MARC details
| 000 -LEADER |
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| fixed length control field |
04027nam a22003377a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20250718171923.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250718b |||||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
TUPM |
| Language of cataloging |
English |
| Transcribing agency |
TUPM |
| Modifying agency |
TUPM |
| Description conventions |
rda |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
BTH TK 870 |
| Item number |
T33 2025 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Taberna, Camille Tracy T. |
| Relator term |
author |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Vifall: |
| Remainder of title |
real-time vital signs monitoring, fall detection, and notification systems using iot-based wearabale technology for elderly care: |
| Statement of responsibility, etc. |
Camille Tracy T. Taberna, Aljhun M. Abella, Justine Mae B. De Guzman, Rowell Mayabason, Evita Joyce B. Ngo, and Matthew Aaron A. Pagente.-- |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Manila: |
| Name of publisher, distributor, etc. |
Technological University of the Philippines, |
| Date of publication, distribution, etc. |
2025. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xv, 229pages: |
| Dimensions |
29cm. |
| 336 ## - CONTENT TYPE |
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| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| 500 ## - GENERAL NOTE |
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| General note |
Bachelor's thesis<br/> |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
College Of Engineering.--<br/> |
| Degree type |
Bachelor of science in electronics engineering: |
| Name of granting institution |
Technological University of the Philippines, <br/> |
| Year degree granted |
2025. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographic references and index. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
This research presents ViFall, an innovative IoT-based wearable system designed for<br/>real-time vital signs monitoring and fall detection, aimed at enhancing elderly care. The<br/>system utilizes an Arduino-powered device equipped with photoplethysmography (PPG)<br/>sensors to continuously monitor vital signs, which include heart rate and blood oxygen<br/>levels. In addition, an accelerometer and gyroscope are used to detect falls based on<br/>differences in movement patterns, giving a reliable fall detection mechanism. To ensure<br/>real-time communication, a GSM module is added into the device for fast sending of alerts<br/><br/>to caregivers or family members through missed calls when a fall happens. A threshold-<br/>based algorithm running on the Arduino processes the sensor data, while the NodeMCU is<br/><br/>used for wireless connectivity, ensuring smooth data transmission to remote monitoring<br/>systems. Additionally, an Android application has been developed, offering users a<br/>platform for continuous monitoring of vital signs and logging the abnormalities and falls<br/>detected into the notifications section. The system’s performance is evaluated in<br/>accordance with ISO/IEEE 11073 – 10471 software quality standards, ensuring the<br/>solution meets key quality attributes such as functionality, usability, and reliability. This<br/>comprehensive testing proved its capability to give dependable real-time vital signs<br/>monitoring and fall detection for elderly individuals, helping improve safety and allowing<br/>caregivers or family members to respond promptly to emergencies. The system achieved<br/>high performance on all metrics, with an accuracy of 93.47% showing high capability to<br/>identify fall and non-fall events. A precision of 98.18%, tells that almost all detected falls<br/>are real falls. The recall of 90.00% shows that the system successfully identified many of<br/>the actual falls, but still need more improvements. The specificity of 97.88% means the<br/><br/>system has a strong ability to identify non-fall events with minimal false alarms. The F1-<br/>Score of 93.91% suggests a good balance between precision and recall, meaning the system<br/>maintains solid performance in both detecting falls and minimizing false positives. For<br/>vital signs monitoring, based on tests conducted with 15 participants, the system achieved<br/>an average Root Mean Square Error (RMSE) of 2.41 for SpO2 and 2.53 for heart rate,<br/>indicating a high level of accuracy in continuous health tracking. Overall, ViFall offers an<br/>efficient and reliable tool for elderly care, with the use of IoT and wearable technology to<br/>enhance health monitoring and emergency response. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Fall detection |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
IoT wearable |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Arduino |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Abella, Aljhun M. |
| Relator term |
author |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
De Guzman, Justine Mae B. |
| Relator term |
author |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Mayabason, Rowell. |
| Relator term |
author |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ngo, Evita Joyce B. |
| Relator term |
author |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Pagente, Matthew Aaron A. |
| Relator term |
author |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Bachelor's Thesis COE |
| Suppress in OPAC |
No |