of this study was to formulate a simple muscle fatigue index for the biceps brachii muscles. Ten male badminton players were chosen to be the subjects for this study. Each subject was asked to do dynamic contraction by lifting 5 kilogram dumbbell. This exercise is called biceps curl exercise and the subjects were asked to repeat the task for one minute and thirty seconds. The electromyogram signal was recorded using Neuroprax EEG device. For that purpose, monopolar surface electrodes were attached to the biceps muscle of the subject. The electromyogram signals were then processed using MATLAB software. Four parameters in time domain were extracted; Zero Crossings (ZC), Root Mean Square (RMS), Mean Absolute Value (MAV), and Variance (VAR). Except for zero crossings (ZC), all other parameters showed significant difference between fatigue signal and non-fatigue signal (p-value <; 0.001). RMS was found to correlate very well with MAV (0.999). The study concludes that several temporal characteristics from electromyogram signal could be used in the formulation of biceps muscle fatigue index, supporting its use in monitoring muscle endurance.

Automatically measuring the effect of strategy drawing features on pupils’ handwriting and gender

Children’s dynamic drawing strategies have been recently recognized as indicators of handwriting ability. However the influence of each feature in predicting handwriting is unknown due to lack of a measuring system. An automated measuring algorithm suitable for psychological assessment and non-subjective scoring is presented here. Using the weight vector and classification rate of a machine learning algorithm, an overall feature’s effect is calculated which is comparable in different groupings. In this study thirteen previously detected drawing strategy features are measured for their influence on handwriting and gender. Features are extracted from drawing a triangle, Beery VMI and Bender Gestalt tangent patterns. Samples are related to 203 pupils (77 below average writers, and 101 female). The results show that the number of strokes in drawing the triangle pattern plays a major role in both groupings; however Left Tendency flag feature is affected by children’s handwriting about 2.5 times greater than their gender. Experiments indicate that different forms of a feature sometimes show different influences.