Open Research
Permanent link for this community
About
Tuwhera Open Access Research Outputs provides free access to full texts of scholarly works from AUT's Schools, Research Institutes and Centres.
AUT's research is built on a foundation of innovation and excellence, with the aim that its discoveries and applications are shared in ways that enhance wellbeing and prosperity.
Adding your outputs
AUT staff research outputs are added to this collection via Research Elements. All items submitted to this collection are checked to ensure material does not breach publisher copyright and is suitable for archiving prior to being made open access.
Find out more about making your work open access
For help with Research Elements contact the Research and Innovation Office.
Browse
Browsing Open Research by Subject "0903 Biomedical Engineering"
Now showing 1 - 5 of 5
Results Per Page
Sort Options
- Item4D Printing in Biomedical Engineering: Advancements, Challenges, and Future Directions(MDPI AG, 2023-06-29) Ramezani, Maziar; Mohd Ripin, Zaidi4D printing has emerged as a transformative technology in the field of biomedical engineering, offering the potential for dynamic, stimuli-responsive structures with applications in tissue engineering, drug delivery, medical devices, and diagnostics. This review paper provides a comprehensive analysis of the advancements, challenges, and future directions of 4D printing in biomedical engineering. We discuss the development of smart materials, including stimuli-responsive polymers, shape-memory materials, and bio-inks, as well as the various fabrication techniques employed, such as direct-write assembly, stereolithography, and multi-material jetting. Despite the promising advances, several challenges persist, including material limitations related to biocompatibility, mechanical properties, and degradation rates; fabrication complexities arising from the integration of multiple materials, resolution and accuracy, and scalability; and regulatory and ethical considerations surrounding safety and efficacy. As we explore the future directions for 4D printing, we emphasise the need for material innovations, fabrication advancements, and emerging applications such as personalised medicine, nanomedicine, and bioelectronic devices. Interdisciplinary research and collaboration between material science, biology, engineering, regulatory agencies, and industry are essential for overcoming challenges and realising the full potential of 4D printing in the biomedical engineering landscape.
- ItemChanges in Functional Outcomes in People with High-Energy Ankle Trauma After the Use of the ReAktiv Posterior Dynamic Element™ Orthosis and a Rehabilitation Program: A Case Series(Lippincott, Williams & Wilkins, 2023-09-14) Gardner, Sarah; Frecklington, Mike; Rose, Kirsten; Carroll, MatthewThe aim of this study was to examine lower-limb function in 2 patients that received a ReAktiv Posterior Dynamic Element™ (PDE) orthosis and 6-week rehabilitation program after a high-energy trauma injury to the lower limb. Lower-limb function was assessed using the lower extremity functional score, walking performance through the 2-minute walk test, and dynamic mobility and balance through the single-leg balance, timed stair ascent, and the 4-square step test. A 6-week physiotherapy-led rehabilitation program was also implemented. Data showed improvements in lower extremity function, walking performance, mobility, and balance measures after 8 weeks of wearing the ReAktiv PDE™ orthosis and completion of the rehabilitation program. The ReAktiv PDE™ orthosis combined with a lower-limb rehabilitation program shows potential as a treatment option to improve lower-limb function and walking performance and return sufferers of high-energy trauma injury to functional levels seen in healthy cohorts.
- ItemDesign of a Compact Energy Storage with Rotary Series Elastic Actuator for Lumbar Support Exoskeleton(MDPI AG, ) Al Dahiree, OS; Ghazilla, RAR; Tokhi, MO; Yap, HJ; Albaadani, EALumbar support exoskeletons with active and passive actuators are currently the cutting-edge technology for preventing back injuries in workers while lifting heavy objects. However, many challenges still exist in both types of exoskeletons, including rigid actuators, risks of human–robot interaction, high battery consumption, bulky design, and limited assistance. In this paper, the design of a compact, lightweight energy storage device combined with a rotary series elastic actuator (ES-RSEA) is proposed for use in a lumbar support exoskeleton to increase the level of assistance and exploit the human bioenergy during the two stages of the lifting task. The energy storage device takes the responsibility to store and release passive mechanical energy while RSEA provides excellent compliance and prevents injury from the human body’s undesired movement. The experimental tests on the spiral spring show excellent linear characteristics (above 99%) with an actual spring stiffness of 9.96 Nm/rad. The results demonstrate that ES-RSEA can provide maximum torque assistance in the ascent phase with 66.6 Nm while generating nearly 21 Nm of spring torque during descent without turning on the DC motor. Ultimately, the proposed design can maximize the energy storage of human energy, exploit the biomechanics of lifting tasks, and reduce the burden on human effort to perform lifting tasks.
- ItemEnhancing Aotearoa, New Zealand’s Free Healthline Service through Image Upload Technology(Hindawi Limited, 2024-02-02) Wilson, Miriama K; Pienaar, Fiona; Large, Ruth; Wright, Matt; Todd, Verity FBackground. Healthline is one of the 39 free telehealth services that Whakarongorau Aotearoa/New Zealand Telehealth Services provides to New Zealanders. In early 2021, an image upload system for viewing service user-uploaded images was implemented into the Healthline service. Aims. The aim of this research was to understand the utilisation of Healthline’s image upload system by clinicians and service users in New Zealand. Methods. This is a retrospective observational study analysing Healthline image upload data over a two-year period: March 2021 through to December 2022. A total of 40,045 images were analysed, including demographics of the service users who uploaded an image: ethnicity, age group, and area of residence. The outcome or recommendation of the Healthline call was also assessed based on whether an image was included. Results. Images uploaded accounted for 6.0% of total Healthline calls (n=671,564). This research found that more service users were advised to go to an Emergency Department if they did not upload an image compared to service users who used the tool (13.5% vs. 7.7%), whereas a higher proportion of service users were given a lower acuity outcome if they included an image, including visiting an Urgent Care (24.0% vs. 16.9%) and GP (36.7% vs. 24.3%). Conclusion. Service users who did not upload an image had a higher proportion of Emergency Department outcomes than service users who did use the tool. This image upload tool has shown the potential to decrease stress on Emergency Departments around Aotearoa, New Zealand, through increased lower acuity outcomes.
- ItemUsing Deep Learning with Bayesian–Gaussian Inspired Convolutional Neural Architectural Search for Cancer Recognition and Classification from Histopathological Image Frames(Hindawi Limited, 2023-02-09) Stephen, Okeke; Sain, MangalWe propose a neural architectural search model which examines histopathological images to detect the presence of cancer in both lung and colon tissues. In recent times, deep artificial neural networks have made tremendous impacts in healthcare. However, obtaining an optimal artificial neural network model that could yield excellent performance during training, evaluation, and inferencing has been a bottleneck for researchers. Our method uses a Bayesian convolutional neural architectural search algorithm in collaboration with Gaussian processes to provide an efficient neural network architecture for efficient colon and lung cancer classification and recognition. The proposed model learns by using the Gaussian process to estimate the required optimal architectural values by choosing a set of model parameters through the exploitation of the expected improvement (EI) values, thereby minimizing the number of sampled trials and suggesting the best model architecture. Several experiments were conducted, and a landmark performance was obtained in both validation and test data through the evaluation of the proposed model on a dataset consisting of 25,000 images of five different classes with convergence and F1-score matrices.