Introducing tangible objects into motion controlled gameplay using Microsoft® Kinect ^TM

Abstract

Improvements in ways of game controlling in recent years yielded higher level of interaction. Release of motion controller devices changed conventional ways of controlling games that have been used so far. Microsoft ® Kinect™ (Microsoft Corporation, WA, USA) recognizes motions of the players as game controlling inputs. Although touchless interaction is perceived to be attractive, games mimicking real life activities may benefit from hand-held tangible objects for the player to get more involved into game. In this study, a tangible gameplay interaction method is developed using Microsoft ® Kinect™ that senses hand-held objects with their dimensions and incorporates them into gameplay. Proposed algorithm is implemented on an experimental game, and a user study is performed to measure effects of tangible interaction on Kinect gameplay experience. Results revealed that an improved gameplay with more natural and accurate motion controlling is achieved.

Research on the rehabilitation of children with autism based on speech recognition

“…At present, the more popular acoustic models are hybrid Gaussian-hidden Markov model GMM-HMM, deep neural network-hidden Markov model DNN-HMM, deep recurrent neural network-hidden Markov model RNN-HMM, deep convolutional neural network-hidden Markov model CNN-HMM, but each model has its own irreparable shortcomings, than the GMM-HMM acoustic model, can not use the context before and after the sound information, context relationship to work, DNN-HMM model cannot use existing historical information to identify the current context, RNN-HMM model: when the number of layers increases to a certain number, the recognition efficiency is greatly reduced. The LSTM-DNN acoustic model used in this study shows powerful functions whether it is based on historical information recognition, or recognition efficiency, [11] LSTM and DNN are inseparable, the use of DNN model alone, will lead to insufficient output fluency, and the analysis of the context is not enough, the input frame length of each voice information in DNN is different, which will have a greater impact on the final Chinese speech recognition results, but after adding LSTM, a good recognition rate can be obtained. It is also stable under the influence of environmental factors and noise.…”

Player Enjoyment in Video Games: A Systematic Review and Meta-analysis of the Effects of Game Design Choices

“…Nineteen articles studied the effects of control mode. Most of these articles studied motion-based controllers, including tangible controllers (e.g., Wiimote, PS Move, or steering wheel controllers) or whole-body controllers (e.g., Kinect sensor) (Berglund et al, 2017;Bozgeyikli et al, 2013;Kniestedt et al, 2018;Limperos et al, 2011;McGloin et al, 2011;Peña & Chen, 2017;Schmierbach, Limperos, et al, 2012;Shafer, 2021;Shafer et al, 2011Shafer et al, , 2014Skalski et al, 2011;Smeddinck et al, 2016;Tamborini et al, 2010;Williams, 2014). The other articles studied the effects of the level of responsiveness of the controls (Jörg et al, 2012;Normoyle & Jörg, 2018), controller physical realism (Wechselberger, 2016), left-handed controllers (Maubert Crotte et al, 2019), and use of a paper-based sketching interface (Macret et al, 2012).…”

Auto-Refining 3D Mesh Reconstruction Algorithm From Limited Angle Depth Data

“…However, this may soon change, as modern consumer electronics have started integrating various depth sensors even in mid-range devices. While in the past depth sensors, like Microsoft Kinect [1] and Intel Realsense [2], have been used to solve various depth recognition tasks, consumer adoption outside of entertainment was rare [3], [4], with one of more notable their applications being in health-related fields, such as physiotherapy [5]- [7]. The rapidly evolving field of three-dimensional object reconstruction from a single perspective may benefit by moving away from monocular cameras and instead adopting stereoscopic cameras and other depth-sensing systems [8], [9].…”