Parkinson’s disease (PD) is a common neurodegenerative disease, one of the symptoms of which is a gait disorder, which decreases gait speed and cadence. Recently, augmented feedback training has been considered to achieve effective physical rehabilitation. Therefore, we have devised a numerical modeling process and algorithm for gait detection and classification (GDC) that actively utilizes augmented feedback training. The numerical model converted each joint angle into a magnitude of acceleration (MoA) and a Z-axis angular velocity (ZAV) parameter. Subsequently, we confirmed the validity of both the GDC numerical modeling and algorithm. As a result, a higher gait detection and classification rate (GDCR) could be observed at a higher gait speed and lower acceleration threshold (AT) and gyroscopic threshold (GT). However, the pattern of the GDCR was ambiguous if the patient was affected by a gait disorder compared to a normal user. To utilize the relationships between the GDCR, AT, GT, and gait speed, we controlled the GDCR by using AT and GT as inputs, which we found to be a reasonable methodology. Moreover, the GDC algorithm could distinguish between normal people and people who suffered from gait disorders. Consequently, the GDC method could be used for rehabilitation and gait evaluation.

A force plate is mainly used in biomechanics; it aims to measure the ground reaction force in a person’s walking or standing position. In this study, a large-area force mat of the piezoresistance sensing type was developed, and a deep-learning-based weight measurement calibration method was applied to solve the problem in which measurements are not normalized because of physical limitations in hardware and signal processing. The test set was composed of the values measured at each point by weight and the value of the center of the pressure variable, and the measured value was predicted using a deep neural network (DNN) regression model. The calibration verification results show that the average weight errors range from a minimum of 0.06% to a maximum of 3.334%. This is simpler than the previous method, which directly measures the ratio of the resistance value to the measured weight of each sensor and derives an equation.

화상에 의해 피해를 받은 신체 세포조직은 심각한 2차 감염의 가능성에 노출되어 있다. 이러한 화상을 입은 환자들을 위해서 환부의 감염 상태와 약물의 투여 또는 방출 상태를 체크할 수 있는 기능성 실의 필요성이 대두되고 있다. 이러한 실 제조를 위해 전기방사 기술을 사용하여 미세한 섬유를 제작하였다. 잉크를 이용하여 약물이 실에 잘 적층될 지, 전기방사 최적 변수에 대해서, 또한 이번 연구에서 사용할 약물의 성능 실험을 통해 기능성 실을 만들 수 있는지 확인하였다. 결과물인 다공성 원단으로 약물 방출과 감염 상태를 체크할 수 있는 다양한 크기를 가진 원단을 제작할 수 있다. 또한, 이 연구를 통해 화상뿐 아니라 다양한 분야에서 사용 가능한 기능성 실을 개발할 수 있음을 확인할 수 있다.

Cueing and feedback training can be effective in maintaining or improving gait in individuals with Parkinson’s disease. We previously designed a rehabilitation assist device that can detect and classify a user’s gait at only the swing phase of the gait cycle, for the ease of data processing. In this study, we analyzed the impact of various factors in a gait detection algorithm on the gait detection and classification rate (GDCR). We collected acceleration and angular velocity data from 25 participants (1 male and 24 females with an average age of 62 ± 6 years) using our device and analyzed the data using statistical methods. Based on these results, we developed an adaptive GDCR control algorithm using several equations and functions. We tested the algorithm under various virtual exercise scenarios using two control methods, based on acceleration and angular velocity, and found that the acceleration threshold was more effective in controlling the GDCR (average Spearman correlation −0.9996, p < 0.001) than the gyroscopic threshold. Our adaptive control algorithm was more effective in maintaining the target GDCR than the other algorithms (p < 0.001) with an average error of 0.10, while other tested methods showed average errors of 0.16 and 0.28. This algorithm has good scalability and can be adapted for future gait detection and classification applications.