In this paper, we discuss the development of cost effective, wireless, and wearable vibrotactile haptic device for stiffness perception during an interaction with virtual objects. Our experimental setup consists of haptic device with five vibrotactile actuators, virtual reality environment tailored in Unity 3D integrating the Oculus Rift Head Mounted Display (HMD) and the Leap Motion controller. The virtual environment is able to capture touch inputs from users. Interaction forces are then rendered at 500 Hz and fed back to the wearable setup stimulating fingertips with ERM vibrotactile actuators. Amplitude and frequency of vibrations are modulated proportionally to the interaction force to simulate the stiffness of a virtual object. A quantitative and qualitative study is done to compare the discrimination of stiffness on virtual linear spring in three sensory modalities: visual only feedback, tactile only feedback, and their combination. A common psychophysics method called the Two Alternative Forced Choice (2AFC) approach is used for quantitative analysis using Just Noticeable Difference (JND) and Weber Fractions (WF). According to the psychometric experiment result, average Weber fraction values of 0.39 for visual only feedback was improved to 0.25 by adding the tactile feedback.
Abstract:In this paper, a low cost, wearable six Degree of Freedom (6-DOF) hand pose tracking system is proposed for Virtual Reality applications. It is designed for use with an integrated hand exoskeleton system for kinesthetic haptic feedback. The tracking system consists of an Infrared (IR) based optical tracker with low cost mono-camera and inertial and magnetic measurement unit. Image processing is done on LabVIEW software to extract the 3-DOF position from two IR targets and Magdwick filter has been implemented on Mbed LPC1768 board to obtain orientation data. Six DOF hand tracking outputs filtered and synchronized on LabVIEW software are then sent to the Unity Virtual environment via User Datagram Protocol (UDP) stream. Experimental results show that this low cost and compact system has a comparable performance of minimal Jitter with position and orientation Root Mean Square Error (RMSE) of less than 0.2 mm and 0.15 degrees, respectively. Total Latency of the system is also less than 40 ms.
Abstract-A novel optical-based fingertip force sensor, which is integrated into a bio-mimetic finger for robotic and prosthetic manipulation is presented. This is used to obtain tactile information during grasping and manipulation of objects.Unlike most devices the proposed force sensor is free of any electrical and metal components and as such is immune to electromagnetic fields. The sensor is simple and very compact, has extremely low power consumption and noise levels and requires no additional hardware. It is based on a cantilever design combined with fiber optics and is integrated into the distal phalanges of a robotic finger.The unique design of the sensor makes it ideally suited for use in messy or harsh environments that may be prone to electromagnetic fields, granular or liquid intrusion, may include combustible gasses or be subject to radiation
Wrist-worn gesture sensing systems can be used as a seamless interface for AR/VR interactions and control of various devices. In this paper, we present a low-cost gesture sensing system that utilizes near Infrared Emitters (600 -1100 nm) and Photo-Receivers encompassing the wrist to infer hand gestures. The proposed system consists of a wristband comprising Infrared emitters and receivers, data acquisition hardware, data post-processing software, and gesture classification algorithms. During the data acquisition process, 24 near Infrared Emitters are sequentially switched on around the wrist, and twelve Photo-diodes measure the light reflected, refracted, and scattered by the tissues inside the wrist. The acquired data corresponding to different gestures are labeled and input into a machine learning algorithm for gesture classification. To demonstrated the accuracy and speed of the proposed system, real-time gesture sensing user studies were conducted. As a result of this comparison, we obtained an average accuracy of 98.06% with standard deviation of 1.82%. In addition, we evaluated that the system can perform six-eight gestures per second in real time using a desktop computer operating with Core i7-7800X CPU at 3.5GHz and 32 GB RAM.
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