人體區域慣性感測網路之多螢幕計算物理電子遊戲與重力感測問題

Abstract

近年來越來越多人關注如何透過人體區域慣性感測器來感測人體的動作，並將其應用於計算物理電子遊戲中。當計算物理電子遊戲越來越普及後，探討如何增加與改善計算物理電子遊戲的物理輸入（例如：慣性感測器）已經成為一個趨勢。基於這樣的趨勢，我們開發一個部屬於人體上之人體區域慣性感測網路來感測人體的姿勢及多螢幕的計算物理電子遊戲，讓玩家可以更真實的體驗遊戲。我們設計一套多螢幕遊戲引擎。其中包含人體區域慣性感測網路平台，計算物理遊戲控制器及一組遊戲引擎。我們的遊戲可以讓玩家與虛擬物件互動。 人體追蹤系統是基於加速度計。而其中一個基本的議題為，不管人體是否在移動，重力加速度的方向是否可以準確地得到。假設一個人體的剛體上部屬多個加速度感測器，近期的研究透過資料融合的法來預測該剛體上的重力加速度方向。然而，如何將加速度感測器部屬在最佳的位置來減少預測誤差仍然是一個開放問題。在這篇論文中，我們將部屬最佳化的問題公式化，展示部屬方式的方針及兩種部署策略virtual-force-based (VF-based) method 及 Metropolis-based。

Deploying body-area inertial sensor networks on human bodies to capture motions has attracted a lot of interests recently, especially in cyber-physical video games and context aware applications. While video games on the cyber world have been quite popular, enhancing them with more physical inputs, such as those from inertial sensors, is becoming a new trend. Following this trend, we develop a video game integrated with body-area inertial sensor networks deployed on players as inputs and with multiple game screens to broaden players’ views and provide more realistic interaction experiences. Our design simulates a multi-screen game engine by controlling a set of game engines simultaneously. A prototype with a body-area inertial sensor network platform, a cyber-physical game controller, and a set of game engines is demonstrated. The demonstrated game also addresses the interaction between virtual objects and players.

Tracking human posture system based on accelerometer. One fundamental issue in such scenarios is how to calculate the gravity, no matter when human body parts are moving or not. Assuming multiple accelerometers being deployed on a rigid part of a human body, a recent work proposes a data fusion method to estimate the gravity on that rigid part. However, how to find the optimal deployment of sensors that minimizes the estimation error of the gravity is still an open problem. In this paper, we formulate the deployment optimization problem, present deployment guidelines, and propose some heuristics, including a virtual-force-based (VF-based) method and a Metropolis-based search method. Experimental results are presented to verify our results.