Background: A major and complex challenge when trying to support individuals with dementia is meeting the needs of those who experience changes in behaviour and mood. Aim: To explore how a sensor measuring electrodermal activity (EDA) impacts assistant nurses' structured assessments of problematic behaviours amongst people with dementia and their choices of care interventions. Methods: Fourteen individuals with dementia wore a sensor that measured EDA. The information from the sensor was presented to assistant nurses during structured assessments of problematic behaviours. The evaluation process included scorings with the instrument NPI-NH (Neuropsychiatric Inventory-Nursing Home version), the care interventions suggested by assistant nurses to decrease problematic behaviours, and the assistant nurses' experiences obtained by focus group interviews. Results: The information from the sensor measuring EDA was perceived to make behavioural patterns more visual and clear, which enhanced assistant nurses' understanding of time-related patterns of behaviours. In turn, this enhancement facilitated timely care interventions to prevent the patterns and decrease the levels of problematic behaviour. Conclusion: With the addition of information from the sensor, nursing staff could target causes and triggers in a better way, making care interventions more specific and directed towards certain times throughout the day to prevent patterns of problematic behaviours.

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.

Computational intelligence is often used in smart environment applications in order to determine a user’scontext. Many computational intelligence algorithms are complex and resource-consuming which can beproblematic for implementation devices such as FPGA:s, ASIC:s and low-level microcontrollers. Thesetypes of devices are, however, highly useful in pervasive and mobile computing due to their small size,energy-efficiency and ability to provide fast real-time responses. In this paper, we propose a classi-fier, CORPSE, specifically targeted for implementation in FPGA:s, ASIC:s or low-level microcontrollers.CORPSE has a small memory footprint, is computationally inexpensive, and is suitable for parallel processing.The classifier was evaluated on eight different datasets of various types. Our results show thatCORPSE, despite its simplistic design, has comparable performance to some common machine learningalgorithms. This makes the classifier a viable choice for use in pervasive systems that have limitedresources, requires energy-efficiency, or have the need for fast real-time responses.

There is an increasing need for personalised and context-aware services in our everyday lives and we rely on mobile and wearable devices to provide such services. Context-aware applications often make use of machine-learning algorithms, but many of these are too complex or resource-consuming for implementation on some devices that are common in pervasive and mobile computing. The algorithm presented in this paper, named CAMP, has been developed to obtain a classifier that is suitable for resource-constrained devices such as FPGA:s, ASIC:s or microcontrollers. The algorithm uses a combination of the McCulloch-Pitts neuron model and Cellular Automata in order to produce a computationally inexpensive classifier with a small memory footprint. The algorithm consists of a sparse binary neural network where neurons are updated using a Cellular Automata rule as the activation function. Output of the classifier is depending on the selected rule and the interconnections between the neurons. Since solving the input-output mapping mathematically can not be performed using traditional optimization algorithms, the classifier is trained using a genetic algorithm. The results of the study show that CAMP, despite its minimalistic structure, has a comparable accuracy to that of more advanced algorithms for the datasets tested containing few classes, while performing poorly on the datasets with a higher amount of classes. CAMP could thus be a viable choice for solving classification problems in environments with extreme demands on low resource consumption

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.

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.

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.

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.