000			03560nam a22003137a 4500
003			OSt
005			20250704154130.0
008			250704b |||||||| |||| 00| 0 eng d
040			_aTUPM _bEnglish _cTUPM _dTUPM _erda
050			_aBTH QA 76.9 _bM37 2025
100			_aMartinez, Henry T. _eauthor
245			_aDevelopment of medication assistance robot with face recognition/ _cHenry T. Martinez, Claire Angel M. Ruan, Patricia Anne C. Subala, Ma. Niecel Ann R. Tesiorna, and Alyanna R. Topacio.--
300			_axiv, 147pages: _c29cm.
336			_2rdacontent
337			_2rdamedia
338			_2rdacarrier
500			_aBachelor's thesis
502			_aCollege of Industrial Education.-- _bBachelor of engineering technology major in computer engineering technology: _cTechnological University of the Philippines, _d2025.
504			_aIncludes bibliographic references and index.
520			_aMedication adherence among elderly individuals continues to be a challenge in healthcare, especially for caregivers responsible not only for assisting with medication intake but also for maintaining accurate records. To address this, the researchers developed a medication assistance robot with facial recognition and a water dispenser, ensuring both secure access and comfort during medication intake. A web-based application was also to provide caregivers with real-time updates, patient records, and live video monitoring. The system was tested for its accuracy based on face recognition using HuskyLens AI camera, and timeliness of the line tracking system, and real-time live monitoring. The HuskyLens AI camera accuracy was tested under various conditions such as lighting, distance, and angle. The test results showed that the HuskyLens AI camera has 89.36% accuracy wherein it can recognize faces with normal to medium lighting condition, can accurately recognize at a distance of 50 to 100 cm, and can detect faces at 0° and 15-degrees viewing angles. Testing of Line Tracking and Real Time Live Monitoring systems across 20 trials showed that Real Time Live Monitoring achieved precision with an average of 1.10 seconds compared to Line Tracking’s 2.5 seconds. Real Time Live Monitoring results in approximately twice the accuracy, with 90% of trials within ±1 second time response compared to Line Tracking 75% within ±2 seconds. The test result showed that Real Time Live Monitoring is more suitable for high-precision timing applications, though both systems have consistent and predictable performance. To evaluate the system’s performance, the researchers used the ISO 25010 quality model, focusing on Functionality, Suitability, Performance Efficiency, Compatibility, Usability, Reliability, and Maintainability. A total of 30 respondents, including IT professionals, caregivers, and students, were purposively selected to assess the prototype. Results showed that the robot performed satisfactorily across all features, including face recognition, medicine dispensing, line tracking, and real-time monitoring, although some limitations were observed, particularly in the line tracking system. The system received a grand weighted mean of 4.23, with a descriptive rating of “Very Acceptable”.
650			_aMedication adherence
650			_aElderly
650			_aLine tracking
700			_aRuan, Claire Angel M. _eauthor
700			_aSubala, Patricia Anne C. _eauthor
700			_aTesiorna, Ma. Niecel Ann R. _eauthor
700			_aTopacio, Alyanna R. _eauthor
942			_2lcc _cBTH CIT _n0
999			_c30141 _d30141