Abstract

The development of techniques for monitoring finger movement is becoming increasingly important in areas, such as robotics, virtual reality, and rehabilitation. To date, various techniques have been proposed for tracking hand movements, but the majority suffer from poor accuracy and repeatability. Inspired by the articulated structure of finger joints, we propose a novel 3-D printed optical sensor with a compact hinged configuration for tracking finger flexion. This sensor exploits Malus’ law using the attenuation of light transmitted through crossed polarizers. The sensor consists of a single LED, two pieces of linear polarizing film, and a photodetector that detects the changes in polarized light intensity proportional to the angle of finger flexion. This paper presents the characterization of the proposed optical sensor and compares it with a commonly used commercial bend sensor. Results show that the bend sensor exhibits hysteresis error, low sensitivity at small angles, and significant temporal drift. In contrast, the optical sensor is more accurate (±0.5°) in the measuring range from 0° to 90°, and exhibits high repeatability and stability, as well as a fast dynamic response. Overall, the optical sensor outperforms the commercial bend sensor, and shows excellent potential for monitoring hand movements in real time.

Highlights

  • M ONITORING hand movement is an important requirement in areas such as robotics, physical rehabilitation and therapy, virtual reality, and sign language recognition, to name a few examples [1]

  • We present the characteristics of our optical sensor and compare its performance with a WANG et al.: DESIGN AND EVALUATION OF A 3-D PRINTED OPTICAL SENSOR FOR MONITORING FINGER FLEXION

  • The designed optical sensor is more accurate and repeatable in comparison with other sensors reported by several other groups

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Summary

Introduction

M ONITORING hand movement is an important requirement in areas such as robotics, physical rehabilitation and therapy, virtual reality, and sign language recognition, to name a few examples [1]. Since the 1970s, considerable attention has been focused on researching methods for tracking hand movement [1], [2]. Current hand tracking devices can be categorized into two main types: camera-based systems and glove-based systems. Camera systems detect either the hand contour [2] or small markers including retro-reflective spheres [3] and LEDs [4] attached to the finger segments. The major limitation of such systems is that the measurement can only be performed in a restricted range determined by the position of the cameras

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