Journal Papers

Published: 0 | Under Review/Preprint: 0

Acceptability of a head-mounted assistive mouse controller for people with upper limb disability: an empirical study using the technology acceptance model (2023)

M. R. Kabir ORCID iD, H. Mahmud ORCID iD, M. K. Hasan ORCID iD.

Abstract: Due to limited motor capabilities, people with upper limb disabilities have trouble utilizing a typical mouse while operating a computer. Different wearable Assistive Mouse Controllers (AMCs) have been developed to overcome their challenges. However, these people may not be able to realize the importance, ease of use, and social approval of these AMCs due to their fear of new technology, lack of confidence, and lack of ingenuity. These may negatively affect their attitude and intention toward accepting AMCs for equitable human-computer interaction. This study presents the development of a sensor-based head-mounted AMC, followed by an empirical analysis of its acceptance using the Technology Acceptance Model (TAM) from the socioeconomic perspective of Bangladesh. In a similar vein, we examined the effects of three additional psychological constructs – technology anxiety, confidence, and innovation, on its acceptance along with the original components of the TAM. A total of 150 individuals with stroke-induced upper limb disability participated in an online survey, and their responses were analyzed using confirmatory factor analysis and structural equation modeling following the general least square method. Analysis revealed, about 96.44% of the participants had positive attitude towards the AMC, and almost 88.56% of them had positive intentions to accept it. Furthermore, about 68.61% of them expressed signs of anxiety, 96.35% were confident, and 94.16% of them had an innovative mindset in terms of device usage. The findings imply that individuals with an innovative mentality are more capable of comprehending the practical implications of a new technology than those without one. It is also feasible to reduce technological anxiety and boost a user’s confidence while using an AMC by combining an innovative mentality with straightforward device interaction techniques. Additionally, peer encouragement and motivation can significantly enhance their positive attitude toward accepting an AMC for facilitating their interaction with a computer.

Survival Prediction of Children Undergoing Hematopoietic Stem Cell Transplantation Using Different Machine Learning Classifiers by Performing Chisquared Test and Hyper-parameter Optimization: A Retrospective Analysis (2022)

I. J. Ratul ORCID iD, U. H. Wani ORCID iD, M. M. Nishat ORCID iD, A. A. Monsur ORCID iD, A. M. A. Rafi ORCID iD, F. Faisal ORCID iD, M. R. Kabir ORCID iD.

Abstract: Bone Marrow Transplant, a gradational rescue for a wide range of disorders emanating from the bone marrow, is an efficacious surgical treatment. Several risk factors, such as post-transplant illnesses, new malignancies, and even organ damage, can impair long-term survival. Therefore, technologies like Machine Learning are deployed for investigating the survival prediction of BMT receivers along with the influences that limit their resilience. In this study, an efficient survival classification model is presented in a comprehensive manner, incorporating the Chi-squared feature selection method to address the dimensionality problem and Hyper Parameter Optimization (HPO) to increase accuracy. A synthetic dataset is generated by imputing the missing values, transforming the data using dummy variable encoding, and compressing the dataset from 59 features to the 11 most correlated features using Chi-squared feature selection. The dataset was split into train and test sets at a ratio of 80:20, and the hyperparameters were optimized using Grid Search Cross-Validation. Several supervised ML methods were trained in this regard, like Decision Tree, Random Forest, Logistic Regression, K-Nearest Neighbors, Gradient Boosting Classifier, Ada Boost, and XG Boost. The simulations have been performed for both the default and optimized hyperparameters by using the original and reduced synthetic dataset. After ranking the features using the Chi-squared test, it was observed that the top 11 features with HPO, resulted in the same accuracy of prediction (94.73%) as the entire dataset with default parameters. Moreover, this approach requires less time and resources for predicting the survivability of children undergoing BMT. Hence, the proposed approach may aid in the development of a computer-aided diagnostic system with satisfactory accuracy and minimal computation time by utilizing medical data records.

Two Decades of Bengali Handwritten Digit Recognition: A Survey (2022)

A. B. M. A. Rahman ORCID iD, M. B. Hasan ORCID iD, S. Ahmed ORCID iD, T. Ahmed ORCID iD, M. H. Ashmafee, M. R. Kabir ORCID iD, M. H. Kabir ORCID iD.

Abstract: Handwritten Digit Recognition (HDR) is one of the most challenging tasks in the domain of Optical Character Recognition (OCR). Irrespective of language, there are some inherent challenges of HDR, which mostly arise due to the variations in writing styles across individuals, writing medium and environment, inability to maintain the same strokes while writing any digit repeatedly, etc. In addition to that, the structural complexities of the digits of a particular language may lead to ambiguous scenarios of HDR. Over the years, researchers have developed numerous offline and online HDR pipelines, where different image processing techniques are combined with traditional Machine Learning (ML)-based and/or Deep Learning (DL)-based architectures. Although evidence of extensive review studies on HDR exists in the literature for languages, such as English, Arabic, Indian, Farsi, Chinese, etc., few surveys on Bengali HDR (BHDR) can be found, which lack a comprehensive analysis of the challenges, the underlying recognition process, and possible future directions. In this paper, the characteristics and inherent ambiguities of Bengali handwritten digits along with a comprehensive insight of two decades of state-of-the-art datasets and approaches towards offline BHDR have been analyzed. Furthermore, several real-life application-specific studies, which involve BHDR, have also been discussed in detail. This paper will also serve as a compendium for researchers interested in the science behind offline BHDR, instigating the exploration of newer avenues of relevant research that may further lead to better offline recognition of Bengali handwritten digits in different application areas.

VIS-iTrack: Visual Intention Through Gaze Tracking Using Low-Cost Webcam (2022)

S. A. Sabab, M. R. Kabir ORCID iD, S. R. Hussain, H. Mahmud ORCID iD, H. A. Rubaiyeat, M. K. Hasan ORCID iD.

Abstract: Human intention is an internal, mental characterization for acquiring desired information. From interactive interfaces containing either textual or graphical information, intention to perceive desired information is subjective and strongly connected with eye gaze. In this work, we determine such intention by analyzing real-time eye gaze data with a low-cost regular webcam. We extracted unique features (e.g., Fixation Count, Eye Movement Ratio) from the eye gaze data of 31 participants to generate a dataset containing 124 samples of visual intention for perceiving textual or graphical information, labeled as either TEXT or IMAGE , having 48.39% and 51.61% distribution, respectively. Using this dataset, we analyzed 5 classifiers, including Support Vector Machine (SVM) (Accuracy : 92.19%). Using the trained SVM , we investigated the variation of visual intention among 30 participants, distributed in 3 age groups, and found out that young users were more leaned towards graphical contents whereas older adults felt more interested in textual ones. This finding suggests that real-time eye gaze data can be a potential source of identifying visual intention, analyzing which intention aware interactive interfaces can be designed and developed to facilitate human cognition.

ANTASID: A Novel Temporal Adjustment to Shannon’s Index of Difficulty for Quantifying the Perceived Difficulty of Uncontrolled Pointing Tasks (2022)

M. R. Kabir ORCID iD, M. I. Abedin ORCID iD, R. Ahmed ORCID iD, H. Mahmud ORCID iD, M. K. Hasan ORCID iD.

Abstract: Shannon’s Index of Difficulty (ID), reputable for quantifying the perceived difficulty of pointing tasks as a logarithmic relationship between movement-amplitude (A) and target-width (W), is used for modeling the corresponding observed movement-times (MTo) in such tasks in controlled experimental setup. However, real-life pointing tasks are both spatially and temporally uncontrolled, being influenced by factors, such as – human aspects, subjective behavior, the context of interaction, the inherent speed-accuracy trade-off, where, emphasizing accuracy compromises speed of interaction and vice versa. Effective target-width (We) is considered as spatial adjustment for compensating accuracy. However, no significant adjustment exists in the literature for compensating speed in different contexts of interaction in these tasks. As a result, without any temporal adjustment, the true difficulty of an uncontrolled pointing task may be inaccurately quantified using Shannon’s ID. To verify this, we propose ANTASID (A Novel Temporal Adjustment to Shannon’s ID) formulation with detailed performance analysis. We hypothesized a temporal adjustment factor (t) as a binary logarithm of MTo , compensating for speed due to contextual differences and minimizing the non-linearity between movement-amplitude and target-width . Considering spatial and/or temporal adjustments to ID , we conducted regression analysis using our own and Benchmark datasets in both controlled and uncontrolled scenarios of pointing tasks with a generic mouse. ANTASID formulation showed significantly superior fitness values and throughput in all the scenarios while reducing the standard error. Furthermore, the quantification of ID with ANTASID varied significantly compared to the classical formulations of Shannon’s ID , validating the purpose of this study.

A Case Study on the Independence of Speech Emotion Recognition in Bangla and English Languages using Language-Independent Prosodic Features (2021) (Preprint)

F. Saad ORCID iD, H. Mahmud ORCID iD, M. R. Kabir ORCID iD, M. A. Shaheen, P. Farastu, M. K. Hasan ORCID iD.

Abstract: A language agnostic approach to recognizing emotions from speech remains an incomplete and challenging task. In this paper, we performed a step-by-step comparative analysis of Speech Emotion Recognition (SER) using Bangla and English languages to assess whether distinguishing emotions from speech is independent of language. Six emotions were categorized for this study, such as - happy, angry, neutral, sad, disgust, and fear. We employed three Emotional Speech Sets (ESS), of which the first two were developed by native Bengali speakers in Bangla and English languages separately. The third was a subset of the Toronto Emotional Speech Set (TESS), which was developed by native English speakers from Canada. We carefully selected language-independent prosodic features, adopted a Support Vector Machine (SVM) model, and conducted three experiments to carry out our proposition. In the first experiment, we measured the performance of the three speech sets individually, followed by the second experiment, where different ESS pairs were integrated to analyze the impact on SER. Finally, we measured the recognition rate by training and testing the model with different speech sets in the third experiment. Although this study reveals that SER in Bangla and English languages is mostly language-independent, some disparities were observed while recognizing emotional states like disgust and fear in these two languages. Moreover, our investigations revealed that non-native speakers convey emotions through speech, much like expressing themselves in their native tongue.

Auxilio: A Sensor-Based Wireless Head-Mounted Mouse for People with Upper Limb Disability (2022) (Preprint)

M. R. Kabir ORCID iD, M. I. Abedin ORCID iD, R. Ahmed ORCID iD, S. B. Ashraf , H. Mahmud ORCID iD, M. K. Hasan ORCID iD.

Abstract: Upper limb disability may be caused either due to accidents, neurological disorders, or even birth defects, imposing limitations and restrictions on the interaction with a computer for the concerned individuals using a generic optical mouse. Our work proposes the design and development of a working prototype of a sensor-based wireless head-mounted Assistive Mouse Controller (AMC), Auxilio, facilitating interaction with a computer for people with upper limb disability. Combining commercially available, low-cost motion and infrared sensors, Auxilio solely utilizes head and cheek movements for mouse control. Its performance has been juxtaposed with that of a generic optical mouse in different pointing tasks as well as in typing tasks, using a virtual keyboard. Furthermore, our work also analyzes the usability of Auxilio, featuring the System Usability Scale. The results of different experiments reveal the practicality and effectiveness of Auxilio as a head-mounted AMC for empowering the upper limb disabled community.

Renovo: A Sensor-Based Therapeutic System for Brachial Monoplegia (2021) (Preprint)

M. R. Kabir ORCID iD, M. A. Jawad ORCID iD, M. Ehsan ORCID iD, H. Mahmud ORCID iD, M. K. Hasan ORCID iD.

Abstract: Stroke patients with Upper Limb Disability (ULD) are re-acclimated to their lost motor capability through therapeutic interventions, following assessment by Physiotherapists (PTs) using various qualitative assessment protocols. However, the assessments are often biased and prone to errors. Real-time visualization and quantitative analysis of various Performance Metrics (PMs) of patient's motion data, such as - Range of Motion (RoM), Repetition Rate (RR), Velocity (V), etc., may be vital for proper assessment. In this study, we present Renovo, a wearable inertial sensor-based therapeutic system, which assists PTs with real-time visualization and quantitative patient assessment, while providing patients with progress feedback. We showcase the results of a three-week pilot study on the rehabilitation of ULD patients (N=16), in 3 successive sessions at one-week interval, following evaluation both by Renovo and PTs (N=5). Results suggest that sensor-based quantitative assessment reduces the possibility of human error and bias, enhancing efficiency of rehabilitation.