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  • 1.
    Cleland, Ian
    et al.
    University of Ulster. School of Computing and Mathematics.
    Kikhia, Basel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Nugent, Chris
    University of Ulster. School of Computing and Mathematics.
    Boytsov, Andrey
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hallberg, Josef
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Synnes, Kåre
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    McClean, Sally
    Computing and Information Engineering, University of Ulster.
    Finlay, Dewar
    University of Ulster. School of Computing and Mathematics.
    Optimal Placement of Accelerometers for the Detection of Everyday Activities2013In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 13, no 7, p. 9183-9200Article in journal (Refereed)
    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

  • 2.
    Hallberg, Josef
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kikhia, Basel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Bengtsson, Johan
    InterNIT.
    Sävenstedt, Stefan
    Luleå University of Technology, Department of Health Sciences, Nursing Care.
    Synnes, Kåre
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Reminiscence processes using life-log entities for persons with mild dementia2009In: Proceedings of the First International Workshop on Reminiscence Systems (RSW-2009): Cambridge, UK, 5 September, 2009, 2009, p. 16-21Conference paper (Refereed)
    Abstract [en]

    In this paper we present the reminiscence process in a prototype memory support tool for persons with mild dementia. The purpose is to promote autonomy for persons with mild dementia by supporting actualization and maintenance of episodic memories, and real-time access to a context-annotated life log.  The main research challenges are defined with a user scenario, Suitable reminiscence methods and memory entitities to reperesent life logs are described, and a preliminary architecture is presented. Finally an early design of a concrete ReviewClient is shown, to solicit feedback on the reminiscence methods, entitites chosen, architecture and the usability of the proposed interface.

  • 3.
    Kikhia, Basel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Remember me! Supporting Reminiscence through Digital Capture of Lifestories and Activity Recognition2014Doctoral 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.

  • 4.
    Kikhia, Basel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Supporting lifestories through activity recognition and digital reminiscence2011Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This licentiate thesis discusses how lifelogging technologies can be used to build digital reminiscence systems. Lifelogging is a recent pervasive computing trend where different aspects of someone’s life are captured digitally. The aim of the proposed system is to create digital lifestories that can visualize the life of a person and provide a means for retrieving life experiences. The target users are people with mild dementia who have problems in navigating their daily life and in recalling previous events. The claim is that digital lifestories can be utilized for memory and reminiscence support as well as strengthen the bond between a person with mild dementia and his family. The main focus of the research study is about designing and developing digital reminiscence systems that can be used by people with mild dementia as aiding memory tools. Creating digital lifestories requires capturing of context data, such as places and people, and content data, such as sound and images, using pervasive lifelogging tools. The passive and continues capture of data results in the occurrence of false data and noise. For that, the system should reduce the collected data to not overload the user when reviewing the lifelogs. Another problem is that the life should be segmented in the form of activities that are searchable and accessible. Thus the collected lifelog data should be aggregated and structured into semantic activities and then represented as digital lifestories where context data can be retrieved together with related content. This licentiate thesis proposes solutions for filtering collected data to reduce the user’s efforts when reminiscing. The thesis also presents a method that uses prior knowledge of context data to improve the recognition of activities when creating the digital lifestories. In addition, locations where the user spends significant time can help in determining context parameters such as activities. This licentiate thesis proposes a novel approach that collects and clusters logged locations of the user to improve the activity recognition task. The presented approach defines possible places first, and it then identifies activities based on those places. Images, as content data, are then associated with the activities based on their timeframes so the user can review and adjust the data before saving it to his lifestory. The presented digital reminiscence system was evaluated through a field-test involving 10 people with mild dementia together with their caregivers. Healthcare professionals were also involved in the design and the evaluation of the system to improve the outcome of the study. The preliminary results indicate that the system indeed improves the quality of life for people with mild dementia, as their reminiscence processes are encouraged and that the communication with their surroundings increases in both volume and quality. The thesis shows that digital reminiscence systems, which describe life through activities, can increase the perceived quality of life for people with mild dementia. It also shows that activity recognition can be improved by using prior knowledge of context data and by automatic location clustering.

  • 5. Kikhia, Basel
    et al.
    Bengtsson, Johan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Melander, Catharina
    Luleå University of Technology, Department of Health Sciences, Nursing Care.
    Sävenstedt, Stefan
    Luleå University of Technology, Department of Health Sciences, Nursing Care.
    Life logging in the context of dementia care: My life story2015In: Alzheimer's & Dementia, ISSN 1552-5260, E-ISSN 1552-5279, Vol. 11, no 7, p. P165-Article in journal (Refereed)
  • 6.
    Kikhia, Basel
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Bengtsson, Johan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Distance- Spanning Technology.
    Synnes, Kåre
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Sani, Zaheer ul Hussain
    Hallberg, Josef
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Creating digital life stories through activity recognition with image filtering2010In: Aging friendly technology for health and independence: 8th International Conference on Smart Homes and Health Telematics, ICOST 2010, Seoul, Korea, June 22-24, 2010 ; proceedings / [ed] Yeunsook Lee, Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 2010, p. 203-210Conference paper (Refereed)
    Abstract [en]

    This paper presents two algorithms that enables the MemoryLane system to support persons with mild dementia through creation of digital life stories. The MemoryLane system consists of a Logging Kit that captures context and image data, and a Review Client that recognizes activities and enables review of the captured data. The image filtering algorithm is based on image characteristics such as brightness, blurriness and similarity, and is a central component of the Logging Kit. The activity recognition algorithm is based on the captured contextual data together with concepts of persons and places. The initial results indicate that the MemoryLane system is technically feasible and that activity-based creation of digital life stories for persons with mild dementia is possible.

  • 7.
    Kikhia, Basel
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Boytsov, Andrey
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hallberg, Josef
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Sani, Zaheer ul Hussain
    Luleå University of Technology, Department of Health Sciences.
    Jonsson, Håkan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Synnes, Kåre
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Structuring and Presenting Lifelogs based on Location Data2014In: 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 (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.

  • 8.
    Kikhia, Basel
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Boytsov, Andrey
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hallberg, Josef
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Sani, Zaheer ul Hussain
    Jonsson, Håkan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Synnes, Kåre
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Structuring and presenting lifelogs based on location data2012Report (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.

  • 9.
    Kikhia, Basel
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hallberg, Josef
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Visualizing and managing stress through colors and images2013In: Proceeding: SenseCam '13 Proceedings of the 4th International SenseCam & Pervasive Imaging Conference, New York: ACM Digital Library, 2013, p. 78-79Conference 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

  • 10.
    Kikhia, Basel
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hallberg, Josef
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Bengtsson, Johan
    InterNIT.
    Sävenstedt, Stefan
    Luleå University of Technology, Department of Health Sciences, Nursing Care.
    Synnes, Kåre
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Building digital life stories for memory support2010In: International Journal of Computers in Healthcare, ISSN 1755-3199, Vol. 1, no 2, p. 161-176Article in journal (Refereed)
    Abstract [en]

    The number of persons suffering from dementia is increasing, and there is significant human and economic value to gain by enabling them to keep living independently in their homes. The top priority unmet need is for memory support. This paper introduces context-awareness and life-logging in a system using reminiscence therapy methods, embodied as an ICT memory aid for recording past, current and future activities, which can later be recalled. The tool may help build or maintain episodic memories and self-image, although evidence in this area is lacking. It is designed to also give direct and instrumental support in other priority needs areas. A prototype design is described for a system that is by necessity extremely easy to use, with a touch screen computer in the home and mobile devices for data capture and cognitive support. The main life-log entities associated with the logged activities are places, persons, personal items, and recorded media. Privacy, trust and dignity are key ethical issues.

  • 11. Kikhia, Basel
    et al.
    Hallberg, Josef
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Synnes, Kåre
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Sani, Zaheer ul Hussain
    Context-aware life-logging for persons with mild dementia2009In: 2009 annual international conference of the IEEE Engineering in Medicine and Biology Society: EMBC 2009 ; Minneapolis, Minnesota, USA, 3 - 6 September 2009, Piscataway, NJ: IEEE Communications Society, 2009, p. 6183-6186Conference paper (Refereed)
    Abstract [en]

    The demands of introducing technology to support independent living is increasing. This is true also for persons suffering from mild dementia who may have difficulties remembering important information, such as activities, numbers, names, objects, faces, and so on. This paper presents a context-aware life-logging system, called MemoryLane, which can support independent living and improve quality of life for persons with mild dementia. The system offers both real time support as well as possibilities to rehearse and recall activities for building episodic memory. This paper also presents a mobile client to be used in MemoryLane, as well as an evaluation of the importance of different data for the purpose of memory recollection.

  • 12.
    Kikhia, Basel
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Simon, Miguel Gomez
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Jimenez, Lara Lorna
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hallberg, Josef
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Karvonen, Niklas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Synnes, Kåre
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Analyzing Body Movements within the Laban Effort Framework using a Single Accelerometer2014In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, no 3, p. 5725-41Article in journal (Refereed)
    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.

  • 13.
    Kikhia, Basel
    et al.
    Luleå University of Technology, Department of Health Sciences, Nursing Care.
    Stavropoulos, Thanos G.
    Information Technologies Institute, Centre for Research & Technology Hellas.
    Andreadis, Stelios
    Information Technologies Institute, Centre for Research & Technology Hellas.
    Karvonen, Niklas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Kompatsiaris, Ioannis
    Information Technologies Institute, Centre for Research & Technology Hellas.
    Sävenstedt, Stefan
    Luleå University of Technology, Department of Health Sciences, Nursing Care.
    Pijl, Marten
    Personal Health Solutions, Philips Research.
    Melander, Catharina
    Luleå University of Technology, Department of Health Sciences, Nursing Care.
    Utilizing a Wristband Sensor to Measure the Stress Level for People with Dementia2016In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 16, no 12, article id 1989Article in journal (Refereed)
    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.

  • 14.
    Kikhia, Basel
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Stavropoulos, Thanos G.
    Information Technologies Institute, Centre for Research & Technology Hellas.
    Meditskos, Georgios
    Information Technologies Institute, Centre for Research & Technology Hellas.
    Kompatsiaris, Ioannis
    Information Technologies Institute, Centre for Research & Technology Hellas.
    Hallberg, Josef
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Sävenstedt, Stefan
    Luleå University of Technology, Department of Health Sciences, Nursing Care.
    Melander, Catharina
    Luleå University of Technology, Department of Health Sciences, Nursing Care.
    Utilizing ambient and wearable sensors to monitor sleep and stress for people with BPSD in nursing homes2018In: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, Vol. 9, no 2, p. 261-273Article in journal (Refereed)
    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.

  • 15.
    Melander, Catharina
    et al.
    Luleå University of Technology, Department of Health Sciences, Nursing Care.
    Kikhia, Basel
    Olsson, Malin
    Luleå University of Technology, Department of Health Sciences, Nursing Care.
    Välivaara, Britt-Marie
    Luleå University of Technology, Department of Health Sciences, Nursing Care.
    Sävenstedt, Stefan
    Luleå University of Technology, Department of Health Sciences, Nursing Care.
    Assessment and evaluation of interventions in bpsd with the help of a multiple sensor system2015In: Alzheimer's & Dementia, ISSN 1552-5260, E-ISSN 1552-5279, Vol. 11, no 7, p. P164-P165Article in journal (Refereed)
1 - 15 of 15
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