3 research outputs found
Magnetic and radar sensing for multimodal remote health monitoring
With the increased life expectancy and rise in health conditions related to aging, there is a need for new technologies that can routinely monitor vulnerable people, identify their daily pattern of activities and any anomaly or critical events such as falls. This paper aims to evaluate magnetic and radar sensors as suitable technologies for remote health monitoring purpose, both individually and fusing their information. After experiments and collecting data from 20 volunteers, numerical features has been extracted in both time and frequency domains. In order to analyse and verify the validation of fusion method for different classifiers, a Support Vector Machine with a quadratic kernel, and an Artificial Neural Network with one and multiple hidden layers have been implemented. Furthermore, for both classifiers, feature selection has been performed to obtain salient features. Using this technique along with fusion, both classifiers can detect 10 different activities with an accuracy rate of approximately 96%. In cases where the user is unknown to the classifier, an accuracy of approximately 92% is maintained
Automatic Fall Risk Detection based on Imbalanced Data
In recent years, the declining birthrate and aging population have gradually brought countries into an ageing society. Regarding accidents that occur amongst the elderly, falls are an essential problem that quickly causes indirect physical loss. In this paper, we propose a pose estimation-based fall detection algorithm to detect fall risks. We use body ratio, acceleration and deflection as key features instead of using the body keypoints coordinates. Since fall data is rare in real-world situations, we train and evaluate our approach in a highly imbalanced data setting. We assess not only different imbalanced data handling methods but also different machine learning algorithms. After oversampling on our training data, the K-Nearest Neighbors (KNN) algorithm achieves the best performance. The F1 scores for three different classes, Normal, Fall, and Lying, are 1.00, 0.85 and 0.96, which is comparable to previous research. The experiment shows that our approach is more interpretable with the key feature from skeleton information. Moreover, it can apply in multi-people scenarios and has robustness on medium occlusion
A Methodology for Trustworthy IoT in Healthcare-Related Environments
The transition to the so-called retirement years, comes with the freedom to pursue old passions
and hobbies that were not possible to do in the past busy life. Unfortunately, that freedom
does not come alone, as the previous young years are gone, and the body starts to feel the
time that passed. The necessity to adapt elder way of living, grows as they become more prone
to health problems. Often, the solution for the attention required by the elders is nursing
homes, or similar, that take away their so cherished independence.
IoT has the great potential to help elder citizens stay healthier at home, since it has the
possibility to connect and create non-intrusive systems capable of interpreting data and act
accordingly. With that capability, comes the responsibility to ensure that the collected data is
reliable and trustworthy, as human wellbeing may rely on it. Addressing this uncertainty is the
motivation for the presented work.
The proposed methodology to reduce this uncertainty and increase confidence relies on
a data fusion and a redundancy approach, using a sensor set. Since the scope of wellbeing
environment is wide, this thesis focuses its proof of concept on the detection of falls inside
home environments, through an android app using an accelerometer sensor and a micro-
phone. The experimental results demonstrates that the implemented system has more than
80% of reliable performance and can provide trustworthy results. Currently the app is being
tested also in the frame of the European Union projects Smart4Health and Smart Bear.A transição para os chamados anos de reforma, vem com a liberdade de perseguir velhas pai-
xões e passatempos que na passada vida ocupada não eram possÃveis de realizar. Infelizmente,
essa liberdade não vem sozinha, uma vez que os anos jovens anteriores terminaram, e o corpo
começa a sentir o tempo que passou. A necessidade de adaptar o modo de vida dos menos
jovens, cresce à medida que estes se tornam mais propensos a problemas de saúde. Muitas
vezes, a solução para a atenção que os mais idosos necessitam são os lares de idosos, ou
similares, que lhes tiram a tão querida independência.
IoT tem o grande potencial de ajudar os cidadãos idosos a permanecerem mais saudá-
veis em casa, uma vez que tem a possibilidade de se ligar e criar sistemas não intrusivos capa-
zes de interpretar dados e agir em conformidade. Com essa capacidade, vem a responsabili-
dade de assegurar que os dados recolhidos são fiáveis e de confiança, uma vez que o bem-
estar humano possa depender dos mesmos. Abordar esta incerteza é a motivação para o tra-
balho apresentado.
A metodologia proposta para reduzir esta incerteza e aumentar a confiança no sistema
baseia-se numa fusão de dados e numa abordagem de redundância, utilizando um conjunto
de sensores. Uma vez que o assunto de bem-estar e saúde é vasto, esta tese concentra a sua
prova de conceito na deteção de quedas dentro de ambientes domésticos, através de uma
aplicação android, utilizando um sensor de acelerómetro e um microfone. Os resultados expe-
rimentais demonstram que o sistema implementado tem um desempenho superior a 80% e
pode fornecer dados fiáveis. Atualmente a aplicação está a ser testada também no âmbito dos
projetos da União Europeia Smart4Health e Smart Bear