Human Motion Tracking and Its Data Compression in Body-Area Inertial Sensor Networks

• Main Authors: Wu, Chun-Hao, 吳鈞豪
• Other Authors: Tseng, Yu-Chee
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/12795347550000517363

博士 === 國立交通大學 === 資訊科學與工程研究所 === 100 === The advance of sensing technology and wireless communication has boosted body-area inertial sensor networks (BISNs), in which wireless wearable inertial sensor nodes are deployed on a human body to monitor its motion. Applications include medical care, pervasive video games, and affective computing. We conduct fundamental research into the technologies required to create an efficient wireless communication BISN that maximizes motion tracking accuracy and data collection efficiency. The first work addresses data collection issues in BISNs by data compression. We observe that, when body parts move, although sensor nodes in vicinity may compete strongly with each other, the transmitted data usually exists some levels of redundancy and even strong temporal and spatial correlations. Our scheme is specifically designed for BISNs, where nodes are likely fully connected and overhearing among sensor nodes is possible. We model the data compression problem for BISNs, where overhearing should be efficiently utilized, as a combinatorial optimization problem on overhearing graphs. We show its computational complexity and present efficient algorithms. We also discuss the design of the underlying MAC protocol to support our compression model. An experimental case study in Pilates exercises for patient rehabilitation is reported. The results show that our schemes reduce more than 70% of overall transmitted data compared with existing approaches. Based on the first work, where a node is allowed to overhear at most $\kappa = 1$ node's transmission, in the second work, we consider multi-spatial correlations by extending $\kappa = 1$ to $\kappa > 1$ and constructing a partial-ordering directed acyclic graph (DAG) to represent the compression dependencies among sensor nodes. While a minimum-cost tree for $\kappa = 1$ can be found in polynomial time, we show that finding a minimum-cost DAG is NP-hard even for $\kappa = 2$. We then propose an efficient heuristic and verify its performance by real sensing data. In addition to data collection, in the third work, we are interested in tracking human postures by deploying accelerometers on a human body. One fundamental issue in such scenarios is how to calculate the gravity. This is very challenging especially when the human body parts keep on moving. 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 vector on that rigid part. However, how to find the optimal deployment of sensors that minimizes the estimation error of the gravity vector is not addressed. In this work, we formulate the deployment optimization problem and propose two heuristics, called Metropolis-based method and largest-inter-distance-based (LID-based) method. Simulation and real experimental results show that our schemes are quite effective in finding near-optimal solutions for a variety of rigid body geometries.