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Existing literature about online handwriting analysis to support pathology diagnosis has taken advantage of in-air trajectories. A similar situation occurred in biometric security applications where the goal is to identify or verify an individual using his signature or handwriting. These studies do not consider the distance of the pen tip to the wr...
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Introduction Existing literature about online handwriting analysis to support pathology diagnosis has taken advantage of in-air trajectories. A similar situation occurred in biometric security applications where the goal is to identify or verify an individual using his signature or handwriting. These studies do not consider the distance of the pen...
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... This suggests that the improvement in terms of sensitivity, observed when combining the velocity and in-air information, mainly comes from the contribution of the in-air movements. The usefulness of in-air patterns in supporting the pathology evaluation has been exploited in previous works [1,22]. ...
Computer aided diagnosis systems can provide non-invasive, low-cost tools to support clinicians. These systems have the potential to assist the diagnosis and monitoring of neurodegenerative disorders, in particular Parkinson's disease (PD). Handwriting plays a special role in the context of PD assessment. In this paper, the discriminating power of "dynamically enhanced" static images of handwriting is investigated. The enhanced images are synthetically generated by exploiting simultaneously the static and dynamic properties of handwriting. Specifically, we propose a static representation that embeds dynamic information based on: (i) drawing the points of the samples, instead of linking them, so as to retain temporal/velocity information; and (ii) adding pen-ups for the same purpose. To evaluate the effectiveness of the new handwriting representation, a fair comparison between this approach and state-of-the-art methods based on static and dynamic handwriting is conducted on the same dataset, i.e. PaHaW. The classification workflow employs transfer learning to extract meaningful features from multiple representations of the input data. An ensemble of different classifiers is used to achieve the final predictions. Dynamically enhanced static handwriting is able to outperform the results obtained by using static and dynamic handwriting separately.
... Both the Five Factor Model and the Big Five model describe hierarchical structures of individual differences, with upper-as well as lowerlevel scales and several questionnaires have been developed in accordance with these models. In this paper, we used the IPIP-NEO-120 questionnaire, developed by Johnson in 2014 [2], a measure composed of 120 items (4 for each of six sub-dimension of each of the Big Five factors) that gives a general overview of the personality On the other hand, handwriting is widely studied both to examine its learning and its alterations due to dysgraphia as well as to tremor or other pathologies [3][4][5]. The approaches in evaluating handwriting can be divided in either graphical or kinematic features analysis. ...
... Thanks to the use of special digital tablets combined with pen capable of writing on ordinary sheets of paper, it is also possible to extract kinematics-related parameters from handwriting during the execution of specific tasks. The variables are calculated on the whole exercise (e.g. total length, duration on sheet or in air, mean velocity or mean pressure, etc.) or as parameters evaluated either on single components (tracts between two successive pen lift) or on single strokes (tracts between two successive minima of the curvilinear velocity) and averaged on the whole test [4,5,7]. However, although graphology hypothesizes a link between writing and personality, and there are some studies that analyse it, to our knowledge, only one study investigated connections between personality traits and handwriting and drawing features recorded through a digitizing tablet able to measure speed, pressure applied to the sheet, dimension and inclination of components [6]. ...
Motor and cognitive systems are largely involved in producing handwriting that develops with age becoming more and more personalized until it reaches a style proper to the subject. This fact has led graphologists to assert that by examining the handwriting it is possible to somehow trace the personality of the writer. Many studies have been carried out to demonstrate this assumption but they are all based on the graphic examination of the tract left on the sheet of paper. On the other hand, recently it has been possible to examine writing through the use of digital tablets capable of providing information also on the kinematic of the movement, extracting parameters used to examine in particular dysgraphia and some neurological pathologies. Aim of this study was to determine possible relationships between kinematic parameters extracted using digital tablets and personality traits. Sixty-one subjects took part in the study, executing three writing tasks (fast and accurate writing of an Italian phrase and fast sequence of cursive lowercase letters “lelele” without pen lifting for 30 s) and a personality test (IPIP-NEO-120). The linear regression between each of fourteen characteristic of handwriting and each of the five personality traits was computed. The results showed that four out of five main psychological tracts presented a linear relation with one or more kinematic characteristics. This study offers a first glance at a complex series of correlations, which will be investigated in future researches.
... The "In air" length indicates the seeking path but the "In air" time may just reflect the halting time. A previous study found that the "In air" time measure may supply information about the perceptual aspect of the motor act 4,30,31 . By comparing with normal controls, the "in air" information in handwriting shows the applicability in the diagnosis of neurodegenerative disease (Parkinson's and Alzheimer's disease) 31 . ...
... A previous study found that the "In air" time measure may supply information about the perceptual aspect of the motor act 4,30,31 . By comparing with normal controls, the "in air" information in handwriting shows the applicability in the diagnosis of neurodegenerative disease (Parkinson's and Alzheimer's disease) 31 . It has been considered to relate to the difficulty with motor memory for letter formation or difficulty in visualizing the letters as needed to form them rapidly 9 . ...
This study proposed a novel computational method for evaluating logographic handwriting. It can precisely evaluate both the handwriting product and the process. The measures included handwriting performance as well as the temporospatial, kinematics, and kinetics features. For examining the psychometrics of this comprehensive evaluation system, typical development children aged 6 to 9 years old (grade 1 to grade 3) (n = 641) were involved in the study of factor analysis. From twelve measuring variables, the exploratory factor analysis extracted five factors (handwriting performance, motor control, speed and automation, halt and exertion, and “in air” events). The test reliability was confirmed by further recruitment of typically developing children (n = 242). The internal consistency mostly demonstrated good to excellent results for every measure. This study further recruited children with handwriting difficulties (n = 33) for testing the discriminative validity of the evaluation system. A series of two-way ANOVA tests was conducted to test the significance of the main effects of the groups (typical development and handwriting deficit) and grades (1, 2, and 3) and their interaction effects on the handwriting measures. All the measures showed significant differences between the two groups, indicating the discriminative validity for identifying handwriting deficits. Seven of twelve measures showed significant interaction effects, indicating the different trends across the grades between the two groups. Typically-developing children demonstrated ongoing progress from grade 1 to grade 3, suggesting a developmental trend during their early school age. Implications for motor development and clinical evaluation are discussed herein in relation to the five dimensions.
... Such signals represent the movement of a digitizing stylus (pen) along both the horizontal and vertical axes, the pressure exerted on the surface of a digitizer, and the tilt and azimuth angles, acquired with respect to a specific series of timestamps to form a collection of time series describing the process of handwriting from beginning to end (referred to as online handwriting). In addition, modern digitizers have the ability to record not only the movement of a pen on the surface of the digitizer but also the movement above the surface (in-air movement; Alonso-Martinez et al., 2017). As shown in a variety of research studies focusing on the identification and assessment of HD in patients suffering from PD, Alzheimer's disease (AD), essential tremor (Drotar et al., 2014(Drotar et al., , 2016Alonso-Martinez et al., 2017;, etc., online handwriting capture provides the ability to characterize the process of handwriting in terms of its kinematic, dynamic, and temporal features, which are not accessible from the final handwritten product when using the conventional pen and paper methodology (referred to as offline handwriting). ...
... In addition, modern digitizers have the ability to record not only the movement of a pen on the surface of the digitizer but also the movement above the surface (in-air movement; Alonso-Martinez et al., 2017). As shown in a variety of research studies focusing on the identification and assessment of HD in patients suffering from PD, Alzheimer's disease (AD), essential tremor (Drotar et al., 2014(Drotar et al., , 2016Alonso-Martinez et al., 2017;, etc., online handwriting capture provides the ability to characterize the process of handwriting in terms of its kinematic, dynamic, and temporal features, which are not accessible from the final handwritten product when using the conventional pen and paper methodology (referred to as offline handwriting). ...
Parkinson's disease dysgraphia (PDYS), one of the earliest signs of Parkinson's disease (PD), has been researched as a promising biomarker of PD and as the target of a noninvasive and inexpensive approach to monitoring the progress of the disease. However, although several approaches to supportive PDYS diagnosis have been proposed (mainly based on handcrafted features (HF) extracted from online handwriting or the utilization of deep neural networks), it remains unclear which approach provides the highest discrimination power and how these approaches can be transferred between different datasets and languages. This study aims to compare classification performance based on two types of features: features automatically extracted by a pretrained convolutional neural network (CNN) and HF designed by human experts. Both approaches are evaluated on a multilingual dataset collected from 143 PD patients and 151 healthy controls in the Czech Republic, United States, Colombia, and Hungary. The subjects performed the spiral drawing task (SDT; a language-independent task) and the sentence writing task (SWT; a language-dependent task). Models based on logistic regression and gradient boosting were trained in several scenarios, specifically single language (SL), leave one language out (LOLO), and all languages combined (ALC). We found that the HF slightly outperformed the CNN-extracted features in all considered evaluation scenarios for the SWT. In detail, the following balanced accuracy (BACC) scores were achieved: SL—0.65 (HF), 0.58 (CNN); LOLO—0.65 (HF), 0.57 (CNN); and ALC—0.69 (HF), 0.66 (CNN). However, in the case of the SDT, features extracted by a CNN provided competitive results: SL—0.66 (HF), 0.62 (CNN); LOLO—0.56 (HF), 0.54 (CNN); and ALC—0.60 (HF), 0.60 (CNN). In summary, regarding the SWT, the HF outperformed the CNN-extracted features over 6% (mean BACC of 0.66 for HF, and 0.60 for CNN). In the case of the SDT, both feature sets provided almost identical classification performance (mean BACC of 0.60 for HF, and 0.58 for CNN).
... This knowledge may be used by artificial intelligence-based systems to replicate the human brain functions. Handwriting movements are considerably more complicated than what has previously been studied [6]. Behavioral characteristics such as honesty, sense, and other emotions are used to discover personality traits. ...
The expertise to quickly and non-invasively determine a subject's emotional state can contribute to a milestone in research on emotionally intelligent computing systems. The ability to identify emotions via everyday activities such as writing and drawing is beneficial to one's well-being. Tablet devices and other human-machine interfaces have made collecting handwriting and drawing samples simpler. To understand more about writing and drawing signals there is a need to investigate them in the temporal, spectral, and cepstral domains for discovering new insights. Extracting more information will help to improve classification accuracy. This study combines temporal, spectral, and Mel Frequency Cepstral Coefficient (MFCC) methods to extract features from such signals, and finds its correlation with depression, anxiety, and stress emotional state of the humans. To examine spatial features, velocities are also computed as variations of displacement in the x-and y-directions while performing the tasks. Bidirectional Long-Short Term Memory (BiLSTM) network is used to classify the generated features' vectors. To evaluate the proposed work, multiple publi-cally available benchmark datasets are utilized. This work determine which activities and features help describe a specific emotional state through in-depth investigation. The results of the experiments demonstrate that fusing several features improve recognition accuracy significantly. For emotions identification like depression, anxiety, and stress states, this work achieved a higher classification improvement ranging from 5.32% to 8.9% as compared to the baseline approaches.
... Time in air: Time in air or time up is the time spent with the pen exerting no pressure. This time is considered at short distance (smaller than 1 cm from the tip of the pen to the surface [35]). This time is zero for those tasks where the whole drawing can be produced in a single stroke, and is large when the drawing requires a large amount of strokes. ...
Practical determination of physical recovery after intense exercise is a challenging topic that must include mechanical aspects as well as cognitive ones because most of physical sport activities, as well as professional activities (including brain computer interface-operated systems), require good shape in both of them. This paper presents a new online handwritten database of 20 healthy subjects. The main goal was to study the influence of several physical exercise stimuli in different handwritten tasks and to evaluate the recovery after strenuous exercise. To this aim, they performed different handwritten tasks before and after physical exercise as well as other measurements such as metabolic and mechanical fatigue assessment. Experimental results showed that although a fast mechanical recovery happens and can be measured by lactate concentrations and mechanical fatigue, this is not the case when cognitive effort is required. Handwriting analysis revealed that statistical differences exist on handwriting performance even after lactate concentration and mechanical assessment recovery. Conclusions: This points out a necessity of more recovering time in sport and professional activities than those measured in classic ways.
... If the distance from the tip of the pen to the paper surface is below one centimeter (depending by the specific acquisition tool) the whole set of information described before is acquired with the unique exception of pressure, which is always zero. A deeper discussion linked with the in-air movement can be found in our previous works [90,2,3]. ...
... The test is used e.g. in the Mini Mental State Examination (MMSE) to assess cognitive impairment [30]. It consists of copying a drawing, which includes two pentagons overlapping into a rhombus (see 3). It is of interest to report that it has been adopted to differentiate dementia associated with Lewy Body (DLB) from Alzheimer's Disease (AD). ...
Background: An advantageous property of behavioural signals ,e.g. handwriting, in contrast to morphological ones, such as iris, fingerprint, hand geometry, etc., is the possibility to ask a user for a very rich amount of different tasks. Methods: This article summarises recent findings and applications of different handwriting and drawing tasks in the field of security and health. More specifically, it is focused on on-line handwriting and hand-based interaction, i.e. signals that utilise a digitizing device (specific devoted or general-purpose tablet/smartphone) during the realization of the tasks. Such devices permit the acquisition of on-surface dynamics as well as in-air movements in time, thus providing complex and richer information when compared to the conventional pen and paper method. Conclusions: Although the scientific literature reports a wide range of tasks and applications, in this paper, we summarize only those providing competitive results (e.g. in terms of discrimination power) and having a significant impact in the field.
... If the distance from the tip of the pen to the paper surface is below one centimetre (depending on the specific acquisition tool) the whole set of information described before is acquired with the unique exception of pressure, which is always zero. A deeper discussion linked with the in-air movement can be found in our previous works [1,3,4]. From a pattern recognition perspective, off-line systems deal with image processing, while on-line ones with timesequence signal processing. ...
Advantageous property of behavioural signals (e.g. handwriting), in contrast to morphological ones (e.g. iris, fingerprint, hand geometry), is the possibility to ask a user to perform many different tasks. This article summarises recent findings and applications of different handwriting/drawing tasks in the field of security and health. More specifically, it is focused on on-line handwriting and hand-based interaction, i.e. signals that utilise a digitizing device (specific devoted or general-purpose tablet/smartphone) during the realization of the tasks. Such devices permit the acquisition of on-surface dynamics as well as in-air movements in time, thus providing complex and richer information when compared to the conventional “pen and paper” method. Although the scientific literature reports a wide range of tasks and applications, in this paper, we summarize only those providing competitive results (e.g. in terms of discrimination power) and having a significant impact in the field.
... Time in air: Time in air or time up is the time spent with the pen exerting no pressure. This time is considered at short distance (smaller than 1 cm from the tip of the pen to the surface [35]). This time is zero for those tasks where the whole drawing can be produced in a single stroke, and is large when the drawing requires a large amount of strokes. ...
Featured Application: Fatigue detection. Abstract: Practical determination of physical recovery after intense exercise is a challenging topic that must include mechanical aspects as well as cognitive ones because most of physical sport activities, as well as professional activities (including brain-computer interface-operated systems), require good shape in both of them. This paper presents a new online handwritten database of 20 healthy subjects. The main goal was to study the influence of several physical exercise stimuli in different handwritten tasks and to evaluate the recovery after strenuous exercise. To this aim, they performed different handwritten tasks before and after physical exercise as well as other measurements such as metabolic and mechanical fatigue assessment. Experimental results showed that although a fast mechanical recovery happens and can be measured by lactate concentrations and mechanical fatigue, this is not the case when cognitive effort is required. Handwriting analysis revealed that statistical differences exist on handwriting performance even after lactate concentration and mechanical assessment recovery. This points out a necessity of more recovering time in sport and professional activities than those measured in classic ways.
... Such a collection of data about handwriting (i. e. that one associated with timestamps) is referred to as online handwriting [29]. Using advanced digital signal processing algorithms a variety of handwriting parameters (commonly referred to as handwriting features) quantifying kinematic (velocity, acceleration, jerk) as well as dynamic (pen pressure, tilt and azimuth) components contributing to the execution of the handwriting process have been designed [6], [30]- [32]. ...
School-aged children spend 31–60%of their time at school performing handwriting, which is a complex perceptual-motor skill composed of a coordinated combination of fine graphomotor movements. As up to 30% of them experience graphomotor difficulties (GD), timely diagnosis of these difficulties and therapeutic intervention are of great importance. At present, an objective, computerized decision support system for the identification and assessment of GD in school-aged children is still missing. In this study, we propose three novel advanced handwriting parametrization techniques based on modulation spectra, fractional order derivatives, and tunable Q-factor wavelet transform to improve the identification of GD using online handwriting. For this purpose, we analyzed signals acquired from 7 basic graphomotor tasks performed by 53 children attending 3rd and 4th grade at several primary schools around the Czech Republic. Combining the newly proposed features with the conventionally used ones, we were able to identify GD with 84% accuracy. In this study, we showed that using advanced parametrization of basic graphomotor movements can be potentially used to improve our capabilities of quantifying problems with the development of legible, fast-paced handwriting, and help with the early diagnosis of handwriting difficulties frequently manifested in developmental dysgraphia.
