Recent advancements in telecommunications and the tactile Internet have paved the way for studying human senses through haptic technology. Haptic technology enables tactile sensations and control using virtual reality (VR) over a network. Researchers are developing various haptic devices to allow for real-time tactile sensation, which can be used in various industries, telesurgery, and other mission-critical operations. One of the main criteria of such devices is extremely low latency, as low as 1 ms. Although researchers are attempting to develop haptic devices with low latency, there remains a need to improve latency and robustness to hand sizes. In this paper, a low-latency haptic open glove (LLHOG) based on a rotary position sensor and min-max scaling (MMS) filter is proposed to realize immersive VR interaction. The proposed device detects finger flexion/extension and adduction/abduction motions using two position sensors located in the metacarpophalangeal (MCP) joint. The sensor data are processed using an MMS filter to enable low latency and ensure high accuracy. Moreover, the MMS filter is used to process object handling control data to enable hand motion-tracking. Its performance is evaluated in terms of accuracy, latency, and robustness to finger length variations. We achieved a very low processing delay of 145.37 s per finger and overall hand motion-tracking latency of 4ms. Moreover, we tested the proposed glove with 10 subjects and achieved an average mean absolute error (MAE) of 3.091∘ for flexion/extension, and 2.068∘ for adduction/abduction. The proposed method is therefore superior to the existing methods in terms of the above factors for immersive VR interaction.

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Relative positioning is one of the important techniques in collaborative robotics, autonomous vehicles, and virtual/augmented reality (VR/AR) applications. Recently, ultra-wideband (UWB) has been utilized to calculate relative position as it does not require a line of sight compared to a camera to calculate the range between two objects with centimeter-level accuracy. However, the single UWB range measurement cannot provide the relative position and attitude of any device in three dimensions (3D) because of lacking bearing information. In this paper, we have proposed a UWB-IMU fusion-based relative position system to provide accurate relative position and attitude between wearable Internet of Things (IoT) devices in 3D. We introduce a distributed Euler angle antenna orientation which can be equipped with the mobile structure to enable relative positioning. Moving average and min-max removing preprocessing filters are introduced to reduce the standard deviation. The standard multilateration method is modified to calculate the relative position between mobile structures. We combine UWB and IMU measurements in a probabilistic framework that enables users to calculate the relative position between two nodes with less error. We have carried out different experiments to illustrate the advantages of fusing IMU and UWB ranges for relative positioning systems. We have achieved a mean accuracy of 0.31 m for 3D relative positioning in indoor line of sight conditions.

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