Generation and Interpretation of Gestures Data in Input Devices - Admission/Application Essay Example

Generation and Interpretation of Gestures Data in Input DevicesIntroductionTechnological advancement has always been an important facet of computer science and information technology sector. Development in the field of computer sciences has led to the creation of a new era in relation to ‘human computer interface’. In this context, the user interface has enhanced to a large extent on the basis of combination, of procedures and parts based on which users interact with a computer system. Currently, advancement in the sphere of input devices has assisted gamers to remotely interact, with game as well as non-game applications, both physically and cognitively through gyroscopes, accelerometers and sensors.

‘Microsoft Kinect’ is an input device used for motion sensing. The device is developed for Xbox and Windows PCs operating systems. The data format of the device includes facial recognition, 3D motion capture as well as voice recognition. The device is based on open source driver. It assists in developing interoperability for imaging in medical field (Shotton et al., n.d.). ‘Wii remote’ is based motion sensing input devices used for interacting with games. The device supports both video and audio data formats.

Additionally, the sensor works on infrared technology. The device works on Nintendo Entertainment System and it does not support interoperability (Nintendo, 2014). ‘Smartphone accelerometers’ is a sensing system, used for recognition of movement pattern. The system is used in smart phone operating systems and is based on three-axis accelerometer. The system has an effective interoperability as the input device is used in different gaming devices and Global Positioning System (GPS) among others (Bujari et al., n.d.). Input Devices Detects and Generates a Depth MapMicrosoft Kinect uses 30 HZ frame rate per second with 640x480 image size with the assistance of Kinetic camera.

The Kinetic camera is used with depth resolution of certain centimeters. The depth camera aids in capturing an enormous data in relation to motion capture (mocap) for developing depth image of around 500k frames (Shotton et al., n.d.).Wii remote develops depth image with the assistance of sensor bar. The IR LEDs in the sensor bar emits light, which emphasize towards the image sensor to develop, image based on two dots (Nintendo, 2014). Smartphone accelerators detect as well as generate depth image on the basis of data emitted through the sensors (Bujari et al., n.d.). Analyze the Accuracy of the Input Generated by Input DevicesMicrosoft Kinect is identified to process data of around 2 gigabits in a second for analyzing the environment.

Additionally, the system runs on consumer hardware of around 200 frames per second (Shotton et al., n.d.). Wii remote uses the technology of an ADXL330 accelerometer, which ensures that the input data is generated accurately (Nintendo, 2014). Smartphone accelerometers use tri-axial accelerometer to ensure the accuracy of the input data (Bujari et al., n.d.).Microsoft Kinect is identified to process input data more accurately as compared to other input devices owing to the fact that it has high processing speed and imaging capabilities with different resolutions as well as interoperability (Shotton et al., n.d.). The advancement of input devices has raised different security concerns for end users and in this context, the input devices comprising Microsoft Kinect, the Wii remote, or Smartphone accelerometers are required to different software as well as hardware to safeguard designs to avoid misuse.

Additionally, the input devices are required to have, system management software with integrated password for ensuring safety measures (Rego et al., 2013). ConclusionIt can be comprehended from the discussion that technological advancement has led to the development of input devices in computer science and information technology. It has been observed that different input devices that include Microsoft Kinect, the Wii remote, or Smartphone accelerometers are used for motion sensing purposes. The devices have assisted in developing better user and system interaction.

In this regard, adequate measures are required to be adopted with the aim of protecting the misuse of these devices. References Bujari, A., Licar, B., & Palazzi, C. E. (n.d.). Movement pattern recognition through smartphone’s accelerometer. Papers, 1-5. Nintendo. (2014). Wii U technical specs. Retrieved from http://www.nintendo.com/wiiu/features/tech-specs/ Rego, P. A., Moreira, P. M., & Reis, L. P. (2013). Natural and multimodal user interfaces in serious games for health rehabilitation.

Retrieved from http://www.inf.pucrs.br/felipe.meneguzzi/download/AAMAS_14/workshops/AAMAS2014-W26/Rego_et_al.pdf Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., & Blake, A. (n.d.). Real-time human pose recognition in parts from single depth images. Publications, 1-8.