A cascade-classifier approach for fall detection

The current machine learning algorithms in fall detection, especially those that use a sliding window, have a high computational cost because they need to compute the features from almost all samples. This computation causes energy drain and means that the associated wearable devices re- quire frequent recharging, making them less usable. This study proposes a cascade approach that reduces the computational cost of the fall detection classifier. To examine this approach, accelerometer data from 48 subjects performing a combination of falls and ordinary behaviour is used to train 3 types of classifier (J48 Decision Tree, Logistic Regression, and Multilayer Perceptron). The results show that the cascade approach significantly reduces the computational cost both for learning the classifier and executing it once learnt. Furthermore, the Multilayer Perceptron achieves the highest performance with precision of 93.5%, recall of 94.2%, and f-measure of 93.5%

An event-triggered machine learning approach for accelerometer-based fall detection

The fixed-size non-overlapping sliding window (FNSW) and fixed-size overlapping sliding window (FOSW) approaches are the most commonly used data-segmentation techniques in machine learning-based fall detection using accelerometer sensors. However, these techniques do not segment by fall stages (pre-impact, impact, and post-impact) and thus useful information is lost, which may reduce the detection rate of the classifier. Aligning the segment with the fall stage is difficult, as the segment size varies. We propose an event-triggered machine learning (EvenT-ML) approach that aligns each fall stage so that the characteristic features of the fall stages are more easily recognized. To evaluate our approach, two publicly accessible datasets were used. Classification and regression tree (CART), k-nearest neighbor (k-NN), logistic regression (LR), and the support vector machine (SVM) were used to train the classifiers. EvenT-ML gives classifier F-scores of 98% for a chest-worn sensor and 92% for a waist-worn sensor, and significantly reduces the computational cost compared with the FNSW- and FOSW-based approaches, with reductions of up to 8-fold and 78-fold, respectively. EvenT-ML achieves a significantly better F-score than existing fall detection approaches. These results indicate that aligning feature segments with fall stages significantly increases the detection rate and reduces the computational cost

First advances in near fall detection and prediction when using a walker

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)Falls are a major concern to society. Several injuries associated with falls need medical care, and in the worst-case scenario, a fall can lead to death. These consequences have a high cost for the population. In order to overcome these problems, a diversity of approaches for detection, prediction, and prevention of falls have been tackled. Walkers are often prescribed to subjects who present a higher risk of falling. Thus, it is essential to develop strategies to enhance the user's safety in an imminent danger situation. In this sense, this dissertation aims to develop a strategy to detect a near fall (NF) and its direction as well as the detection of incipient near fall (INF) while the subject uses a walker. Furthermore, it has the purpose of detecting two gait events, the heel strike (HS) and the toe-off (TO). The strategies established, in this work, were based on the data gathered through an inertial sensor placed on the lower trunk and force sensors placed on the insoles. Following data collection, the methodology adopted to identify the situations aforementioned was based on machine learning algorithms. In order to reach the model with best performance, many combinations of different classifiers were tested with three feature selection methods. Regarding the detection of NF, the results achieved presented a Matthews Correlation Coefficient (MCC) of 79.99% being possible to detect a NF 1.76±0.76s before its end. With the implementation of the post-processing algorithm, a large part of the false positives was eliminated being able to detect all NF 1.48±0.68s before its end. Concerning the models built to distinguish the direction of the NF, the best model presented accuracy of 58.97% being unable to reliably distinguish the three fall directions. The methodology followed, in this work, was unsuccessful to detect an INF. The best model presented MCC=23.87%, in this case. Lastly, with respect to the detection of HS and TO events the best model reached MCC=86.94%. With the application of the post-processing algorithm, part of misclassified samples was eliminated, however, a delay in the detection of the HS and TO events was introduced. With the post-processing it was possible to reach MCC=88.82%, not including the imposed delay.As quedas representam uma grande preocupação para a sociedade. Várias lesões associadas às quedas necessitam de cuidados médicos e, no pior dos casos, uma queda pode levar à morte. Estas consequências traduzem-se em custos elevados para a população. A fim de ultrapassar estes problemas, várias abordagens têm sido endereçadas para deteção, previsão e prevenção das quedas. Os andarilhos são muitas vezes prescritos a sujeitos que apresentam um risco de queda maior. Desta forma, é essencial desenvolver estratégias para aumentar a segurança do utilizador perante uma situação de perigo iminente. Neste sentido, esta dissertação visa desenvolver uma estratégia que permita a deteção de uma quase queda (NF) e a sua direção, assim como a deteção incipiente de uma NF (INF). Para além disso, tem o objetivo de detetar dois eventos de marcha, o heel strike (HS) e o toe-off (TO). As estratégias definidas, neste trabalho, basearam-se nos dados recolhidos através de um sensor inercial posicionado no tronco inferior e de sensores de força colocados nas palmilhas. Após a aquisição dos dados, a metodologia adotada para identificar as situações anteriormente referidas foi baseada em algoritmos de machine learning. Com o intuito de obter o modelo com melhor desempenho, várias combinações de diferentes classificadores foram testadas com três métodos de seleção de features. No que concerne à deteção da NF, os resultados alcançados apresentaram um Matthews Correlation Coefficient (MCC) de 79.99% sendo possível detetar uma NF 1.76±0.76s antes do seu final. Com a implementação do algoritmo de pós-processamento, grande parte dos falsos positivos foram eliminados, sendo possível detetar todas as NF 1.48±0.68s antes do seu final. Em relação aos modelos construídos para distinguir a direção da NF, o melhor modelo apresentou uma precisão (ACC) de 59.97%. A metodologia seguida neste trabalho não foi bem sucedida na deteção INF. O melhor modelo apresentou um MCC=23.87%. Relativamente à deteção dos eventos, HS e TO, o melhor modelo atingiu um MCC=86.94%. Com a aplicação do algoritmo de pós-processamento parte das amostras mal classificadas foram eliminadas, no entanto, foi introduzido um atraso na deteção do HS e do TO. Com o pós-processamento foi possível obter um MCC=88.82%, não incluindo o atraso imposto pelo pós-processamento

Proceedings, MSVSCC 2018

Proceedings of the 12th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2018 at VMASC in Suffolk, Virginia. 155 pp

Towards a smart fall detection system using wearable sensors

Empirical thesis."A thesis submitted as part of a cotutelle programme in partial fulfilment of Coventry University’s and Macquarie University’s requirements for the degree of Doctor of Philosophy" -- title page.Bibliography: pages 183-205.1. Introduction -- 2. Literature review -- 3. Falls and activities of daily living datasets -- 4. An analysis of fall-detection approaches -- 5. Event-triggered machine-learning approach (EvenT-ML) -- 6. Genetic-algorithm-based feature-selection technique for fall detection (GA-Fade) -- 7. Conclusions and future work -- References -- Appendices.A fall-detection system is employed in order to monitor an older person or infirm patient and alert their carer when a fall occurs. Some studies use wearable-sensor technologies to detect falls, as those technologies are getting smaller and cheaper. To date, wearable-sensor-based fall-detection approaches are categorised into threshold and machine-learning-based approaches. A high number of false alarms and a high computational cost are issues that are faced by the threshold- and machine-learning basedapproaches, respectively. The goal of this thesis is to address those issues by developing a novel low-computational-cost machine-learning-based approach for fall detection using accelerometer sensors.Toward this goal, existing fall-detection approaches (both threshold- and machine-learning-based) are explored and evaluated using publicly accessible datasets: Cogent, SisFall, and FARSEEING. Four machine-learning algorithms are implemented in this study: Classification and Regression Tree (CART), k-Nearest Neighbour (k-NN), Logistic Regression (LR), and Support Vector Machine (SVM). The experimental results show that using the correct size and type for the sliding window to segment the data stream can give the machine-learning-based approach a better detection rate than the threshold-based approach, though the difference between the threshold- and machine-learning-based approaches is not significant in some cases.To further improve the performance of the machine-learning-based approaches, fall stages (pre-impact, impact, and post-impact) are used as a basis for the feature extraction process. A novel approach called an event-triggered machine-learning approach for fall detection (EvenT-ML) is proposed, which can correctly align fall stages into a data segment and extract features based on those stages. Correctly aligning the stages to a data segment is difficult because of multiple high peaks, where a high peak usually indicates the impact stage, often occurring during the pre-impact stage. EvenT-ML significantly improves the detection rate and reduces the computational cost of existing machine-learning-based approaches, with an up to 97.6% F-score and a reduction in computational cost by a factor of up to 80 during feature extraction. Also, this technique can significantly outperform the threshold-based approach in all cases.Finally, to reduce the computational cost of EvenT-ML even further, the number of features needs to be reduced through a feature-selection process. A novel genetic-algorithm-based feature-selection technique (GA-Fade) is proposed, which uses multiple criteria to select features. GA-Fade considers the detection rate, the computational cost, and the number of sensors used as the selection criteria. GAFade is able to reduce the number of features by 60% on average, while achieving an F-score of up to 97.7%. The selected features also can give a significantly lower total computational cost than features that are selected by two single-criterion-based feature-selection techniques: SelectKBest and Recursive Feature Elimination.In summary, the techniques presented in this thesis significantly increase the detection rate of the machine-learning-based approach, so that a more reliable fall detection system can be achieved. Furthermore, as an additional advantage, these techniques can significantly reduce the computational cost of the machine-learning approach. This advantage indicates that the proposed machine-learning-based approach is more applicable to a small wearable device with limited resources (e.g., computing power and battery capacity) than the existing machine-learning-based approaches.Mode of access: World wide web1 online resource (xx, 211 pages) diagrams, graphs, table

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing