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Pejner, N. M., Ourique de Morais, W., Lundström, J., Laurell, H. & Skärsäter, I. (2019). A Smart Home System for Information Sharing, Health Assessments, and Medication Self-Management for Older People: Protocol for a Mixed-Methods Study. JMIR Research Protocols, 8(4), Article ID e12447.
Open this publication in new window or tab >>A Smart Home System for Information Sharing, Health Assessments, and Medication Self-Management for Older People: Protocol for a Mixed-Methods Study
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2019 (English)In: JMIR Research Protocols, ISSN 1929-0748, E-ISSN 1929-0748, Vol. 8, no 4, article id e12447Article in journal (Refereed) Published
Abstract [en]

Background: Older adults often want to stay in a familiar place, such as their home, as they get older. This so-called aging in place, which may involve support from relatives or care professionals, can promote older people’s independence and well-being. The combination of aging and disease, however, can lead to complex medication regimes, and difficulties for care providers in correctly assessing the older person's health. In addition, the organization of the health care is fragmented, which makes it difficult for health professionals to encourage older people to participate in their care. It is also a challenge to perform adequate health assessment and appropriate communication between health care professionals.

Objective: The purpose of this paper is to describe the design for an integrated home-based system that can acquire and compile health-related evidence for guidance and information sharing among care providers and care receivers in order to support and promote medication self-management among older people.

Methods: The authors used a participatory design (PD) approach for this mixed-method project, which was divided into four phases: Phase I, Conceptualization, consisted of the conceptualization of a system to support medication self- management, objective health assessments, and communication between health care professionals. Phase II, Development of a System, consisted of building and bringing together the conceptualized systems from phase I. Phases III (pilot study) and IV (a full-scale study) are described briefly.

Results: Our participants in phase I were people who were involved in some way in the care of older adults, and included older adults themselves, relatives of older adults, care professionals, and industrial partners. With input from phase I participants, we identified two relevant concepts for promoting medication self-management, both of which related to systems that participants believed could provide guidance for the older adults themselves, relatives of older adults, and care professionals. The system will also encourage information sharing between care providers and care receivers. The first is the concept of the Intelligent Friendly Home (IAFH), defined as an integrated residential system that evolves to sense, reason and act in response to individual needs, preferences and behaviors as these change over time. The second concept is the MedOP system, a system that would be supported by the IAFH, and which consists of three related components: one that assess health behaviors, another that communicates health data, and a third that promotes medication self-management.

Conclusions: The participants in this project were older adults, relatives of older adults, care professionals, and our industrial partners. With input from the participants, we identified two main concepts that could comprise a system for health assessment, communication and medication self-management: the Intelligent Friendly Home (IAFH), and the MedOP system. These concepts will be tested in this study to determine whether they can facilitate and promote medication self-management in older people. © The authors. All rights reserved. 

Place, publisher, year, edition, pages
Toronto: J M I R Publications, Inc., 2019
Keywords
assessments, medication, mixed methods, older people, self-management, smart homes
National Category
Nursing
Identifiers
urn:nbn:se:hh:diva-39753 (URN)10.2196/12447 (DOI)000466496800024 ()31038459 (PubMedID)2-s2.0-85067859310 (Scopus ID)
Available from: 2019-06-13 Created: 2019-06-13 Last updated: 2019-09-04Bibliographically approved
Ali Hamad, R., Järpe, E. & Lundström, J. (2018). Stability analysis of the t-SNE algorithm for human activity pattern data. In: : . Paper presented at The 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018), Miyazaki, Japan, Oct. 7-10, 2018.
Open this publication in new window or tab >>Stability analysis of the t-SNE algorithm for human activity pattern data
2018 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Health technological systems learning from and reacting on how humans behave in sensor equipped environments are today being commercialized. These systems rely on the assumptions that training data and testing data share the same feature space, and residing from the same underlying distribution - which is commonly unrealistic in real-world applications. Instead, the use of transfer learning could be considered. In order to transfer knowledge between a source and a target domain these should be mapped to a common latent feature space. In this work, the dimensionality reduction algorithm t-SNE is used to map data to a similar feature space and is further investigated through a proposed novel analysis of output stability. The proposed analysis, Normalized Linear Procrustes Analysis (NLPA) extends the existing Procrustes and Local Procrustes algorithms for aligning manifolds. The methods are tested on data reflecting human behaviour patterns from data collected in a smart home environment. Results show high partial output stability for the t-SNE algorithm for the tested input data for which NLPA is able to detect clusters which are individually aligned and compared. The results highlight the importance of understanding output stability before incorporating dimensionality reduction algorithms into further computation, e.g. for transfer learning.

National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-38442 (URN)
Conference
The 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018), Miyazaki, Japan, Oct. 7-10, 2018
Projects
SA3L
Available from: 2018-12-05 Created: 2018-12-05 Last updated: 2019-01-11Bibliographically approved
Norell Pejner, M., Lundström, J., Ourique de Morais, W., Laurell, H., Isaksson, A., Stranne, F. & Skärsäter, I. (2017). Smart medication organizer – one way to promote self-management and safety in drug administration in elderly people. In: : . Paper presented at Medicinteknikdagarna 2017, Västerås, Sweden, 10-11 October, 2017.
Open this publication in new window or tab >>Smart medication organizer – one way to promote self-management and safety in drug administration in elderly people
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2017 (English)Conference paper, Oral presentation only (Refereed)
National Category
Medical Engineering Other Medical Engineering
Identifiers
urn:nbn:se:hh:diva-35738 (URN)
Conference
Medicinteknikdagarna 2017, Västerås, Sweden, 10-11 October, 2017
Available from: 2017-12-05 Created: 2017-12-05 Last updated: 2017-12-11Bibliographically approved
Spinsante, S., Angelici, A., Lundström, J., Espinilla, M., Cleland, I. & Nugent, C. (2016). A Mobile Application for Easy Design and Testing of Algorithms to Monitor Physical Activity in the Workplace. International Journal of Mobile Information Systems, Article ID 5126816.
Open this publication in new window or tab >>A Mobile Application for Easy Design and Testing of Algorithms to Monitor Physical Activity in the Workplace
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2016 (English)In: International Journal of Mobile Information Systems, ISSN 1574-017X, E-ISSN 1875-905X, article id 5126816Article in journal (Refereed) Published
Abstract [en]

This paper addresses approaches to Human Activity Recognition (HAR) with the aim of monitoring the physical activity of people in the workplace, by means of a smartphone application exploiting the available on-board accelerometer sensor. In fact, HAR via a smartphone or wearable sensor can provide important information regarding the level of daily physical activity, especially in situations where a sedentary behavior usually occurs, like inmodern workplace environments. Increased sitting time is significantly associated with severe health diseases, and the workplace is an appropriate intervention setting, due to the sedentary behavior typical of modern jobs. Within this paper, the state-of-the-art components of HAR are analyzed, in order to identify and select the most effective signal filtering and windowing solutions for physical activity monitoring. The classifier development process is based upon three phases; a feature extraction phase, a feature selection phase, and a training phase. In the training phase, a publicly available dataset is used to test among different classifier types and learning methods. A user-friendly Android-based smartphone application with low computational requirements has been developed to run field tests, which allows to easily change the classifier under test, and to collect new datasets ready for use with machine learning APIs. The newly created datasets may include additional information, like the smartphone position, its orientation, and the user's physical characteristics. Using the mobile tool, a classifier based on a decision tree is finally set up and enriched with the introduction of some robustness improvements. The developed approach is capable of classifying six activities, and to distinguish between not active (sitting) and active states, with an accuracy near to 99%. The mobile tool, which is going to be further extended and enriched, will allow for rapid and easy benchmarking of new algorithms based on previously generated data, and on future collected datasets. © 2016 Susanna Spinsante et al.

Place, publisher, year, edition, pages
New York, NY: Hindawi Publishing Corporation, 2016
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:hh:diva-35695 (URN)10.1155/2016/5126816 (DOI)000382130500001 ()2-s2.0-84984698013 (Scopus ID)
Note

COST Action IC1303 AAPELE, Architectures, Algorithms and Platforms for Enhanced Living Environments

Available from: 2018-01-10 Created: 2018-01-10 Last updated: 2018-03-23Bibliographically approved
Lundström, J., Järpe, E. & Verikas, A. (2016). Detecting and exploring deviating behaviour of smart home residents. Expert systems with applications, 55, 429-440
Open this publication in new window or tab >>Detecting and exploring deviating behaviour of smart home residents
2016 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 55, p. 429-440Article in journal (Refereed) Published
Abstract [en]

A system for detecting deviating human behaviour in a smart home environment is the long-term goal of this work. Clearly, such systems will be very important in ambient assisted living services. A new approach to modelling human behaviour patterns is suggested in this paper. The approach reveals promising results in unsupervised modelling of human behaviour and detection of deviations by using such a model. Human behaviour/activity in a short time interval is represented in a novel fashion by responses of simple non-intrusive sensors. Deviating behaviour is revealed through data clustering and analysis of associations between clusters and data vectors representing adjacent time intervals (analysing transitions between clusters). To obtain clusters of human behaviour patterns, first, a random forest is trained without using beforehand defined teacher signals. Then information collected in the random forest data proximity matrix is mapped onto the 2D space and data clusters are revealed there by agglomerative clustering. Transitions between clusters are modelled by the third order Markov chain.

Three types of deviations are considered: deviation in time, deviation in space and deviation in the transition between clusters of similar behaviour patterns.

The proposed modelling approach does not make any assumptions about the position, type, and relationship of sensors but is nevertheless able to successfully create and use a model for deviation detection-this is claimed as a significant result in the area of expert and intelligent systems. Results show that spatial and temporal deviations can be revealed through analysis of a 2D map of high dimensional data. It is demonstrated that such a map is stable in terms of the number of clusters formed. We show that the data clusters can be understood/explored by finding the most important variables and by analysing the structure of the most representative tree. © 2016 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2016
Keywords
Ambient assisted living, Random forests, Stochastic neighbour embedding, Markov chain, Intelligent environments
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Signal Processing
Identifiers
urn:nbn:se:hh:diva-30594 (URN)10.1016/j.eswa.2016.02.030 (DOI)000374811000033 ()2-s2.0-84960082873 (Scopus ID)
Projects
CAISR / SA3L
Funder
Knowledge Foundation, 2010/0271
Available from: 2016-03-30 Created: 2016-03-30 Last updated: 2018-03-22Bibliographically approved
Synnott, J., Nugent, C., Zhang, S., Calzada, A., Cleland, I., Espinilla, M., . . . Lundström, J. (2016). Environment Simulation for the Promotion of the Open Data Initiative. In: 2016 IEEE INTERNATIONAL CONFERENCE ON SMART COMPUTING (SMARTCOMP): . Paper presented at 2nd IEEE International Conference on Smart Computing (SMARTCOMP), MAY 18-20, 2016, St Louis, MO (pp. 246-251). Piscataway, N.J.: IEEE
Open this publication in new window or tab >>Environment Simulation for the Promotion of the Open Data Initiative
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2016 (English)In: 2016 IEEE INTERNATIONAL CONFERENCE ON SMART COMPUTING (SMARTCOMP), Piscataway, N.J.: IEEE, 2016, p. 246-251Conference paper, Published paper (Refereed)
Abstract [en]

The development, testing and evaluation of novel approaches to Intelligent Environment data processing require access to datasets which are of high quality, validated and annotated. Access to such datasets is limited due to issues including cost, flexibility, practicality, and a lack of a globally standardized data format. These limitations are detrimental to the progress of research. This paper provides an overview of the Open Data Initiative and the use of simulation software (IE Sim) to provide a platform for the objective assessment and comparison of activity recognition solutions. To demonstrate the approach, a dataset was generated and distributed to 3 international research organizations. Results from this study demonstrate that the approach is capable of providing a platform for benchmarking and comparison of novel approaches.

Place, publisher, year, edition, pages
Piscataway, N.J.: IEEE, 2016
Keywords
simulation, intelligent environments, data sharing, activity recognition, deep learning
National Category
Software Engineering Computer Systems Computer Sciences
Identifiers
urn:nbn:se:hh:diva-35674 (URN)10.1109/SMARTCOMP.2016.7501690 (DOI)000390715200019 ()2-s2.0-84979520437& (Scopus ID)978-1-5090-0898-8 (ISBN)
Conference
2nd IEEE International Conference on Smart Computing (SMARTCOMP), MAY 18-20, 2016, St Louis, MO
Available from: 2017-12-05 Created: 2017-12-05 Last updated: 2018-01-13Bibliographically approved
Lundström, J., Ourique de Morais, W., Menezes, M. L., Gabrielli, C., Bentes, J., Pinheiro Sant'Anna, A., . . . Nugent, C. (2016). Halmstad intelligent home - Capabilities and opportunities. In: Mobyen Uddin AhmedShahina BegumWasim Raad (Ed.), Internet of Things Technologies for HealthCare: Third International Conference, HealthyIoT 2016, Västerås, Sweden, October 18-19, 2016, Revised Selected Papers. Paper presented at 3rd International Conference on Internet of Things Technologies for HealthCare, HealthyIoT 2016, Västerås, Sweden, 18 October 2016 through 19 October, 2016 (pp. 9-15). Berlin: Springer Berlin/Heidelberg, 187
Open this publication in new window or tab >>Halmstad intelligent home - Capabilities and opportunities
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2016 (English)In: Internet of Things Technologies for HealthCare: Third International Conference, HealthyIoT 2016, Västerås, Sweden, October 18-19, 2016, Revised Selected Papers / [ed] Mobyen Uddin AhmedShahina BegumWasim Raad, Berlin: Springer Berlin/Heidelberg, 2016, Vol. 187, p. 9-15Conference paper, Published paper (Refereed)
Abstract [en]

Research on intelligent environments, such as smart homes, concerns the mechanisms that intelligently orchestrate the pervasive technical infrastructure in the environment. However, significant challenges are to build, configure, use and maintain these systems. Providing personalized services while preserving the privacy of the occupants is also difficult. As an approach to facilitate research in this area, this paper presents the Halmstad Intelligent Home and a novel approach for multioccupancy detection utilizing the presented environment. This paper also presents initial results and ongoing work. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016.

Place, publisher, year, edition, pages
Berlin: Springer Berlin/Heidelberg, 2016
Series
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, ISSN 1867-8211 ; 187
Keywords
Intelligent environments, Multi-occupancy detection, Automation, Health care, Intelligent agents, Internet of things, Intelligent environment, Intelligent home, Occupancy detections, Personalized service, Smart homes, Technical infrastructure, Intelligent buildings
National Category
Computer and Information Sciences Computer Systems Human Aspects of ICT
Identifiers
urn:nbn:se:hh:diva-37786 (URN)10.1007/978-3-319-51234-1_2 (DOI)2-s2.0-85011263177 (Scopus ID)978-3-319-51233-4 (ISBN)
Conference
3rd International Conference on Internet of Things Technologies for HealthCare, HealthyIoT 2016, Västerås, Sweden, 18 October 2016 through 19 October, 2016
Available from: 2018-08-27 Created: 2018-08-27 Last updated: 2018-08-27Bibliographically approved
Nugent, C., Synnott, J., Gabrielli, C., Zhang, S., Espinilla, M., Calzada, A., . . . Ortiz Barrios, M. A. (2016). Improving the Quality of User Generated Data Sets for Activity Recognition. In: Garcia, CR CaballeroGil, P Burmester, M QuesadaArencibia, A (Ed.), Ubiquitous Computing and Ambient Intelligence, UCAMI 2016, PT II: . Paper presented at 10th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI), NOV 29-DEC 02, 2016, San Bartolome de Tirajana, SPAIN (pp. 104-110). Amsterdam: Springer Publishing Company
Open this publication in new window or tab >>Improving the Quality of User Generated Data Sets for Activity Recognition
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2016 (English)In: Ubiquitous Computing and Ambient Intelligence, UCAMI 2016, PT II / [ed] Garcia, CR CaballeroGil, P Burmester, M QuesadaArencibia, A, Amsterdam: Springer Publishing Company, 2016, p. 104-110Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Amsterdam: Springer Publishing Company, 2016
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10070
Keywords
Activity recognition, Open data sets, Data validation, Data driven classification
National Category
Other Computer and Information Science Computer Sciences Media Engineering Computer Systems
Identifiers
urn:nbn:se:hh:diva-35659 (URN)10.1007/978-3-319-48799-1_13 (DOI)000389507400013 ()2-s2.0-85009788304 (Scopus ID)978-3-319-48799-1 (ISBN)978-3-319-48798-4 (ISBN)
Conference
10th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI), NOV 29-DEC 02, 2016, San Bartolome de Tirajana, SPAIN
Available from: 2017-12-13 Created: 2017-12-13 Last updated: 2018-01-13Bibliographically approved
Uličný, M., Lundström, J. & Byttner, S. (2016). Robustness of Deep Convolutional Neural Networks for Image Recognition. In: Anabel Martin-Gonzalez, Victor Uc-Cetina (Ed.), Intelligent Computing Systems: First International Symposium, ISICS 2016, Mérida, México, March 16-18, 2016, Proceedings. Paper presented at First International Symposium, ISICS 2016, Mérida, México, March 16-18 2016 (pp. 16-30). Cham: Springer, 597
Open this publication in new window or tab >>Robustness of Deep Convolutional Neural Networks for Image Recognition
2016 (English)In: Intelligent Computing Systems: First International Symposium, ISICS 2016, Mérida, México, March 16-18, 2016, Proceedings / [ed] Anabel Martin-Gonzalez, Victor Uc-Cetina, Cham: Springer, 2016, Vol. 597, p. 16-30Conference paper, Published paper (Refereed)
Abstract [en]

Recent research has found deep neural networks to be vulnerable, by means of prediction error, to images corrupted by small amounts of non-random noise. These images, known as adversarial examples are created by exploiting the input to output mapping of the network. For the MNIST database, we observe in this paper how well the known regularization/robustness methods improve generalization performance of deep neural networks when classifying adversarial examples and examples perturbed with random noise. We conduct a comparison of these methods with our proposed robustness method, an ensemble of models trained on adversarial examples, able to clearly reduce prediction error. Apart from robustness experiments, human classification accuracy for adversarial examples and examples perturbed with random noise is measured. Obtained human classification accuracy is compared to the accuracy of deep neural networks measured in the same experimental settings. The results indicate, human performance does not suffer from neural network adversarial noise.

Place, publisher, year, edition, pages
Cham: Springer, 2016
Series
Communications in Computer and Information Science, ISSN 1865-0929
Keywords
Adversarial examples, Deep neural network, Noise robustness
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-31443 (URN)10.1007/978-3-319-30447-2_2 (DOI)000378489600002 ()2-s2.0-84960448659 (Scopus ID)978-3-319-30446-5 (ISBN)978-3-319-30447-2 (ISBN)
Conference
First International Symposium, ISICS 2016, Mérida, México, March 16-18 2016
Available from: 2016-06-28 Created: 2016-06-28 Last updated: 2018-03-22Bibliographically approved
Lundström, J., Ourique de Morais, W. & Cooney, M. (2015). A Holistic Smart Home Demonstrator for Anomaly Detection and Response. In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops): . Paper presented at SmartE: Closing the Loop – The 2nd IEEE PerCom Workshop on Smart Environments, St. Louis, Missouri, USA, March 23-27, 2015 (pp. 330-335). Piscataway, NJ: IEEE Press
Open this publication in new window or tab >>A Holistic Smart Home Demonstrator for Anomaly Detection and Response
2015 (English)In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), Piscataway, NJ: IEEE Press, 2015, p. 330-335Conference paper, Published paper (Refereed)
Abstract [en]

Applying machine learning methods in scenarios involving smart homes is a complex task. The many possible variations of sensors, feature representations, machine learning algorithms, middle-ware architectures, reasoning/decision schemes, and interactive strategies make research and development tasks non-trivial to solve.In this paper, the use of a portable, flexible and holistic smart home demonstrator is proposed to facilitate iterative development and the acquisition of feedback when testing in regard to the above-mentioned issues. Specifically, the focus in this paper is on scenarios involving anomaly detection and response. First a model for anomaly detection is trained with simulated data representing a priori knowledge pertaining to a person living in an apartment. Then a reasoning mechanism uses the trained model to infer and plan a reaction to deviating activities. Reactions are carried out by a mobile interactive robot to investigate if a detected anomaly constitutes a true emergency. The implemented demonstrator was able to detect and respond properly in 18 of 20 trials featuring normal and deviating activity patterns, suggesting the feasibility of the proposed approach for such scenarios. © IEEE 2015

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Press, 2015
National Category
Signal Processing Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-27740 (URN)10.1109/PERCOMW.2015.7134058 (DOI)000380510900075 ()2-s2.0-84946061065 (Scopus ID)978-1-4799-8425-1 (ISBN)
Conference
SmartE: Closing the Loop – The 2nd IEEE PerCom Workshop on Smart Environments, St. Louis, Missouri, USA, March 23-27, 2015
Projects
SA3L, CAISR
Funder
Knowledge Foundation
Available from: 2015-05-26 Created: 2015-02-09 Last updated: 2018-03-22Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8804-5884

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