本研究採用低壓量測方式在手臂上加壓，利用自行設計的壓力感測裝置擷取使用者之血壓波形訊號，進而將此訊號導入自行開發之程式演算法，經過濾波、小波轉換找出區域的最大值及最小值，再將位移及正規化處理後的波形進行切波平均後，存成資料庫。當再次量測受測者之血壓時，讓系統利用相關係數、餘弦相似及均方誤差三種方式自動比對出受測者之身分，並登錄量測值，同時將最新之血壓波形更新至資料庫，以供下次比對之用。
經過實際測試的數據分析，證實限制量測的位置及壓脈帶的鬆緊度可以降低量測時造成的誤差，於演算法中加入性別參數會些微提升辨識成功率。目前本系統同一時段之辨識準確率已達 85% 以上；不同時段之辨識率為 41% 以上。由於在同一時段與不同時段的量測，其辨識結果有相當大的差距，因此實用性有待加強。未來若能以此系統為基礎，降低外界干擾因素並加入更多特徵值或是與其他生物特徵進行結合，可以提升辨識率同時也增加實用性。

Blood pressure waveform possess universality, uniqueness, permanency, collectability and other characteristics. In this study, we discussed the feasibility of human identification by using blood pressure waveform.
In this study, the pressuring process is achieved by low-pressure measurement method using self-designed pressure sensing device to capture the user's blood pressure waveform signal, and then this signal is importing to a self-developed algorithms. After process of filtering and the wavelet transform to identify regional maximum and minimum value, we store the displacement and normalized signal waveform with equalization into the database. When the user take another measurement of blood pressure, it allows the system to identify user identity automatically by using methods of correlation coefficient, cosine similarity, and the mean square error. Then the system will store the newest blood pressure waveform into database for next identification.
According to the experimental result analysis, the restrictions of the location and tightness of the cuff affect the measurement error. Additionally, add the gender parameter will slightly enhance the recognition results. Currently, recognition accuracy of this system at the same period has reached more than 85%, and 41% at different time; obviously, there is a great disparity between same period and separate time zone. Thus, we can reduce the external interference, add more characteristics, or combine original version with another biometric method to improve recognition rate and practicability of the system.

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