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

ANALYZING BIG DATA WITH DECISION TREES

ANALYZING BIG DATA WITH DECISION TREE

Application of multiobjective genetic programming to the design of robot failure recognition systems

We present an evolutionary approach using multiobjective genetic programming (MOGP) to derive optimal feature extraction preprocessing stages for robot failure detection. This data-driven machine learning method is compared both with conventional (nonevolutionary) classifiers and a set of domain-dependent feature extraction methods. We conclude MOGP is an effective and practical design method for failure recognition systems with enhanced recognition accuracy over conventional classifiers, independent of domain knowledge

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing