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This paper reviews self-powered medical devices integrated with advanced energy harvesting technologies. This article aims to explain the advantages of integrating self-powered medical devices with advanced energy harvesting technologies, outlining the transformation in healthcare system and patient experience. In today's world, we focus more on consuming energy harnessed from the environment and human body. This approach lowers down our emphasis on conventional power sources like batteries, power packs etc. As a result, the devices used in the medical sector have a longer lifespan, maintain continuous functioning, and improve patient comfort and mobility. Integrating advanced energy harvesting technologies (i.e., piezoelectric, thermal, solar, and electromagnetic) with medical devices plays a pivotal role in revolutionizing the healthcare sector. But there is still some research and development needed to enhance these technologies. This paper will set out by introducing some self-powered medical devices commonly used in healthcare, followed by their advantages, benefits, and challenges that the healthcare practitioners face. This review also discusses their biocompatibility factor which is crucial to use. Then there are examples of a few advanced energy harvesting methods that are being used which include: piezoelectric, solar, thermal, triboelectric and electromagnetic. As we go further, we will come across a table consisting of a comparison between these advanced energy harvesting technologies and their examples in the healthcare sector. The last section is future perspective and the conclusion highlights the transformative potential of this integration, followed by a future recommendation for advancing this field. • Self-powered medical devices and their prevalent uses in healthcare are reviewed. • Comparison of energy harvesting technologies i.e., piezoelectric, solar, thermal, and electromagnetic are presented. • Potential of integrating energy harvesting technologies with medical devices and future recommendations are discussed. [ABSTRACT FROM AUTHOR]

Over the past two decades, an increasing number of large-scale structures have been built around the world. Constructing these structures has been a time consuming and highly expensive process. Thus, providing a structural health monitoring system to guarantee their proper functionality is important. In recent years, the advancement of technology and artificial intelligence methods based on signal processing and machine learning has attracted the attention of researchers. The challenges currently exist in the field of structural health monitoring to identify and classify damages to achieve high accuracy in a health-monitoring program. The presence of noise in measurement, various exciting load types, and varying environmental conditions cause difficulty in the practical identification and classification of damage in structures. Recent studies have employed finite element modeling to test the effectiveness of proposed methods for identifying damages in structures. However, detecting damage in real-world structures as mentioned above, presents unique difficulties, and the effectiveness of the proposed methods for damage detection in real-world structures remains uncertain. In order to improve the performance of damage detection methods and increase the accuracy of these methods as much as possible, the most important action is to identify damage sensitive data in the structure. The next challenge is to choose a high performance algorithm for damage identification and classification. One of the advanced algorithms, which has a very high ability to extract the desired features from the measured data, is the XGBoost algorithm. This algorithm has recently attracted the attention of researchers and has been used in different fields. So far, the ability of this algorithm has not been examined in the field of damage detection in order to extract desirable features. This article deals with the identification, classification, and severity of damages in the SMC benchmark bridge, which is an existing megastructure in the real world, as well as the IASC-ASCE benchmark structure, whose responses were taken under applied loads in the laboratory environment. First, using the XGBoost algorithm, the importance of the features extracted from the sensors' data is evaluated, and then the features, which are effective in the damage detection process, are selected. The results of this algorithm indicate that only by selecting 6 features from a large volume of data, the best performance can be achieved and selecting more does not help increase efficiency. In the next step, the Stacking method, which is a hybrid machine learning algorithm for damage classification, is evaluated and compared with some conventional machine learning algorithms that have been used in previous studies. The Stacking method stands out as the top performer with an average accuracy rate of 93.1%, leading to the conclusion that it is the most effective approach. Finally, by applying the presented algorithm to the two mentioned structures, its validation is appraised. [ABSTRACT FROM AUTHOR]