Abstract
<p class="Abstract">Supervising manual operations performed by workers in industrial environments is crucial in a smart factory. Indeed, the production of products with superior quality, at higher throughput rates and reduced costs with the support of the Industry 4.0 enabling technologies is based on the strict control of all the resources inside the factory, including workers. This paper shows a protocol for validating a new wearable system for tracking finger movements. The wearable system consists of two measuring modules worn on the thumb and on the index measuring flexion and extension of the proximal interphalangeal (PIP) joint by a stretch sensor and rotation of the proximal phalanx (PP) by an inertial measurement unit. An optical system is used to validate the system by capturing specific finger movements. Four movements that simulate typical tasks and gestures, such as grasp and pinch, were performed to validate the proposed system. The maximum root-mean-square error is 3.7 deg for the roll angle of PP. The resistance changes of the stretch sensors with respect to flexion and extension of PIP joint is 0.47 Ohm/deg. The results are useful for the data interpretation when the system is adopted for monitoring finger movements and gestures.</p>
Highlights
In recent years, Industry 4.0 has radically revolutionised manufacturing processes and industrial production [1]
This paper shows a protocol for validating a new wearable system for tracking finger movements
The wearable system consists of two measuring modules worn on the thumb and index finger that measure flexion and extension of the proximal interphalangeal (PIP) joint by a stretch sensor and rotation of the proximal phalanx (PP) by an inertial measurement unit
Summary
Industry 4.0 has radically revolutionised manufacturing processes and industrial production [1]. The rapid increase in the level of automation and the introduction of information and communication technologies into the manufacturing world are enabling more efficient and more flexible production processes that can fabricate higher-quality goods at reduced cost and at high production rates [2]. In this context, industrial settings are transformed into smart factories; information about processes is shared in real time through the Internet of Things and cyber-physical production systems in order to improve efficiency and throughput as well as the quality of the final products [3], [4]. Assembly tasks are based on the repetitive composition of different parts to produce the final product, and the quality of the product is affected by human errors, especially when the manual operations contain many steps
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