本研究主要運用三軸加速度計(Accelerometer)結合單晶片及快閃記憶體來達到即時三維資料的多元運算。三軸加速度計為利用壓電晶片感測運動學中之加速度或角加速度參數，以輸出之電壓值表示測得之數據，更可以透過「物聯網」並結合電腦或其他攜帶式裝置記錄所有生理信號並連線到雲端。
三軸加速度計又稱重力感測器(G-sensor)，主要是提供加速度變化的資訊，在工程上的應用非常廣泛。也因為固態微機電系統(Micro Electro Mechanical Systems , MEMS)的發展與普及，許多原本大尺寸的零件，都隨著MEMS製程的演進而越來越精細，尺寸越來越小，並且製作成本也隨著技術與時間的進展，隨之不斷的降低，到足以大量生產零件，並且以可接受的價格提供給市場，提供商業化的元件，如前述提到的三軸加速度計就是其中一種結合MEMS技術達到體積輕量化。另外磁場感應器(Compass, Magnetic Field)及傾斜度感應器(Orientation)等等也是常見結合MEMS的產品，可應用的範圍也愈來越多。
藉前述所提到的MEMS技術，可將如米粒般大小的晶片整合於穿戴式裝置中，並且透過不同的演算法及配戴方法，藉由不同姿態所造成的不同加速值進行演算，可量測多項生理功能，如活動紀錄、跌倒偵測、計步器、久坐偵測、心跳與呼吸偵測及睡眠分析，但針對不同訊號的量測，所需要的計算方法及取樣頻率也會有所不同，如計步器而言，取樣頻率10Hz就足以運算並獲得相應的走路或跑步狀態下較精確的步數。一般心跳及呼吸的量測都是需要透過醫院的儀器設備來量測，而且這兩種信號頻率經常是有相互交疊的現象，所以本研究將突破性開發結合三軸加速度計的穿戴式裝置來量測生理訊號。但也因為目前市面上常見的三軸加速度計對於微弱的震動感應較不靈敏，所以本研究需要透過軟韌體進行信號處理的方式，將微弱的心律震動與呼吸信號擷取出來，作為後續生理特徵檢測的信號來源。
有許多研究指出，三軸加速度計量測使用者動作訊號，較無量測位置限制，亦不需電極緊貼皮膚，在實務上較為方便，設計彈性也較大。本研究目的在發展個人生活型態模式分析，將穿戴式裝置三軸加速度計輸出的使用者動作及生理訊號轉換成判別活動強度及生理功能，並進而分析個人生活型態模式。

This study used a three-axis accelerometer combined with single-chip and flash memory for multivariate real-time 3D data. The three-axis accelerometer uses the piezoelectric principle to sense the acceleration or angular acceleration in kinematics and express the measured value in terms of output voltage also widely used in engineering applications. In the future, all physiological signals can be recorded and connected to the cloud through the Internet of Things (IOT)and combined with a computer or other portable device.
Because of the development and popularity of Micro Electro Mechanical Systems (MEMS), many of the original large-sized parts are getting smaller and smaller with the MEMS process. Production costs continue to decrease as technology and time evolve, enough to mass produce parts, and to the market at an acceptable price, providing commercial components. The aforementioned three-axis accelerometer is one of the requirements for combining MEMS technology to achieve small size and low price. In addition, the magnetic field sensor (Compass, Magnetic Field) and the tilt sensor (Orientation) are also common MEMS products, and the range of applications is increasing.
The aforementioned MEMS technology can integrate a rice-sized sensor into a wearable device, and through different algorithms and wearing methods, calculate different acceleration values caused by different postures, and measure a plurality of physiological signal. Such as activity records, fall detection, pedometer, sedentary detection, heartbeat and respiratory detection, and sleep analysis. However, the calculation method and sampling frequency required for different signal measurements will also be different. For example, a pedometer sampling frequency of 10 Hz is sufficient to calculate and obtain a corresponding number of steps in a walking or running state. Generally, the measurement of heart rate and breathing needs to be measured through hospital equipment, and the two signal frequencies often overlap each other. Therefore, this study will be a breakthrough development of a wearable device combined with a three-axis accelerometer to measure physiological signals. Because the three-axis accelerometers module currently on the market are less sensitive to weak vibration sensing, this study needs to use signal processing by software and firmware to extract weak heart rhythm vibrations and respiratory signals as signals for subsequent physiological feature detection.
There are many studies that indicate that the three-axis acceleration measurement user motion signal is less restrictive than the measurement position, and does not require the electrode to be close to the skin. It is more convenient in practice and has greater design flexibility. The purpose of this study is to record and analyze user motion and physiological signals through a three-axis accelerometer wearable device, which can be used to determine the intensity of activity and physiological state, and then to analyze the pattern of personal life.

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