近年「病人安全」的議題受到世界各國的重視，病人安全開始成為新的研究議題。國內政府積極投入國內醫療照顧體系，大幅引用RFID、WLAN、Bluetooth及Zigbee等技術建立無線感測網路，結合醫療資訊系統，且以病人安全為中心的醫療照護模式。然而，在現有之醫療結合無線感測網路應用中，仍多著重於偵測病患之生理狀況或是追蹤病患之所在區域，仍未有整合性的研究平台。但對於精神疾病患者而言，由於患者情緒起伏大，容易失控，出現自殘等的失控行為，為避免患者離開管制範圍，或獨處而跌倒無法適時給予幫助，對於患者的定位追蹤尤其重要；此外，在醫學研究中，得知精神疾病患者在病情發作時或有自殘行為時，心跳會出現高於120/分或低於60/分的情況。因此本研究目標在提升精神疾病病患之醫療照顧，首先探討影響定位精準度之環境因子，透過檢討目前的定位相關演算法，改善目前定位演算法之準確性，並結合電機領域共同開發整合心跳感測與追蹤定位平台，以達到即時監控，提高對精神疾病患者之醫療照護服務品質。 Psychiatric patients are often required continuously monitoring for preventing dangerous situations. For the reason of patient safety, hospitals have to arrange additional staffs in protecting patients from danger, monitoring vital signs, and keeping them from leaving hospitals with no notice. However, ward staffs usually cannot get sufficient information to diagnose a psychiatric patient stepping into potential danger zones and encountering any other danger. Therefore, the aim of this project is to develop a wireless monitoring system for patient safety in psychiatric wards to reduce avoidable risks. This system can relieve nurses’ burden and help localize patients and monitor patient’s heartbeat. First, this research conducts experimental designs of reading performance considering various environment factors in order to verify current localization approaches. Based on evaluation results, an improved localization algorithm is proposed for tracking patients’ location. The third step is to design of heartbeat sensing on Zigbee-based platform. Finally, a proof-of-concept system is developed as to understand the current hardware challenges and functional analysis in Zigbee-based patient localization system. It is expected to significantly improve the patient safety in psychiatric wards with a low price.
