Stand-Up Assistive Devices Allowing Patients to Perform Voluntary Movements Within the Safety Movement Tolerance

Chugo, Daisuke; Muramatsu, Satoshi; Yokota, Sho; She, Jinhua; Hashimoto, Hiroshi

doi:10.37965/jait.2022.0121

Cited by 5 publications

(2 citation statements)

References 10 publications

(24 reference statements)

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“…Machine learning (ML) has expanded its usability in the fields of image recognition [115], structural biology [116], cosmology [117], and healthcare [118,119]. Along with the direct application, ML is also used in improving the force field [120,121] and time economic prediction of MD simulations [122][123][124][125].…”

Section: Miscellaneous Workmentioning

confidence: 99%

A Review of Nano and Microscale Heat Transfer: An Experimental and Molecular Dynamics Perspective

Chatterjee,

Paras,

et al. 2023

Processes

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Significant progress in the development of micro and nanoscale devices has been observed for the past three decades. The thermal transportation in these small-length scales varies significantly, and it is difficult to explain the underlying physics using the pre-existing theoretical formulations. When the bulk dimension of a system is comparable to or smaller than the mean free path (MFP) of the thermal carriers, classical theories, such as Fourier’s Law of heat conduction, are unable to accurately explain the system energy dynamics. The phenomena of energy transit and conversion at the micro to nanoscale is an interesting topic of research due to the substantial changes in behavior that are documented when compared to those at the macro size. This review article is broadly divided into two parts. Initially, the recent development in the field of molecular dynamic (MD) simulations is emphasized. Classical MD simulation is such a powerful tool that provides insight into the length scales where the conventional continuum approaches cease to be valid. Several examples of recent developments in the applicability of MD simulations for micro and nanoscale thermal transportation are reviewed. However, there are certain limitations of the MD simulations where the results deviate from experimental validation due to the lack of knowledge of the appropriate force fields. Hence the experimental development of micro and nanoscale thermal transportation processes is briefly reviewed and discussed in the other section of this review article.

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Section: Miscellaneous Workmentioning

confidence: 99%

A Review of Nano and Microscale Heat Transfer: An Experimental and Molecular Dynamics Perspective

Chatterjee,

Paras,

et al. 2023

Processes

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“…(v) Patient and elderly cares are an important domain of artificial intelligence and robotics. The fifth paper in this issue presents a novel standing assistance robot, for assisting the patient to stand up safely while using their physical abilities, which realizes and takes advantage of the voluntary movements of the patient within a safety motion range [7].…”

Section: Preview Of the Studies In This Issuementioning

confidence: 99%

Latest Advances on AI and Robotics Applications

Silva

2022

JAIT

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The applications of artificial intelligence and robotics, their associated technologies, and their integration continue to grow and are seen in more and more diverse fields. With these considerations in mind, this issue of the Journal of Artificial Intelligence and Technology presents five state-of-the-art peer-reviewed contributions, ranging from theory to prototyping, and covering several distinct application topics.

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Machine learning deployment for energy monitoring of Internet of Things nodes in smart agriculture

John,

Sarkar,

Davis

2024

Int J Communication

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SummaryLow‐Power Wide‐Area Network technologies, such as LoRa, are gaining popularity in the agricultural sector for field deployment. The crucial factors in these devices are their range and power efficiency. The energy consumption of a LoRa wireless sensor network is predominantly affected by transmission parameters like carrier frequency, bandwidth, transmit power, spreading factor, and coding rate. Incorrect chosen transmission parameters can lead to a reduction in the battery life of end nodes, requiring frequent battery replacements—a situation undesirable for field deployment. This study introduces a machine learning deployment in the form of a web application designed to monitor the energy consumption of end nodes in LoRa wireless sensor networks. The research initially employs 12 regression models, including Linear, Random Forest, K‐Nearest Neighbours, Decision Tree, Support Vector, Lasso, Ridge, AdaBoost, Gradient Boost, XGBoost, CatBoost, and LightGBM models. The findings of the study reveal that the LightGBM model surpasses other models in accurately predicting the energy consumption of Internet of Things (IoT) nodes, leading to its selection for the web application. This machine learning web application can be implemented in a programmable Long Range Wide Area Network (LoRaWAN) gateway to effectively monitor the energy consumption of IoT end nodes in the agricultural sector.

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Stand-Up Assistive Devices Allowing Patients to Perform Voluntary Movements Within the Safety Movement Tolerance

Cited by 5 publications

References 10 publications

A Review of Nano and Microscale Heat Transfer: An Experimental and Molecular Dynamics Perspective

A Review of Nano and Microscale Heat Transfer: An Experimental and Molecular Dynamics Perspective

Latest Advances on AI and Robotics Applications

Machine learning deployment for energy monitoring of Internet of Things nodes in smart agriculture

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