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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.
Basel Kikhia; Thanos G. Stavropoulos; Stelios Andreadis; Niklas Karvonen; Ioannis Kompatsiaris; Stefan Sävenstedt; Marten Pijl; Catharina Melander. Utilizing a Wristband Sensor to Measure the Stress Level for People with Dementia. Sensors 2016, 16, 1989 .
AMA StyleBasel Kikhia, Thanos G. Stavropoulos, Stelios Andreadis, Niklas Karvonen, Ioannis Kompatsiaris, Stefan Sävenstedt, Marten Pijl, Catharina Melander. Utilizing a Wristband Sensor to Measure the Stress Level for People with Dementia. Sensors. 2016; 16 (12):1989.
Chicago/Turabian StyleBasel Kikhia; Thanos G. Stavropoulos; Stelios Andreadis; Niklas Karvonen; Ioannis Kompatsiaris; Stefan Sävenstedt; Marten Pijl; Catharina Melander. 2016. "Utilizing a Wristband Sensor to Measure the Stress Level for People with Dementia." Sensors 16, no. 12: 1989.
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 [email protected] 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.
Basel Kikhia; Thanos G. Stavropoulos; Georgios Meditskos; Ioannis Kompatsiaris; Josef Hallberg; Stefan Sävenstedt; Catharina Melander. Utilizing ambient and wearable sensors to monitor sleep and stress for people with BPSD in nursing homes. Journal of Ambient Intelligence and Humanized Computing 2015, 9, 261 -273.
AMA StyleBasel Kikhia, Thanos G. Stavropoulos, Georgios Meditskos, Ioannis Kompatsiaris, Josef Hallberg, Stefan Sävenstedt, Catharina Melander. Utilizing ambient and wearable sensors to monitor sleep and stress for people with BPSD in nursing homes. Journal of Ambient Intelligence and Humanized Computing. 2015; 9 (2):261-273.
Chicago/Turabian StyleBasel Kikhia; Thanos G. Stavropoulos; Georgios Meditskos; Ioannis Kompatsiaris; Josef Hallberg; Stefan Sävenstedt; Catharina Melander. 2015. "Utilizing ambient and wearable sensors to monitor sleep and stress for people with BPSD in nursing homes." Journal of Ambient Intelligence and Humanized Computing 9, no. 2: 261-273.
Basel Kikhia; Johan E. Bengtsson; Catharina Melander; Stefan Savenstedt. F2-01-04: Life logging in the context of dementia care: My life story. Alzheimer's & Dementia 2015, 11, P165 -P165.
AMA StyleBasel Kikhia, Johan E. Bengtsson, Catharina Melander, Stefan Savenstedt. F2-01-04: Life logging in the context of dementia care: My life story. Alzheimer's & Dementia. 2015; 11 (7):P165-P165.
Chicago/Turabian StyleBasel Kikhia; Johan E. Bengtsson; Catharina Melander; Stefan Savenstedt. 2015. "F2-01-04: Life logging in the context of dementia care: My life story." Alzheimer's & Dementia 11, no. 7: P165-P165.
Basel Kikhia; Andrey Boytsov; Josef Hallberg; Zaheer Ul Hussain Sani; Håkan Jonsson; Kåre Synnes. Structuring and Presenting Lifelogs Based on Location Data. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2014, 133 -144.
AMA StyleBasel Kikhia, Andrey Boytsov, Josef Hallberg, Zaheer Ul Hussain Sani, Håkan Jonsson, Kåre Synnes. Structuring and Presenting Lifelogs Based on Location Data. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2014; ():133-144.
Chicago/Turabian StyleBasel Kikhia; Andrey Boytsov; Josef Hallberg; Zaheer Ul Hussain Sani; Håkan Jonsson; Kåre Synnes. 2014. "Structuring and Presenting Lifelogs Based on Location Data." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 133-144.
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.
Basel Kikhia; Miguel Gomez; Lara Lorna Jimenez; Josef Hallberg; Niklas Karvonen; Kåre Synnes. Analyzing Body Movements within the Laban Effort Framework Using a Single Accelerometer. Sensors 2014, 14, 5725 -5741.
AMA StyleBasel Kikhia, Miguel Gomez, Lara Lorna Jimenez, Josef Hallberg, Niklas Karvonen, Kåre Synnes. Analyzing Body Movements within the Laban Effort Framework Using a Single Accelerometer. Sensors. 2014; 14 (3):5725-5741.
Chicago/Turabian StyleBasel Kikhia; Miguel Gomez; Lara Lorna Jimenez; Josef Hallberg; Niklas Karvonen; Kåre Synnes. 2014. "Analyzing Body Movements within the Laban Effort Framework Using a Single Accelerometer." Sensors 14, no. 3: 5725-5741.
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.
Ian Cleland; Basel Kikhia; Chris Nugent; Andrey Boytsov; Josef Hallberg; Kåre Synnes; Sally McClean; Dewar Finlay. Optimal Placement of Accelerometers for the Detection of Everyday Activities. Sensors 2013, 13, 9183 -9200.
AMA StyleIan Cleland, Basel Kikhia, Chris Nugent, Andrey Boytsov, Josef Hallberg, Kåre Synnes, Sally McClean, Dewar Finlay. Optimal Placement of Accelerometers for the Detection of Everyday Activities. Sensors. 2013; 13 (7):9183-9200.
Chicago/Turabian StyleIan Cleland; Basel Kikhia; Chris Nugent; Andrey Boytsov; Josef Hallberg; Kåre Synnes; Sally McClean; Dewar Finlay. 2013. "Optimal Placement of Accelerometers for the Detection of Everyday Activities." Sensors 13, no. 7: 9183-9200.
Basel Kikhia; Josef Hallberg. Visualizing and managing stress through colors and images. Proceedings of the 4th International SenseCam & Pervasive Imaging Conference on - SenseCam '13 2013, 78 -79.
AMA StyleBasel Kikhia, Josef Hallberg. Visualizing and managing stress through colors and images. Proceedings of the 4th International SenseCam & Pervasive Imaging Conference on - SenseCam '13. 2013; ():78-79.
Chicago/Turabian StyleBasel Kikhia; Josef Hallberg. 2013. "Visualizing and managing stress through colors and images." Proceedings of the 4th International SenseCam & Pervasive Imaging Conference on - SenseCam '13 , no. : 78-79.
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.
Basel Kikhia; Johan E. Bengtsson; Kåre Synnes; Zaheer Ul Hussain Sani; Josef Hallberg. Creating Digital Life Stories through Activity Recognition with Image Filtering. Transactions on Petri Nets and Other Models of Concurrency XV 2010, 6159, 203 -210.
AMA StyleBasel Kikhia, Johan E. Bengtsson, Kåre Synnes, Zaheer Ul Hussain Sani, Josef Hallberg. Creating Digital Life Stories through Activity Recognition with Image Filtering. Transactions on Petri Nets and Other Models of Concurrency XV. 2010; 6159 ():203-210.
Chicago/Turabian StyleBasel Kikhia; Johan E. Bengtsson; Kåre Synnes; Zaheer Ul Hussain Sani; Josef Hallberg. 2010. "Creating Digital Life Stories through Activity Recognition with Image Filtering." Transactions on Petri Nets and Other Models of Concurrency XV 6159, no. : 203-210.
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.
Basel Kikhia; Josef Hallberg; Kåre Synnes; Zaheer Ul Hussain Sani. Context-aware life-logging for persons with mild dementia. 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2009, 2009, 6183 -6186.
AMA StyleBasel Kikhia, Josef Hallberg, Kåre Synnes, Zaheer Ul Hussain Sani. Context-aware life-logging for persons with mild dementia. 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2009; 2009 ():6183-6186.
Chicago/Turabian StyleBasel Kikhia; Josef Hallberg; Kåre Synnes; Zaheer Ul Hussain Sani. 2009. "Context-aware life-logging for persons with mild dementia." 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2009, no. : 6183-6186.