出版社：The Japanese Society for Artificial Intelligence
摘要：On-demand, skill-level self-checks are required to establish effective training, and this is the same for metal-filing. However, such a training system has not been established yet for metal-filing because the current skill-level check depends on subjective skill-level estimation by experts. Such subjective skill-level estimation is composed of various complex viewpoints, and its estimation mechanisms cannot be represented in language because they are generated and supported by the experience of experts. That is why sensor-based, on-demand systems cannot faithfully imitate or replace the current skill-level checks of metal-filing. To solve this problem, we analyze the relationships among the subjective skill-level estimation mechanisms of experts and metal-filing mechanics structures. Our analysis yielded three simple viewpoints and related measures: class 2 lever-like movement measure ( L ), upper body rigidity measure ( R ), and pre-acceleration measure ( A ). Surveys of experts also yielded another viewpoint and a related measure: stability measure ( S ). These four measures successfully reproduced the subjective skill-level estimation of experts (adjusted-R2 = 0.90, p < 0.1, N = 10). The coefficients for the measures, which suggest that A is the main factor of the subjective skill-level estimation of experts, also suggest that effective training must emphasize these points in this order: A > L > R . S 's coefficient suggests that the skill-level scores of experts are reduced by 69% when the filings of learners fail. In addition, since these four measures can be calculated with three small wearable hybrid sensors, they can be implemented on scalable wearable sensor-based skill-training systems. In future works, we will implement one such system, integrate it into a skill-training center's teaching plan, and assess how much the system improved the learning speeds of the students.
