Jae-Hoon Kim

In this study, we collected various past study results on tools and analytical methods for measuring hand functions of patients with degenerative brain diseases, such as Parkinson’s disease and stroke, and selected and proposed appropriate hand function measurement tools, methods, and analysis software based on text network analysis. We searched the literatures using keywords related to degenerative brain disease and stroke patients for participant types, use of devices and sensors for the intervention types, and hand function assessment for measurement types. Among the 2484 literatures collected, 19 were eventually selected based on certain inclusion and exclusion criteria. As a result of text network analysis, the degree-centrality and the betweenness centrality were the highest in the keyword of Parkinson’s disease for the participant type, force sensor for the intervention type, and finger tapping for the measurement type. Based on these results, pinch gloves comprising an FSR sensor were manufactured, and software and contents were implemented to measure and analyze various quantitative parameter values during finger tapping. The software can evaluate endurance and agility by measuring the finger-tapping intensity and operation time using the index finger and thumb. The contents can evaluate the stability of hand functions by analyzing the coefficient of variation of the tapping interval, the average contact time, and the accuracy of hand functions by analyzing the reaction rate to the presented visual stimulus. As a result of comparing hand functions through 10 types of analysis parameters with a sample of 12 ordinary subjects (8 men and 4 women) using the manufactured pinch gloves, there was a difference between the two genders in the items evaluating muscle strength and agility, and a significant difference in the analysis parameters evaluating stability and accuracy. The results indicate that using the text network analysis-based hand function measurement tool and the method proposed in this study should help derive the objective research results as well as a quantitative comparison of research results of various researchers.

샤인머스캣은 하나의 저울을 이용하여 여러 송이의 중량을 조합하는 방식으로 포장한다. 이는 많은 시간과 비용이 소요되며, 조합된 송이의 총 중량을 최적으로 산출하거나 샤인머스켓의 품질을 결정하는 요소 중 하나인 과방중을 확인하기 어렵다. 따라서 본 논문에서는 작업 시간을 단축하기 위해 12개의 저울로 구성하여 사용자가 설정하는 3가지 요소들의 범위를 기준으로 빠르게 조합을 산출하고 LED와 부저를 사용하여 다감각적인 피드백을 제공하는 조합형 저울을 개발하였다. 그리고 무게로 인한 손실이 최소화되도록 조건을 만족하는 조합들 중에서 중량의 합이 가장 낮은 조합을 추출하는 최적 조합 알고리즘을 적용하였으며, 샤인머스켓을 포장하는 과정에서 얻은 과방중을 데이터베이스에 저장하고 시각화하여 연/월별로 과방중의 분포와 균일성을 확인할 수 있도록 하였다.