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Kikhia, Basel
Publications (10 of 15) Show all publications
Kikhia, B., Stavropoulos, T. G., Meditskos, G., Kompatsiaris, I., Hallberg, J., Sävenstedt, S. & Melander, C. (2018). Utilizing ambient and wearable sensors to monitor sleep and stress for people with BPSD in nursing homes (ed.). Journal of Ambient Intelligence and Humanized Computing, 9(2), 261-273
Open this publication in new window or tab >>Utilizing ambient and wearable sensors to monitor sleep and stress for people with BPSD in nursing homes
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2018 (English)In: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, Vol. 9, no 2, p. 261-273Article in journal (Refereed) Published
Abstract [en]

Clinical assessment of behavioral and psychological symptoms of dementia (BPSD) in nursing homes is often based on staff member’s observations and the use of the Neuropsychiatric Inventory-Nursing Home version (NPI-NH) instrument. This requires continuous observation of the person with BPSD, and a lot of effort and manual input from the nursing home staff. This article presents the DemaWare@NH monitoring framework system, which complements traditional methods in measuring patterns of behavior, namely sleep and stress, for people with BPSD in nursing homes. The framework relies on ambient and wearable sensors for observing the users and analytics to assess their conditions. In our proof-of-concept scenario, four residents from two nursing homes were equipped with sleep and skin sensors, whose data is retrieved, processed and analyzed by the framework, detecting and highlighting behavioral problems, and providing relevant, accurate information to clinicians on sleep and stress patterns. The results indicate that structured information from sensors can ease and improve the understanding of behavioral patterns, and, as a consequence, the efficiency of care interventions, yielding a positive impact on the quality of the clinical assessment process for people with BPSD in nursing homes.

Place, publisher, year, edition, pages
Springer, 2018
National Category
Media and Communication Technology Nursing
Research subject
Pervasive Mobile Computing; Nursing
Identifiers
urn:nbn:se:ltu:diva-9225 (URN)10.1007/s12652-015-0331-6 (DOI)000429249200005 ()7c97293a-e058-4224-b5bb-882faec2867e (Local ID)7c97293a-e058-4224-b5bb-882faec2867e (Archive number)7c97293a-e058-4224-b5bb-882faec2867e (OAI)
Note

Validerad;2018;Nivå 2;2018-04-04 (rokbeg)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-04-26Bibliographically approved
Kikhia, B., Stavropoulos, T. G., Andreadis, S., Karvonen, N., Kompatsiaris, I., Sävenstedt, S., . . . Melander, C. (2016). Utilizing a Wristband Sensor to Measure the Stress Level for People with Dementia. Sensors, 16(12), Article ID 1989.
Open this publication in new window or tab >>Utilizing a Wristband Sensor to Measure the Stress Level for People with Dementia
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2016 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 16, no 12, article id 1989Article in journal (Refereed) Published
Abstract [en]

Stress is a common problem that affects most people with dementia and their caregivers. Stress symptoms for people with dementia are often measured by answering a checklist of questions by the clinical staff who work closely with the person with the dementia. This process requires a lot of effort with continuous observation of the person with dementia over the long term. This article investigates the effectiveness of using a straightforward method, based on a single wristband sensor to classify events of "Stressed" and "Not stressed" for people with dementia. The presented system calculates the stress level as an integer value from zero to five, providing clinical information of behavioral patterns to the clinical staff. Thirty staff members participated in this experiment, together with six residents suffering from dementia, from two nursing homes. The residents were equipped with the wristband sensor during the day, and the staff were writing observation notes during the experiment to serve as ground truth. Experimental evaluation showed relationships between staff observations and sensor analysis, while stress level thresholds adjusted to each individual can serve different scenarios.

National Category
Nursing Media and Communication Technology
Research subject
Nursing; Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-60730 (URN)10.3390/s16121989 (DOI)000391303000009 ()27886155 (PubMedID)2-s2.0-84997328010 (Scopus ID)
Note

Validerad; 2016; Nivå 2; 2016-11-28 (andbra)

Available from: 2016-11-28 Created: 2016-11-28 Last updated: 2018-10-15Bibliographically approved
Melander, C., Kikhia, B., Olsson, M., Välivaara, B.-M. & Sävenstedt, S. (2015). Assessment and evaluation of interventions in bpsd with the help of a multiple sensor system (ed.). Paper presented at Alzheimer's Association International Conference : 18/07/2015 - 23/07/2015. Alzheimer's & Dementia, 11(7), P164-P165
Open this publication in new window or tab >>Assessment and evaluation of interventions in bpsd with the help of a multiple sensor system
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2015 (English)In: Alzheimer's & Dementia, ISSN 1552-5260, E-ISSN 1552-5279, Vol. 11, no 7, p. P164-P165Article in journal, Meeting abstract (Refereed) Published
National Category
Nursing
Research subject
Nursing
Identifiers
urn:nbn:se:ltu:diva-35233 (URN)10.1016/j.jalz.2015.07.110 (DOI)9af13c7b-344c-4000-a62d-5bed6c2e853a (Local ID)9af13c7b-344c-4000-a62d-5bed6c2e853a (Archive number)9af13c7b-344c-4000-a62d-5bed6c2e853a (OAI)
Conference
Alzheimer's Association International Conference : 18/07/2015 - 23/07/2015
Note
Godkänd; 2015; 20151215 (andbra)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-30Bibliographically approved
Kikhia, B., Bengtsson, J., Melander, C. & Sävenstedt, S. (2015). Life logging in the context of dementia care: My life story (ed.). Paper presented at Alzheimer's Association International Conference : 18/07/2015 - 23/07/2015. Alzheimer's & Dementia, 11(7), P165
Open this publication in new window or tab >>Life logging in the context of dementia care: My life story
2015 (English)In: Alzheimer's & Dementia, ISSN 1552-5260, E-ISSN 1552-5279, Vol. 11, no 7, p. P165-Article in journal, Meeting abstract (Refereed) Published
National Category
Nursing
Research subject
Nursing
Identifiers
urn:nbn:se:ltu:diva-36983 (URN)10.1016/j.jalz.2015.07.111 (DOI)ad78ac6e-1eae-403a-a4bd-f99c5fac621c (Local ID)ad78ac6e-1eae-403a-a4bd-f99c5fac621c (Archive number)ad78ac6e-1eae-403a-a4bd-f99c5fac621c (OAI)
Conference
Alzheimer's Association International Conference : 18/07/2015 - 23/07/2015
Note
Godkänd; 2015; 20151215 (andbra)Available from: 2016-10-03 Created: 2016-10-03 Last updated: 2017-11-30Bibliographically approved
Kikhia, B., Simon, M. G., Jimenez, L. L., Hallberg, J., Karvonen, N. & Synnes, K. (2014). Analyzing Body Movements within the Laban Effort Framework using a Single Accelerometer (ed.). Paper presented at . Sensors, 14(3), 5725-41
Open this publication in new window or tab >>Analyzing Body Movements within the Laban Effort Framework using a Single Accelerometer
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2014 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, no 3, p. 5725-41Article in journal (Refereed) Published
Abstract [en]

This article presents a study on analyzing body movements by using a single accelerometer sensor. The investigated categories of body movements belong to the Laban Effort Framework: Strong - Light, Free – Bound and Sudden - Sustained. All body movements were represented by a set of activities used for data collection. The calculated accuracy of detecting the body movements was based on collecting data from a single wireless tri-axial accelerometer sensor. Ten healthy subjects collected data from three body locations (chest, wrist and thigh) simultaneously in order to analyze the locations comparatively. The data was then processed and analyzed using Machine Learning techniques. The wrist placement was found to be the best single location to record data for detecting (Strong – Light) body movements using the Random Forest classifier. The wrist placement was also the best location for classifying (Bound – Free) body movements using the SVM classifier. However, the data collected from the chest placement yielded the best results for detecting (Sudden – Sustained) body movements using the Random Forest classifier. The study shows that the choice of the accelerometer placement should depend on the targeted type of movement. In addition, the choice of the classifier when processing data should also depend on the chosen location and the target movement.

National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-14175 (URN)10.3390/s140305725 (DOI)000336783300101 ()24662408 (PubMedID)d869bee7-d398-4f28-a6fc-b928a102737c (Local ID)d869bee7-d398-4f28-a6fc-b928a102737c (Archive number)d869bee7-d398-4f28-a6fc-b928a102737c (OAI)
Note
Validerad; 2014; 20140311 (basel)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-10-15Bibliographically approved
Kikhia, B. (2014). Remember me! Supporting Reminiscence through Digital Capture of Lifestories and Activity Recognition (ed.). (Doctoral dissertation). Paper presented at . : Luleå tekniska universitet
Open this publication in new window or tab >>Remember me! Supporting Reminiscence through Digital Capture of Lifestories and Activity Recognition
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Lifelogging is the act of digitally capturing a person’s life experiences in the form of digital lifestories. A digital lifestory is a view of the person’s life based on a single activity or a set of activities, where activities are defined by content data, such as images, and context data, such as related places and persons. An example of a digital lifestory is a wedding where a set of selected images visually represents a person’s activities at the wedding. The person can use this representation as a digital means for later review and reminiscence of the captured activities in the lifestory about the wedding. This thesis discusses how new lifelogging technologies and methods for activity recognition can be used to create such digital lifestories and how these lifestories can be utilized for digital reminiscence. Digital capture of lifestories requires a lifelogging system capable of capturing daily activities. The digital representation of the person’s life should be interpreted and organized as activities that provide an insight into: What activities did the person do, When did the activities take place, Where did the activities take place, and Who was involved in the activities. Presenting the person’s life as activities provides an overview for reminiscing and helps the person selecting the significant activities to keep in digital lifestories.This thesis proposes a system that automatically filters captured lifelog data and then organizes the filtered data in the form of activities identified by time, location, movement data, and knowledge of context. The detection of significant places is implemented based on location clustering techniques that utilizes the density of the collected location data. The time periods when the user lingers at the same place of significance are then used to identify activities. The thesis shows that the required activity recognition can be improved by using knowledge of prior context and by automatic detection of significant places. The thesis also shows that everyday activities can be recognized using a single accelerometer, for which the wrist is the best placement of the accelerometer to analyze body movements and to detect daily activities.Two important aspects of lifelogging system design are to avoid encumbering or stigmatizing a person with too many devices, and to minimize required user interaction. The proposed lifelogging system is therefore highly automated, where the pervasively captured data is filtered from noisy data, segmented into representative activities, annotated with captured images, and organized as digital lifestories. A key finding is that one accelerometer plus one device for capturing location and images constitute a sufficient set of devices required to capture digital lifestories, hence supporting a person in reminiscing past activities by using the proposed system.The work has been evaluated through proof-of-concept prototype systems, which demonstrate the potential of reminiscence tools based on capture and review of digital lifestories. This work has the potential to make digital reminiscence systems more affordable, acceptable and easy to use, which also would lead to a positive impact on utilization. This can in particular be important for persons with special needs, such as persons with mild dementia, who generally cannot cope with too complex interaction. They can thus use such digital reminiscence systems for recollecting and reflecting on past life experiences.

Place, publisher, year, edition, pages
Luleå tekniska universitet, 2014
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-26470 (URN)e6514acf-3a22-4ccb-bb70-0b5bd2035573 (Local ID)978-91-7439-900-4 (ISBN)978-91-7439-901-1 (ISBN)e6514acf-3a22-4ccb-bb70-0b5bd2035573 (Archive number)e6514acf-3a22-4ccb-bb70-0b5bd2035573 (OAI)
Note
Godkänd; 2014; 20140326 (basel); Nedanstående person kommer att disputera för avläggande av teknologie doktorsexamen. Namn: Basel Kikhia Ämne: Distribuerade datorsystem/Pervasive Mobile Computing Avhandling: Remember Me! Supporting Reminiscence through Digital Capture of Lifestories and Activity Recognition Opponent: Professor Anthony Maeder, School of Computing, Engineering & Mathematics, University of Western Sydney, Campelltown, Australia, Ordförande: Professor Christer Åhlund, Avd för datavetenskap, Institutionen för system- och rymdteknik, Luleå tekniska universitet Tid: Tisdag den 27 maj 2014, kl 13.00 Plats: A109, Luleå tekniska universitetAvailable from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-01-10Bibliographically approved
Kikhia, B., Boytsov, A., Hallberg, J., Sani, Z. u., Jonsson, H. & Synnes, K. (2014). Structuring and Presenting Lifelogs based on Location Data (ed.). In: (Ed.), Pietro Cipresso; Alaksandar Matic; Guillaume Lopez (Ed.), Pervasive Computing Paradigms for Mental Health: 4th International Symposium, MindCare 2014, Tokyo, Japan, May 8-9, 2014, Revised Selected Papers. Paper presented at Mindcare : 4th International Symposium on Pervasive Computing Paradigms for Mental Health 08/05/2014 - 09/05/2014 (pp. 133-144). Cham: Encyclopedia of Global Archaeology/Springer Verlag
Open this publication in new window or tab >>Structuring and Presenting Lifelogs based on Location Data
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2014 (English)In: Pervasive Computing Paradigms for Mental Health: 4th International Symposium, MindCare 2014, Tokyo, Japan, May 8-9, 2014, Revised Selected Papers / [ed] Pietro Cipresso; Alaksandar Matic; Guillaume Lopez, Cham: Encyclopedia of Global Archaeology/Springer Verlag, 2014, p. 133-144Conference paper, Published paper (Refereed)
Abstract [en]

Lifelogging techniques help individuals to log their life and retrieve important events, memories and experiences. Structuring lifelogs is a major challenge in lifelogging systems since the system should present the logs in a concise and meaningful way to the user. In this paper the authors present an approach for structuring lifelogs as places and activities based on location data. The structured lifelogs are achieved using a combination of density-based clustering algorithms and convex hull construction to identify the places of interest. The periods of time where the user lingers at the same place are then identified as possible activities. In addition to structuring lifelogs the authors present an application in which images are associated to the structuring results and presented to the user for reviewing. The system is evaluated through a user study consisting of 12 users, who used the system for 1 day and then answered a survey. The proposed approach in this paper allows automatic inference of information about significant places and activities, which generates structured image-annotated logs of everyday life.

Place, publisher, year, edition, pages
Cham: Encyclopedia of Global Archaeology/Springer Verlag, 2014
Series
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, ISSN 1867-8211 ; 100
National Category
Media and Communication Technology Other Health Sciences Computer Sciences
Research subject
Mobile and Pervasive Computing; Health Science; Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-32667 (URN)10.1007/978-3-319-11564-1_14 (DOI)000349600800014 ()84910645111 (Scopus ID)7399f549-ac8d-4192-940d-d82b66db8791 (Local ID)978-3-319-11563-4 (ISBN)7399f549-ac8d-4192-940d-d82b66db8791 (Archive number)7399f549-ac8d-4192-940d-d82b66db8791 (OAI)
Conference
Mindcare : 4th International Symposium on Pervasive Computing Paradigms for Mental Health 08/05/2014 - 09/05/2014
Note
Godkänd; 2014; 20140328 (basel)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-07-10Bibliographically approved
Cleland, I., Kikhia, B., Nugent, C., Boytsov, A., Hallberg, J., Synnes, K., . . . Finlay, D. (2013). Optimal Placement of Accelerometers for the Detection of Everyday Activities (ed.). Paper presented at . Sensors, 13(7), 9183-9200
Open this publication in new window or tab >>Optimal Placement of Accelerometers for the Detection of Everyday Activities
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2013 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 13, no 7, p. 9183-9200Article in journal (Refereed) Published
Abstract [en]

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

National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-5827 (URN)10.3390/s130709183 (DOI)000328612800062 ()403d5d70-017f-4e22-8430-1117523ed033 (Local ID)403d5d70-017f-4e22-8430-1117523ed033 (Archive number)403d5d70-017f-4e22-8430-1117523ed033 (OAI)
Note
Validerad; 2014; 20140120 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Kikhia, B. & Hallberg, J. (2013). Visualizing and managing stress through colors and images (ed.). In: (Ed.), (Ed.), Proceeding: SenseCam '13 Proceedings of the 4th International SenseCam & Pervasive Imaging Conference. Paper presented at International SenseCam & Pervasive Imaging Conference : 18/11/2013 - 19/11/2013 (pp. 78-79). New York: ACM Digital Library
Open this publication in new window or tab >>Visualizing and managing stress through colors and images
2013 (English)In: Proceeding: SenseCam '13 Proceedings of the 4th International SenseCam & Pervasive Imaging Conference, New York: ACM Digital Library, 2013, p. 78-79Conference paper, Published paper (Refereed)
Abstract [en]

Stress is a widespread health problem [1] and technical solutions to manage stress are limited. This abstract presents a novel solution for identifying stressful situations using a sensecam and a skin conductance sensor. The presented system brings awareness to the user about emotional reactions in life that she might not always be aware of, and therefore helps people in managing their life to better maintain their wellbeing

Place, publisher, year, edition, pages
New York: ACM Digital Library, 2013
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-32960 (URN)10.1145/2526667.2526680 (DOI)2-s2.0-84890763795 (Scopus ID)7a53dcf7-6d67-4ea1-b846-4cb4b0fb1d22 (Local ID)978-1-4503-2247-8 (ISBN)7a53dcf7-6d67-4ea1-b846-4cb4b0fb1d22 (Archive number)7a53dcf7-6d67-4ea1-b846-4cb4b0fb1d22 (OAI)
Conference
International SenseCam & Pervasive Imaging Conference : 18/11/2013 - 19/11/2013
Note
Godkänd; 2013; 20140102 (andbra)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-07-10Bibliographically approved
Kikhia, B., Boytsov, A., Hallberg, J., Sani, Z. u., Jonsson, H. & Synnes, K. (2012). Structuring and presenting lifelogs based on location data (ed.). Paper presented at .
Open this publication in new window or tab >>Structuring and presenting lifelogs based on location data
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2012 (English)Report (Other academic)
Abstract [en]

Lifelogging techniques help individuals to log their life and retrieve important events, memories and experiences. Structuring lifelogs is a major challenge in lifelogging systems since the system should present the logs in a concise and meaningful way to the user. In this article the authors present a novel approach for structuring lifelogs as places and activities based on location data. The structured lifelogs are achieved using a combination of density-based clustering algorithms and convex hull construction to identify the places of interest. The periods of time where the user lingers at the same place are then identified as possible activities. In addition to structuring lifelogs the authors present an application in which images are associated to the structuring results and presented to the user for reviewing. The proposed approach allows automatic inference of information about significant places and activities, which generates structured image-annotated logs of everyday life.

Publisher
p. 19
Keywords
activity recognition, activity inference, lifelogging, clustering algorithms, SenseCam, GPS, Information technology - Computer science, Informationsteknik - Datorvetenskap
National Category
Media and Communication Technology Other Health Sciences Computer Sciences
Research subject
Pervasive Mobile Computing; Health Science; Dependable Communication and Computation Systems; Centre - eHealth Innovation Centre (EIC)
Identifiers
urn:nbn:se:ltu:diva-22631 (URN)394deb46-c865-4a03-9457-90beee116fa2 (Local ID)394deb46-c865-4a03-9457-90beee116fa2 (Archive number)394deb46-c865-4a03-9457-90beee116fa2 (OAI)
Note

Godkänd; 2012; 20121030 (andboy)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2019-03-20Bibliographically approved
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