本論文設計了兩種有關於線性加速度感測器的應用─「跌倒偵測系統」和「手勢移動軌跡辨識系統」。近年來，已經有許多跌倒偵測的相關研究進行中，各有優缺點和限制。本論文目標之ㄧ是利用線性加速度感測器能偵測三軸方向加速度的特性，來辨別跌倒的方向，並結合ZigBee定位系統，讓跌倒意外的發生地點可以被快速地找到，以便做最迅速的救援。爲了提高容錯的可能性以及減少日常動作的誤判，本論文利用模糊系統來實現跌倒偵測系統，目前此系統已可分辨出前、後、左、右四種方向的跌倒，整體的辨識率為九成五。此外，我們已將此模糊系統實現在硬體的單晶片上，以增加其實用性。本論文提出的第二種應用是手勢移動軌跡的辨識系統，利用加速度感測器記錄手部移動軌跡的加速度值，透過無線方式傳至電腦，再藉由動態時間校正(dynamic time warping, DTW)演算法來辨識移動軌跡，目前可分辨六種基本手部移動軌跡，其整體辨識率可達到九成二。此外，本論文利用所發展的手勢移動軌跡的辨識系統來控制自走車之行進，有不錯之效果。 This thesis presents two accelerometer-based applications: a fall detection system and a gesture recognition system. Recently, there are several approaches to fall detection. Each approach has its own advantages, disadvantages, and limitations. The first objective of this thesis is not only to detect falls but also to identify the directions of falls based on a a tri-axis accelerometer. The proposed fall detection system incorporated with a ZigBee-based location system can quickly locate the position where a fall happens such that a quick and effective response can be issued. In order to increase the error tolerance and decrease the miss-classification of the activities of daily living, the fall detection system adopts a fuzzy system to implement the decision core module of the fall detection system. For the time being, the fall detection system can identify four directions and the correct recognition rate was about 95%. In addition, we have already implemented the fall detection system in a microprocessor to increase its applicability. The second objective of the thesis is to develop a hand gesture recognition system. An accelerometer is adopted to record a user’s hand trajectories. The trajectory data is transmitted wirelessly via an RF module to a computer. Then the dynamic time warping (DTW) algorithm is adopted to classify six different hand trajectories. Simulation results show that the recognition rate could achieve 92.2% correct. Finally, the proposed hand gesture recognition system was adopted for navigating a car-robot.