Latest Issue
Volume 3, Issue 1
January 2024

Digital Technologies Research and Applications (DTRA) is a peer-reviewed open-access journal published two issues a year in English-language, providing researchers, scholars, scientists, and engineers throughout the world with the exchange and dissemination of theoretical and practice-oriented papers dealing with digital technologies and applications.

  • E-ISSN: 2754-5687
  • Frequency: Semiyearly publication
  • Language: English
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Latest Published Articles


Virtual Reality as a Tool for Children with Autism Spectrum Disorder

Children with an autism spectrum disorder tend to lose focus quickly. The possibility of displaying changeable, three-dimensional environments in virtual reality can increase the user's concentration time due to the stimuli used. Through the use of virtual reality, an individual assistant can be provided for the daily care and encouragement of learning for children with autism spectrum disorders. To investigate the feasibility of a virtual assistant and the acceptability of virtual reality among autistic children, a review of the literature and conducted studies is undertaken. The selection, identification and evaluation of the relevant research results is carried out according to the preferred reporting items of the systemic literature search.


Study on the Effect of Three-dimensional Reconstruction Technique Based on Human Gait Plantar Transient Data on Rehabilitation of Patients with Abnormal Foot Arch

This research employs non-contact plantar 3D data scanning and gait analysis methodologies to establish a rehabilitation assistance system tailored for foot arch anomalies. The system utilizes a non-contact plantar 3D data model to mitigate dysfunctions within the plantar skeletal-muscular system. Its objectives include facilitating personalized remote diagnosis of foot arch anomalies, enabling patients to monitor their rehabilitation progress, and supporting at-home rehabilitation efforts. A dataset comprising 124 cases of physiological foot arch anomalies in adults aged 18 and above was collected and analyzed. The findings demonstrate the system’s flexibility, high spatial resolution, personalization, and innovation. Notably, the system achieves real-time measurement of positive pressure and shear force distribution at the plantar interface, facilitates the construction of accurate geometric models, and yields high-quality plantar three-dimensional coordinate data. This research contributes theoretical and technical underpinnings for the application of footwork anomaly diagnosis and correction.

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On the Use of Raman Blood Spectroscopy and Prediction Machines for Enhanced Care of Endometriosis Patients

Endometriosis is a prevalent disease of the female endometrium which affects women of all ethnicities and has been seen to be most common in the 25–35 years age group. The disease does not have a definitive cure, hence care and management are the essential components towards dealing with the disease. At present, the predominant means towards the diagnosis of the presence of the disease involves different imaging modalities alongside laparoscopy, where the instrumentation is expensive to acquire and requires clinical expertise. Recently, work has been done by an author who leveraged Raman blood spectroscopy alongside machine learning towards an affordable high throughput means towards the prediction of endometriosis.

This work utilises the Raman blood spectroscopy dataset alongside advanced signal processing, machine learning and clinical cybernetics, towards the design of a prediction machine which sits within a clinical framework to facilitate Human-Machine interaction for an enhanced care strategy for patients with endometriosis. The prediction machine is designed to initially predict whether a patient has the disease, and is then followed by the use of unsupervised learning to form an inference means towards predicting the extent of the disease. The results showed that a combination of the adopted methods could allow for a high prediction of the endometriosis disease. Subsequent work in this area would now include further optimisation of the prediction machine in order to potentially maximise the prediction accuracy.

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On the Gesture Recognition of a Faint Phantom Motion for the Control of a Transradial Prosthesis amidst varying Contraction Forces

The variation of the contraction force associated with the phantom motion used for the actuation of a bionic upper-limb prosthesis represents a scenario encountered regularly by amputees, while prior research appears to not have been able to succinctly address this problem. In this study, an extended prosthesis control system is proposed which is able to recognise gesture intent motions alongside the prediction of an associated contraction force as part of an advanced pattern recognition system. As part of this research topic, this paper introduces the proposed control architecture and is based on the solving of the gesture recognition problem amidst varying contraction forces for a transradial amputee with a seemingly faint phantom motion.

The work involves the application of a novel decomposition algorithm and the use of a set of computationally effective features, alongside the contrast of the recognition capabilities of the proposed approach using various classification models. The results show an enhanced recognition of gesture motion intent with the use of the decomposition method, despite the faint phantom motion signal from the amputee.


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