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This paper proposes a scheme for predicting ground reaction force (GRF) and center of pressure (CoP) using low-cost FSR sensors. GRF and CoP data are commonly collected from smart insoles to analyze the wearer's gait and diagnose balance issues. This approach can be utilized to improve a user's rehabilitation process and enable customized treatment plans for patients with specific diseases, making it a useful technology in many fields. However, the conventional measuring equipment for directly monitoring GRF and CoP values, such as F-Scan, is expensive, posing a challenge to commercialization in the industry. To solve this problem, this paper proposes a technology to predict relevant indicators using only low-cost Force Sensing Resistor (FSR) sensors instead of expensive equipment. In this study, data were collected from subjects simultaneously wearing a low-cost FSR Sensor and an F-Scan device, and the relationship between the collected data sets was analyzed using supervised learning techniques. Using the proposed technique, an artificial neural network was constructed that can derive a predicted value close to the actual F-Scan values using only the data from the FSR Sensor. In this process, GRF and CoP were calculated using six virtual forces instead of the pressure value of the entire sole. It was verified through various simulations that it is possible to achieve an improved prediction accuracy of more than 30% when using the proposed technique compared to conventional prediction techniques. [ABSTRACT FROM AUTHOR]

Objectives: This study compared the scientific publication speeds of Korean medical journals before and during the coronavirus disease 2019 (COVID-19) era., Methods: We analyzed 2,064 papers from 43 international Korean medical journals, selecting 12 papers annually from 2019 to 2022. We assessed publication speed indicators, including the time from submission to revision and from submission to publication. Additionally, we examined variations in publication speed based on journal and paper characteristics, including whether the studies were related to COVID-19., Results: Among the 43 journals analyzed, 39.5% disclosed the peer review duration from submission to the first decision, and 11.6% reported their acceptance rates. The average time from submission to acceptance was 127.0 days in 2019, 126.1 days in 2020, 124.6 days in 2021, and 126.4 days in 2022. For COVID-19-related studies, the average time from submission to revision was 61.4 days, compared to 105.1 days for non-COVID-19 studies; from submission to acceptance, it was 87.4 days for COVID-19-related studies and 127.1 days for non-COVID-19 studies. All indicators for COVID-19-related studies showed shorter durations than those for non-COVID-19 studies, and the proportion of studies accepted within 30 or 60 days was significantly higher for COVID-19-related studies., Conclusions: This study investigated the publication speed of Korean international medical journals before and during the COVID-19 pandemic. The pandemic influenced journals' review and publication processes, potentially impacting the quality of academic papers. These findings provide insights into publication speeds during the COVID-19 era, suggesting that journals should focus on maintaining the integrity of their publication and review processes.