An Approach to the Definition of the Aerodynamic Comfort of Motorcycle Helmets

Scappaticci, Lorenzo; Risitano, Giacomo; Santonocito, Dario; D’Andrea, Danilo; Milone, Dario

doi:10.3390/vehicles3030033

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…The intersecting domains of bioengineering and artificial intelligence have forged new avenues in various applications, most notably in biomechanical analyses and medical applications, especially for what concerns orthopaedic diseases and neurological conditions [19][20][21][22][23]. This burgeoning field focuses on advanced motion-tracking technologies that leverage neural networks, video analysis, and other computational approaches, including sensor technologies.…”

Section: Related Workmentioning

confidence: 99%

MocapMe: DeepLabCut-Enhanced Neural Network for Enhanced Markerless Stability in Sit-to-Stand Motion Capture

Milone,

Longo,

Merlino

et al. 2024

Sensors

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This study examined the efficacy of an optimized DeepLabCut (DLC) model in motion capture, with a particular focus on the sit-to-stand (STS) movement, which is crucial for assessing the functional capacity in elderly and postoperative patients. This research uniquely compared the performance of this optimized DLC model, which was trained using ’filtered’ estimates from the widely used OpenPose (OP) model, thereby emphasizing computational effectiveness, motion-tracking precision, and enhanced stability in data capture. Utilizing a combination of smartphone-captured videos and specifically curated datasets, our methodological approach included data preparation, keypoint annotation, and extensive model training, with an emphasis on the flow of the optimized model. The findings demonstrate the superiority of the optimized DLC model in various aspects. It exhibited not only higher computational efficiency, with reduced processing times, but also greater precision and consistency in motion tracking thanks to the stability brought about by the meticulous selection of the OP data. This precision is vital for developing accurate biomechanical models for clinical interventions. Moreover, this study revealed that the optimized DLC maintained higher average confidence levels across datasets, indicating more reliable and accurate detection capabilities compared with standalone OP. The clinical relevance of these findings is profound. The optimized DLC model’s efficiency and enhanced point estimation stability make it an invaluable tool in rehabilitation monitoring and patient assessments, potentially streamlining clinical workflows. This study suggests future research directions, including integrating the optimized DLC model with virtual reality environments for enhanced patient engagement and leveraging its improved data quality for predictive analytics in healthcare. Overall, the optimized DLC model emerged as a transformative tool for biomechanical analysis and physical rehabilitation, promising to enhance the quality of patient care and healthcare delivery efficiency.

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Section: Related Workmentioning

confidence: 99%

MocapMe: DeepLabCut-Enhanced Neural Network for Enhanced Markerless Stability in Sit-to-Stand Motion Capture

Milone,

Longo,

Merlino

et al. 2024

Sensors

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“…Three-dimensional printing has made it possible to create customized prostheses based on the individual needs of patients using high-performance materials such as 316L stainless steel [5]. However, wear caused by regular activities such as walking, sitting, or running can lead to deterioration of the materials used in the joint [6][7][8]. To overcome this problem, researchers are increasingly turning to more advanced materials [9][10][11] and numerical techniques, such as the finite element method (FEM), to improve the functionality and lifespan of prostheses [12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Smart Design of Hip Replacement Prostheses Using Additive Manufacturing and Machine Learning Techniques

Milone,

D’Andrea,

Santonocito

2023

Prosthesis

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The field of additive manufacturing, particularly 3D printing, has ushered in a significant transformation in the realm of joint arthritis treatment through prosthetic surgery. This innovative technology allows for the creation of bespoke prosthetic devices that are tailored to meet the specific needs of individual patients. These devices are constructed using high-performance materials, including titanium and cobalt-chrome alloys. Nevertheless, the routine physical activities of patients, such as walking, sitting, and running, can induce wear and tear on the materials comprising these prosthetic devices, subsequently diminishing their functionality and durability. In response to this challenge, this research has endeavored to leverage novel techniques. The primary focus of this study lies in the development of an algorithm designed to optimize hip replacement procedures via the mechanical design of the prosthesis. This optimization process exploits the capabilities of machine learning algorithms, multi-body dynamics, and finite element method (FEM) simulations. The paramount innovation in this methodology is the capacity to design a prosthetic system that intricately adapts to the distinctive characteristics of each patient (weight, height, gait cycle). The primary objective of this research is to enhance the performance and longevity of prosthetic devices by improving their fatigue strength. The evaluation of load distribution on the prosthetic device, facilitated by FEM simulations, anticipates a substantial augmentation in the useful life of the prosthetic system. This research holds promise as a notable advancement in prosthetic technology, offering a more efficacious treatment option for patients suffering from joint arthritis. The aim of this research is to make meaningful contributions to the enhancement of patient quality of life and the long-term performance of prosthetic devices.

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“…3D printing has made it possible to obtain customized prostheses based [1] on the patient needs, using high-performance materials such as 316L stainless [2]steel or Ti-6Al-4V. However, wear caused by regular gait activities such as walking, sitting, or running, leads to the deterioration of the material used in the joint [3][4][5]. The use of traditional materials has gradually been replaced with more performing ones [6][7][8] which have made it possible to obtain customized devices based on patient needs and, therefore, more effective.…”

Section: Introductionmentioning

confidence: 99%

Smart design of customized hip prostheses in additive manufacturing by combining numerical and experimental methodologies

Milone

Marchis

Longo

et al. 2023

IOP Conf. Ser.: Mater. Sci. Eng.

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Thanks to the development of additive manufacturing techniques, prosthetic surgery has reached increasingly advanced levels, revolutionizing the clinical course of patients with joint arthritis. 3D printing has made it possible to obtain customized prostheses based on patient needs, using high-performance materials. However, wear caused by regular gait activities such as walking, sitting, or running, leads to the deterioration of the material used in the joint. Thus, the use of traditional materials has gradually been replaced with more performing ones which have made it possible to obtain customized devices based on patient needs and, therefore, more effective. Numerical techniques have recently been adopted, such as the Finite Element Method (FEM), to support the experimentation, allowing the calculation of the useful life and the optimization of the prostheses’ functionality to accurately evaluate the distribution of the load on the prosthesis. The present work aims to develop an algorithm that optimizes hip replacement mechanically using a machine learning algorithm coupled with multi-body and finite element model simulations.

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An Approach to the Definition of the Aerodynamic Comfort of Motorcycle Helmets

Cited by 3 publications

References 24 publications

MocapMe: DeepLabCut-Enhanced Neural Network for Enhanced Markerless Stability in Sit-to-Stand Motion Capture

MocapMe: DeepLabCut-Enhanced Neural Network for Enhanced Markerless Stability in Sit-to-Stand Motion Capture

Smart Design of Hip Replacement Prostheses Using Additive Manufacturing and Machine Learning Techniques

Smart design of customized hip prostheses in additive manufacturing by combining numerical and experimental methodologies

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