Valencia-Aragón, Kevin
Thumbnail Image
Preferred name
Valencia-Aragón, Kevin
Main Affiliation
Quito

Research Output

Filters

2
Reset filters

Settings
Now showing 1 - 2 of 2
No Thumbnail Available
Publication

Low-Cost Non-Wearable Fall Detection System Implemented on a Single Board Computer for People in Need of Care

2024 , Vanessa Vargas , Pablo Ramos , Edwin A. Orbe , Zapata, Mireya , Valencia-Aragón, Kevin

This work aims at proposing an affordable, non-wearable system to detect falls of people in need of care. The proposal uses artificial vision based on deep learning techniques implemented on a Raspberry Pi4 4GB RAM with a High-Definition IR-CUT camera. The CNN architecture classifies detected people into five classes: fallen, crouching, sitting, standing, and lying down. When a fall is detected, the system sends an alert notification to mobile devices through the Telegram instant messaging platform. The system was evaluated considering real daily indoor activities under different conditions: outfit, lightning, and distance from camera. Results show a good trade-off between performance and cost of the system. Obtained performance metrics are: precision of 96.4%, specificity of 96.6%, accuracy of 94.8%, and sensitivity of 93.1%. Regarding privacy concerns, even though this system uses a camera, the video is not recorded or monitored by anyone, and pictures are only sent in case of fall detection. This work can contribute to reducing the fatal consequences of falls in people in need of care by providing them with prompt attention. Such a low-cost solution would be desirable, particularly in developing countries with limited or no medical alert systems and few resources.

View
No Thumbnail Available
Publication

Indoor Monitoring System Based on Computer Vision for Fall Detection Oriented to Elderly Assistance

2025 , Vanessa Vargas , Pablo Ramos , Zapata, Mireya , Myriam Estrella , Valencia-Aragón, Kevin

This work aims to evaluate a non-wearable fall-detection system for indoor activities of older adults. The computer vision proposal uses a Convolutional Neural Network (CNN) architecture using the lightweight SSD-MobileNet-V2 model. Transfer learning techniques were applied to develop this approach. During the training process, a combined dataset conformed by the UR Fall Detection and an own dataset was used. Verification tests were performed under diverse scenarios, participants, and platforms to provide greater reliability to the evaluation results. Different distances from camera to person, lighting levels, attire, participant’s gender, and age were considered. For evaluation purposes, two platforms were selected: one based on a Single Board Computer (SBC) and the second on a laptop computer. Results show an accuracy of 95.5%, a precision of 99.4%, a sensitivity of 91.6%, and a specificity of 99.5% when executing the approach on a SBC. On the other hand, when the approach runs on a laptop, it achieves an accuracy of 96.6%, a precision of 97.3%, a sensitivity of 96.0%, and a specificity of 97.4%. Lastly, the system sends an alert notification when a fall is detected through a messaging platform. This event supports effectively medical assistance in reducing fatal consequences, especially for older adults living alone

View