Complex technology of virtual reality devices

    **Foreword** In today's consumer virtual reality (VR) devices, there is a lack of somatosensory interaction tools. Except for the HTC Vive, Oculus Rift, and PSVR, these VR devices often cause motion sickness during use because they lack physical interaction. The absence of tactile feedback and real-world interaction limits the immersive experience and can lead to discomfort for users. VR devices that support somatosensory interaction can significantly reduce motion sickness and enhance immersion. These systems allow users to interact more naturally with the virtual environment. Somatosensory devices come in various forms, such as haptic seats, treadmills, haptic suits, spatial positioning systems, and motion capture technologies. There are generally five main principles behind somatosensory interaction devices. Let’s explore them one by one. **1. Laser Positioning Technology** The basic principle involves placing several laser-emitting devices in a space. These lasers scan horizontally and vertically, while multiple laser-sensing receivers on the object being tracked calculate the angles of the beams to determine its 3D coordinates. As the object moves, its position and orientation are continuously updated, enabling accurate motion tracking. Example: HTC Vive's Lighthouse technology. HTC Vive uses lasers and light-sensitive sensors to track the position of moving objects. Two "lighthouses" are placed diagonally in the space, emitting 6 laser beams per second. These beams scan the area in horizontal and vertical directions alternately. The head-mounted display and controllers have up to 70 photo-sensors that detect the laser timing, allowing precise calculation of the device's position and orientation. Advantages: This technology is cost-effective, highly accurate, and not affected by occlusion. It supports multiple targets and offers fast response times. However, it relies on mechanical scanning, which can be unstable over time. If the lighthouses are moved or shaken, the system may lose tracking. Mechanical wear can also lead to malfunctions after prolonged use. **2. Infrared Optical Positioning Technology** This method uses infrared cameras installed throughout the space. The object being tracked has reflective spots that reflect the infrared light from the cameras. By analyzing the reflected signals, the system calculates the object’s spatial coordinates. Example: Oculus Rift's active infrared optical positioning + nine-axis sensor. Unlike passive systems, the Oculus Rift uses active infrared emitters on the headset and controllers. Two cameras with infrared filters capture only the emitted light, and software calculates the position. The system also includes a nine-axis sensor to improve accuracy when the infrared signal is blocked or blurred. Advantages: High precision and low latency. However, the setup is expensive and requires many cameras. The Oculus Rift version simplifies this by using only two cameras and an internal sensor, making it more user-friendly and durable than the HTC Vive’s lighthouse system. However, it has limited movement space and cannot track many objects simultaneously. **3. Visible Light Positioning Technology** Similar to infrared optical positioning, this method uses visible light instead of infrared. Different colored lights are placed on tracked objects, and cameras detect the color spots to identify and track them. Example: PSVR. Sony’s PSVR uses blue light emitted by the headset and controllers. The cameras detect these lights and calculate their positions. This method is cost-effective and easy to implement, offering high sensitivity and stability. However, it is less accurate, prone to occlusion, and sensitive to ambient lighting. The camera’s viewing angle also limits the movement range and the number of objects that can be tracked. **4. Computer Vision Motion Capture Technology** This technique uses multiple high-speed cameras to capture motion from different angles. Software then processes the data to reconstruct the 3D motion of the target. Example: Leap Motion gesture recognition. Leap Motion uses two cameras on the VR headset to capture hand movements using stereo vision. It creates a 3D model of the hands and tracks gestures for natural interaction. This method allows for free movement without wearing any equipment, providing a realistic experience. However, it requires powerful hardware, is sensitive to lighting conditions, and may struggle with fine movements if the camera angle is poor. **5. Inertial Sensor-Based Motion Capture** This technology uses inertial measurement units (IMUs) that include accelerometers, gyroscopes, and magnetometers. These sensors are worn on the body and track motion through data processing. Example: Novint Falcon - Perception Neuron. Perception Neuron is a wearable motion capture system that tracks full-body and finger movements. Each module is small and wireless, offering high precision and flexibility. It is ideal for complex movements but requires the user to wear the sensors, which can be uncomfortable. It is primarily used in professional settings rather than consumer applications. **Summary: In the Future, Computer Vision Will Lead the Way** Each motion capture technology has its own strengths and weaknesses. For example, HTC Vive’s laser system offers high accuracy and wide coverage, but lacks durability. The Oculus Rift’s infrared system improves stability but limits movement space. While inertial-based systems provide excellent performance, they are still mostly used in commercial applications. Currently, laser positioning remains the most practical for consumer-level VR due to its balance of accuracy and range. However, in the future, computer vision motion capture is expected to become the dominant technology. As cameras, software, and computing power advance, it will offer greater freedom and realism, such as in 3D holographic experiences like Microsoft HoloLens. Although still in development, this technology holds great promise for the next generation of VR.

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