| 1. |
- Beattie, Mark, et al.
(författare)
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A Collaborative Patient-Carer Interface for Generating Home Based Rules for Self-Management
- 2015
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Ingår i: Smart Homes and Health Telematics. - New York : Encyclopedia of Global Archaeology/Springer Verlag. - 9783319144238 - 9783319144245 ; , s. 93-102
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Konferensbidrag (refereegranskat)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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| 2. |
- Cleland, Dougal, et al.
(författare)
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Molecular dynamics approaches to the design and synthesis of PCB targeting molecularly imprinted polymers: interference to monomer-template interactions in imprinting of 1,2,3-trichlorobenzene
- 2014
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Ingår i: Organic and biomolecular chemistry. - : Royal Society of Chemistry (RSC). - 1477-0520 .- 1477-0539. ; 12:5, s. 844-853
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Tidskriftsartikel (refereegranskat)abstract
- The interactions between each component of the pre-polymerisation mixtures used in the synthesis of molecularly imprinted polymers (MIP) specific for 1,2,3,4,5-pentachlorobenzene (1) and 1,2,3-trichlorobenzene (2) were examined in four molecular dynamics simulations. These simulations revealed that the relative frequency of functional monomer template (FM T) interactions was consistent with results obtained by the synthesis and evaluation of the actual MIPs. The higher frequency of 1 interaction with tri-methylstyrene (TMS; 54.7%) than 1 interaction with pentafluorostyrene (PFS; 44.7%) correlated with a higher imprinting factor (IF) of 2.1 vs. 1.7 for each functional monomer respectively. The higher frequency of PFS interactions with 2 (29.6%) than TMS interactions with 2 (1.9%) also correlated well with the observed differences in IF (3.7) of 2 MIPs imprinted using PFS as the FM than the IF (2,8) of 2 MIPs imprinted using TMS as the FM. The TMS-1 interaction dominated the molecular simulation due to high interaction energies, but the weaker TMS-2 resulted in low interaction maintenance, and thus lower IF values. Examination of the other pre-polymerisation mixture components revealed that the low levels of TMS-2 interaction was, in part, due to interference caused by the cross linker (CL) ethyleneglycol dimethylacrylate (EGDMA) interactions with TMS. The main reason was, however, attributed to MeOH interactions with TMS in both a hydrogen bond and perpendicular configuration. This positioned a MeOH directly above the it-orbital of all TMS for an average of 63.8% of MD2 creating significant interference to pi-pi stacking interactions between 2 and TMS. These findings are consistent with the deviation from the 'normal' molecularly imprinted polymer synthesis ratio of 1 : 4 : 20 (T : FM : CL) of 20 : 1 : 29 and 15 : 6 : 29 observed with 2 and TMS and PFS respectively. Our molecular dynamics simulations correctly predicted the high level of interference from other MIP synthesis components. The effect on PFS-1 interaction by MeOH was significantly lower and thus this system was not adversely affected.
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| 3. |
- Cleland, Ian, et al.
(författare)
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Collection of a Diverse, Naturalistic and Annotated Dataset for Wearable Activity Recognition
- 2018
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Ingår i: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). - : IEEE. ; , s. 555-560
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Konferensbidrag (refereegranskat)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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| 4. |
- Cleland, Ian, et al.
(författare)
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Optimal Placement of Accelerometers for the Detection of Everyday Activities
- 2013
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 13:7, s. 9183-9200
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Tidskriftsartikel (refereegranskat)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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| 5. |
- Cruciani, Frederico, et al.
(författare)
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Automatic annotation for human activity recognition in free living using a smartphone
- 2018
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 18:7
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Tidskriftsartikel (refereegranskat)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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| 6. |
- Cruciani, Federico, et al.
(författare)
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Personalized Online Training for Physical Activity monitoring using weak labels
- 2018
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Ingår i: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). - : IEEE. - 9781538632277 ; , s. 567-572
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Konferensbidrag (refereegranskat)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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| 7. |
- Cruciani, Federico, et al.
(författare)
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Personalizing Activity Recognition with a Clustering based Semi-Population Approach
- 2020
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 8, s. 207794-207804
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Tidskriftsartikel (refereegranskat)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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| 8. |
- Nugent, Chris, et al.
(författare)
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An initiative for the creation of open datasets within pervasive healthcare
- 2016
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Ingår i: Proceedings of the 10th EAI International Conference onPervasive Computing Technologies for Healthcare. - : ICST, the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. - 9781631900518 ; , s. 318-321
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Konferensbidrag (refereegranskat)abstract
- In this paper issues surrounding the collection, annotation, management and sharing of data gathered from pervasive health systems are presented. The overarching motivation for this work has been to provide an approach whereby annotated data sets can be made readily accessible to the research community in an effort to assist the advancement of the state-of-the-art in activity recognition and behavioural analysis using pervasive health systems. Recommendations of how this can be made a reality are presented in addition to the initial steps which have been taken to facilitate such an initiative involving the definition of common formats for data storage and a common set of tools for data processing and visualization.
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| 9. |
- Nugent, Christopher, et al.
(författare)
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Improving the Quality of User Generated Data Sets for Activity Recognition
- 2016
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Ingår i: Ubiquitous Computing and Ambient Intelligence, UCAMI 2016, PT II. - Amsterdam : Springer Publishing Company. - 9783319487991 - 9783319487984 ; , s. 104-110
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Konferensbidrag (refereegranskat)abstract
- It is fully appreciated that progress in the development of data driven approaches to activity recognition are being hampered due to the lack of large scale, high quality, annotated data sets. In an effort to address this the Open Data Initiative (ODI) was conceived as a potential solution for the creation of shared resources for the collection and sharing of open data sets. As part of this process, an analysis was undertaken of datasets collected using a smart environment simulation tool. A noticeable difference was found in the first 1-2 cycles of users generating data. Further analysis demonstrated the effects that this had on the development of activity recognition models with a decrease of performance for both support vector machine and decision tree based classifiers. The outcome of the study has led to the production of a strategy to ensure an initial training phase is considered prior to full scale collection of the data.
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| 10. |
- Ortíz-Barrios, Miguel Angel, et al.
(författare)
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Simulated Data to Estimate Real Sensor Events—A Poisson-Regression-Based Modelling
- 2020
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Ingår i: Remote Sensing. - Basel : MDPI. - 2072-4292. ; 12:5
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Tidskriftsartikel (refereegranskat)abstract
- Automatic detection and recognition of Activities of Daily Living (ADL) are crucial for providing effective care to frail older adults living alone. A step forward in addressing this challenge is the deployment of smart home sensors capturing the intrinsic nature of ADLs performed by these people. As the real-life scenario is characterized by a comprehensive range of ADLs and smart home layouts, deviations are expected in the number of sensor events per activity (SEPA), a variable often used for training activity recognition models. Such models, however, rely on the availability of suitable and representative data collection and is habitually expensive and resource-intensive. Simulation tools are an alternative for tackling these barriers; nonetheless, an ongoing challenge is their ability to generate synthetic data representing the real SEPA. Hence, this paper proposes the use of Poisson regression modelling for transforming simulated data in a better approximation of real SEPA. First, synthetic and real data were compared to verify the equivalence hypothesis. Then, several Poisson regression models were formulated for estimating real SEPA using simulated data. The outcomes revealed that real SEPA can be better approximated ( R2pred = 92.72 % ) if synthetic data is post-processed through Poisson regression incorporating dummy variables. © 2020 MDPI (Basel, Switzerland)
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