Cristina Bolaños Peño

Bio: Cristina Bolaños Peño is an academic researcher. The author has contributed to research in topics: Computer science & Human–computer interaction. The author has co-authored 2 publications.

The SHAPES Smart Mirror Approach for Independent Living, Healthy and Active Ageing

Abstract: The benefits that technology can provide in terms of health and support for independent living are in many cases not enough to break the barriers that prevent older adults from accepting and embracing technology. This work proposes a hardware and software platform based on a smart mirror, which is equipped with a set of digital solutions whose main focus is to overcome older adults’ reluctance to use technology at home and wearable devices on the move. The system has been developed in the context of two use cases: the support of independent living for older individuals with neurodegenerative diseases and the promotion of physical rehabilitation activities at home. Aspects such as reliability, usability, consumption of computational resources, performance and accuracy of the proposed platform and digital solutions have been evaluated in the initial stages of the pilots within the SHAPES project, an EU-funded innovation action. It can be concluded that the SHAPES smart mirror has the potential to contribute as a technological breakthrough to overcome the barriers that prevent older adults from engaging in the use of assistive technologies.

Crowded event management in smart cities using a digital twin approach

Abstract: One challenge of any smart city is the management of crowded events (concerts, protests, marathons, etc.). For civil servants in charge of management, in-advance attendance prevision, real-time situational awareness and its evolution forecasting are crucial to resource assignment. These massive events put under stress public resources, organization and safety of smart cities. In this paper, we describe an ongoing effort to model urban layout, sensors deployed, and citizen information (from social networks and smartphone application) to deal with these situations. We use the concept of a digital twin applied to a city by modelling different flows of information which are integrated with a 3D virtual representation with forecasting possibilities. The main contribution of this paper is the architecture proposed and GUI using the augmented virtuality concept. The main purpose of our proposal is to facilitate the knowledge of the situation and the management of this type of event.

A Decision-Aware Ambient Assisted Living System with IoT Embedded Device for In-Home Monitoring of Older Adults

Abstract: Older adults’ independent life is compromised due to various problems, such as memory impairments and decision-making difficulties. This work initially proposes an integrated conceptual model for assisted living systems capable of providing helping means for older adults with mild memory impairments and their caregivers. The proposed model has four main components: (1) an indoor location and heading measurement unit in the local fog layer, (2) an augmented reality (AR) application to make interactions with the user, (3) an IoT-based fuzzy decision-making system to handle the direct and environmental interactions with the user, and (4) a user interface for caregivers to monitor the situation in real time and send reminders once required. Then, a preliminary proof-of-concept implementation is performed to evaluate the suggested mode’s feasibility. Functional experiments are carried out based on various factual scenarios, which validate the effectiveness of the proposed approach. The accuracy and response time of the proposed proof-of-concept system are further examined. The results suggest that implementing such a system is feasible and has the potential to promote assisted living. The suggested system has the potential to promote scalable and customizable assisted living systems to reduce the challenges of independent living for older adults.

Fall Detection and Prediction Based on IMU and EMG Sensors for Elders

Abstract: One of the most crucial changes in the future of social structure is the increase of the aging population. Accidents in the elderly are often caused by degeneration and worsening of their bodies. The most common accidents in the elderly are falls. This research proposes fall prediction and detection methods based on the Inertial Measurement Unit (IMU) sensor and Electromyogram (EMG). This method used features from EMG signal to adjust and co-verify with IMU sensor and then used machine learning technicians to create the model for abnormal classification gait, normal gait, and fall event. The results show that the EMG signal based on the Random forest model gained the average accuracy values of 3-class classifications (Abnormal gait, Normal gait, and Fall event) is 71.91%. For 4-class classifications (Abnormal left leg, Abnormal right leg, Normal gait, and Fall event) is 67.76%. The IMU sensor base on the Random Forest (RF) model got the best performance on both accuracies at 3-class and 4-class classification; the average accuracy value of 3-class classification is 94.72%. For the 4-class classification is 87.70%, respectively.

Bedtime Monitoring for Fall Detection and Prevention in Older Adults

Abstract: Life expectancy has increased, so the number of people in need of intensive care and attention is also growing. Falls are a major problem for older adult health, mainly because of the consequences they entail. Falls are indeed the second leading cause of unintentional death in the world. The impact on privacy, the cost, low performance, or the need to wear uncomfortable devices are the main causes for the lack of widespread solutions for fall detection and prevention. This work present a solution focused on bedtime that addresses all these causes. Bed exit is one of the most critical moments, especially when the person suffers from a cognitive impairment or has mobility problems. For this reason, this work proposes a system that monitors the position in bed in order to identify risk situations as soon as possible. This system is also combined with an automatic fall detection system. Both systems work together, in real time, offering a comprehensive solution to automatic fall detection and prevention, which is low cost and guarantees user privacy. The proposed system was experimentally validated with young adults. Results show that falls can be detected, in real time, with an accuracy of 93.51%, sensitivity of 92.04% and specificity of 95.45%. Furthermore, risk situations, such as transiting from lying on the bed to sitting on the bed side, are recognized with a 96.60% accuracy, and those where the user exits the bed are recognized with a 100% accuracy.

IoT Smart Flooring Supporting Active and Healthy Lifestyles

Abstract: The lack of physical exercise is among the most relevant factors in developing health issues, and strategies to incentivize active lifestyles are key to preventing these issues. The PLEINAIR project developed a framework for creating outdoor park equipment, exploiting the IoT paradigm to build "Outdoor Smart Objects" (OSO) for making physical activity more appealing and rewarding to a broad range of users, regardless of their age and fitness. This paper presents the design and implementation of a prominent demonstrator of the OSO concept, consisting of a smart, sensitive flooring, based on anti-trauma floors commonly found in kids playgrounds. The floor is equipped with pressure sensors (piezoresistors) and visual feedback (LED-strips), to offer an enhanced, interactive and personalized user experience. OSOs exploit distributed intelligence and are connected to the Cloud infrastructure by using a MQTT protocol; apps have then been developed for interacting with the PLEINAIR system. Although simple in its general concept, several challenges must be faced, related to the application range (which called for high pressure sensitivity) and the scalability of the approach (requiring to implement a hierarchical system architecture). Some prototypes were fabricated and tested in a public environment, providing positive feedback to both the technical design and the concept validation.