Adaptive Collaborative Multi-Sensor Devices to Detect Body Position

• Main Authors: Jui-Cheng Chiang, 江瑞正
• Other Authors: Yueh-Min Huaung
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/41695622728845014868

碩士 === 國立成功大學 === 工程科學系碩博士班 === 97 === This study explored the collaborative detection of body behavior modes and accidental falling incidents by using multiple tri-axis acceleration sensors. Information is provided by sensors distributed over the body that transmit positions, by radio transmission devices to a computer, in order to analyze and recognize current body behavior status, which create a warning when a falling accident happens. After a falling accident occurs, more information of the sudden incident, such as body posture and impact of crucial position, can be provided to medical personnel for more accurate diagnosis. As affected by gravity, every object has a gravitational acceleration, g, toward the ground. This is a permanent and often fixed parameter with remarkable reference value. Under gravity, direction of force on each limb of the body varies. For example, when sitting, the legs and body are subjected to gravity of various directions. Each limb bears different accelerations due to different forces of motion behaviors. These characteristics are utilized to study the collaborative detection of multiple tri-axis acceleration sensors. When a falling accident occurs, injury of the first position impacted is usually the maximal, especially with head or spinal cord injuries. Thus, it is important to detect body posture of the fall and crucial position impact to provide relevant data to medical personnel for rescue and treatment. However, this topic is seldom discussed. Past studies have suggested that, using single tri-axis acceleration sensors have limitations of information deficiency, and fail to provide correct information of falling posture and impact position. Therefore, this study proposed a method that can provide enough data to recognize falling information. To identify a behavior incident or detect a falling incident, using multiple sensors can provide various conditions to judge the incident collaboratively.