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| 2. |
- Baumgarten, Matthias, et al.
(author)
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Embedding Self-Awareness into Objects of Daily Life - The Smart Kettle
- 2010
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In: 6th International Conference on Intelligent Environments. - Los Alamitos, Calif. - 9781424478361 ; , s. 34-39
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Conference paper (peer-reviewed)abstract
- Intelligent Environments on varying scales and for different purposes are slowly becoming a reality. In the near future, global smart world infrastructures will become a commodity that will support various activities of daily life at different degrees of realism. Such infrastructures have the potential to offer dedicated, context- and situation-aware information and services by simultaneously providing the next-generation of data collection, execution and service provisioning layers. One key aspect of this vision is the correct monitoring and understanding of how people interact with their environment; how they can actually benefit from the added intelligence; and finally how future services can be improved or better personalized to enhance human environment interaction as a whole. This level of intelligence is of particular relevance in the health and social care domain where person-centric services can be deployed to assist or even enable a person in performing activities of daily living. This paper discusses the concept of embedded self-aware profiles for smart devices that can be used to gain a deeper contextual understanding of their use and also discusses the emergence of a general model of Ambient Intelligence that is based on the collective existence and behavior of such smart devices. Although generic in principle, the proposed concepts have been exemplified by a distinct use case, namely a smart kettle.
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| 3. |
- Beattie, Mark, et al.
(author)
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A Collaborative Patient-Carer Interface for Generating Home Based Rules for Self-Management
- 2015
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In: Smart Homes and Health Telematics. - New York : Encyclopedia of Global Archaeology/Springer Verlag. - 9783319144238 - 9783319144245 ; , s. 93-102
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Conference paper (peer-reviewed)abstract
- The wide spread prevalence of mobile devices, the decreasing costs of sensor technologies and increased levels of computational power have all lead to a new era in assistive technologies to support persons with Alzheimer’s disease. There is, however, still a requirement to improve the manner in which the technology is integrated into current approaches of care management. One of the key issues relating to this challenge is in providing solutions which can be managed by non-technically orientated healthcare professionals. Within the current work efforts have been made to develop and evaluate new tools with the ability to specify, in a non-technical manner, how the technology within the home environment should be monitored and under which conditions an alarm should be raised. The work has been conducted within the remit of a collaborative patient-carer system to support self-management for dementia. A visual interface has been developed and tested with 10 healthcare professionals. Results following a post evaluation of system usability have been presented and discussed.
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| 4. |
- Candefjord, Stefan, et al.
(author)
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Combining scanning haptic microscopy and fibre optic Raman spectroscopy for tissue characterization
- 2012
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In: Journal of Medical Engineering & Technology. - : Taylor & Francis. - 0309-1902 .- 1464-522X. ; 36:6, s. 319-327
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Journal article (peer-reviewed)abstract
- The tactile resonance method (TRM) and Raman spectroscopy (RS) are promising for tissue characterization in vivo. Our goal is to combine these techniques into one instrument, to use TRM for swift scanning, and RS for increasing the diagnostic power. The aim of this study was to determine the classification accuracy, using support vector machines, for measurements on porcine tissue and also produce preliminary data on human prostate tissue. This was done by developing a new experimental set-up combining micro-scale TRMscanning haptic microscopy (SHM)for assessing stiffness on a micro-scale, with fibre optic RS measurements for assessing biochemical content. We compared the accuracy using SHM alone versus SHM combined with RS, for different degrees of tissue homogeneity. The cross-validation classification accuracy for healthy porcine tissue types using SHM alone was 6581%, and when RS was added it increased to 8187%. The accuracy for healthy and cancerous human tissue was 6770% when only SHM was used, and increased to 7277% for the combined measurements. This shows that the potential for swift and accurate classification of healthy and cancerous prostate tissue is high. This is promising for developing a tool for probing the surgical margins during prostate cancer surgery.
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| 5. |
- Cleland, Ian, et al.
(author)
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Collection of a Diverse, Naturalistic and Annotated Dataset for Wearable Activity Recognition
- 2018
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In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). - : IEEE. ; , s. 555-560
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Conference paper (peer-reviewed)abstract
- This paper discusses the opportunities and challenges associated with the collection of a large scale, diverse dataset for Activity Recognition. The dataset was collected by 141 undergraduate students, in a controlled environment. Students collected triaxial accelerometer data from a wearable accelerometer whilst each carrying out 3 of the 18 investigated activities, categorized into 6 scenarios of daily living. This data was subsequently labelled, anonymized and uploaded to a shared repository. This paper presents an analysis of data quality, through outlier detection and assesses the suitability of the dataset for the creation and validation of Activity Recognition models. This is achieved through the application of a range of common data driven machine learning approaches. Finally, the paper describes challenges identified during the data collection process and discusses how these could be addressed. Issues surrounding data quality, in particular, identifying and addressing poor calibration of the data were identified. Results highlight the potential of harnessing these diverse data for Activity Recognition. Based on a comparison of six classification approaches, a Random Forest provided the best classification (F-measure: 0.88). In future data collection cycles, participants will be encouraged to collect a set of “common” activities, to support generation of a larger homogeneous dataset. Future work will seek to refine the methodology further and to evaluate model on new unseen data.
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| 6. |
- Cleland, Ian, et al.
(author)
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Optimal Placement of Accelerometers for the Detection of Everyday Activities
- 2013
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In: Sensors. - : MDPI AG. - 1424-8220. ; 13:7, s. 9183-9200
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Journal article (peer-reviewed)abstract
- This article describes an investigation to determine the optimal placement of accelerometers for the purpose of detecting a range of everyday activities. The paper investigates the effect of combining data from accelerometers placed at various bodily locations on the accuracy of activity detection. Eight healthy males participated within the study. Data were collected from six wireless tri-axial accelerometers placed at the chest, wrist, lower back, hip, thigh and foot. Activities included walking, running on a motorized treadmill, sitting, lying, standing and walking up and down stairs. The Support Vector Machine provided the most accurate detection of activities of all the machine learning algorithms investigated. Although data from all locations provided similar levels of accuracy, the hip was the best single location to record data for activity detection using a Support Vector Machine, providing small but significantly better accuracy than the other investigated locations. Increasing the number of sensing locations from one to two or more statistically increased the accuracy of classification. There was no significant difference in accuracy when using two or more sensors. It was noted, however, that the difference in activity detection using single or multiple accelerometers may be more pronounced when trying to detect finer grain activities. Future work shall therefore investigate the effects of accelerometer placement on a larger range of these activities.
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| 7. |
- Couch, Fergus J., et al.
(author)
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Identification of four novel susceptibility loci for oestrogen receptor negative breast cancer
- 2016
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In: Nature Communications. - : NATURE PUBLISHING GROUP. - 2041-1723. ; 7:11375, s. 1-13
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Journal article (peer-reviewed)abstract
- Common variants in 94 loci have been associated with breast cancer including 15 loci with genome-wide significant associations (P<5 x 10(-8)) with oestrogen receptor (ER)-negative breast cancer and BRCA1-associated breast cancer risk. In this study, to identify new ER-negative susceptibility loci, we performed a meta-analysis of 11 genome-wide association studies (GWAS) consisting of 4,939 ER-negative cases and 14,352 controls, combined with 7,333 ER-negative cases and 42,468 controls and 15,252 BRCA1 mutation carriers genotyped on the iCOGS array. We identify four previously unidentified loci including two loci at 13q22 near KLF5, a 2p23.2 locus near WDR43 and a 2q33 locus near PPIL3 that display genome-wide significant associations with ER-negative breast cancer. In addition, 19 known breast cancer risk loci have genome-wide significant associations and 40 had moderate associations (P<0.05) with ER-negative disease. Using functional and eQTL studies we implicate TRMT61B and WDR43 at 2p23.2 and PPIL3 at 2q33 in ER-negative breast cancer aetiology. All ER-negative loci combined account for similar to 11% of familial relative risk for ER-negative disease and may contribute to improved ER-negative and BRCA1 breast cancer risk prediction.
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| 8. |
- Cruciani, Frederico, et al.
(author)
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Automatic annotation for human activity recognition in free living using a smartphone
- 2018
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In: Sensors. - : MDPI. - 1424-8220. ; 18:7
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Journal article (peer-reviewed)abstract
- Data annotation is a time-consuming process posing major limitations to the development of Human Activity Recognition (HAR) systems. The availability of a large amount of labeled data is required for supervised Machine Learning (ML) approaches, especially in the case of online and personalized approaches requiring user specific datasets to be labeled. The availability of such datasets has the potential to help address common problems of smartphone-based HAR, such as inter-person variability. In this work, we present (i) an automatic labeling method facilitating the collection of labeled datasets in free-living conditions using the smartphone, and (ii) we investigate the robustness of common supervised classification approaches under instances of noisy data. We evaluated the results with a dataset consisting of 38 days of manually labeled data collected in free living. The comparison between the manually and the automatically labeled ground truth demonstrated that it was possible to obtain labels automatically with an 80–85% average precision rate. Results obtained also show how a supervised approach trained using automatically generated labels achieved an 84% f-score (using Neural Networks and Random Forests); however, results also demonstrated how the presence of label noise could lower the f-score up to 64–74% depending on the classification approach (Nearest Centroid and Multi-Class Support Vector Machine).
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| 9. |
- Cruciani, Federico, et al.
(author)
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Personalized Online Training for Physical Activity monitoring using weak labels
- 2018
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In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). - : IEEE. - 9781538632277 ; , s. 567-572
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Conference paper (peer-reviewed)abstract
- The use of smartphones for activity recognition is becoming common practice. Most approaches use a single pretrained classifier to recognize activities for all users. Research studies, however, have highlighted how a personalized trained classifier could provide better accuracy. Data labeling for ground truth generation, however, is a time-consuming process. The challenge is further exacerbated when opting for a personalized approach that requires user specific datasets to be labeled, making conventional supervised approaches unfeasible. In this work, we present early results on the investigation into a weakly supervised approach for online personalized activity recognition. This paper describes: (i) a heuristic to generate weak labels used for personalized training, (ii) a comparison of accuracy obtained using a weakly supervised classifier against a conventional ground truth trained classifier. Preliminary results show an overall accuracy of 87% of a fully supervised approach against a 74% with the proposed weakly supervised approach.
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| 10. |
- Cruciani, Federico, et al.
(author)
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Personalizing Activity Recognition with a Clustering based Semi-Population Approach
- 2020
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In: IEEE Access. - : IEEE. - 2169-3536. ; 8, s. 207794-207804
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Journal article (peer-reviewed)abstract
- Smartphone-based approaches for Human Activity Recognition have become prevalent in recent years. Despite the amount of research undertaken in the field, issues such as cross-subject variability are still posing an obstacle to the deployment of solutions in large scale, free-living settings. Personalized methods (i.e. aiming to adapt a generic classifier to a specific target user) attempt to solve this problem. The lack of labeled data for training purposes, however, represents a major barrier. This is especially the case when taking into consideration that personalization generally requires labeled data to be user-specific. This paper presents a novel personalization method combining a semi-population based approach with user adaptation. Personalization is achieved through the following. Firstly, the proposed method identifies a subset of users from the available population as best candidates for initializing the classifier to the target user. Subsequently, a semi-population Neural Network classifier is trained using data from this subset of users. The classifier’s network weights are then updated using a small amount of labeled data from the target user subsequently implementing personalization. This approach was validated on a large publicly available dataset collected in a free-living scenario. The personalized approach using the proposed method has shown to improve the overall F-score to 74.4% compared to 70.9% when using a generic non-personalized approach. Results obtained, with statistical significance being confirmed on a set of 57 users, indicate that model initialization using the semi-population approach can reduce the amount of labeled data required for personalization. As such, the proposed method for model initialization could facilitate the real-world deployment of systems implementing personalization by reducing the amount of data needed for personalization.
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