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  • 1.
    Hedman, Anders
    et al.
    Kungliga tekniska högskolan, KTH.
    Karvonen, Niklas
    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.
    Merilahti, Juho
    VTT Technical Research Centre of Finland, Espoo.
    Designing ICT for Health and Wellbeing2014In: Ambient Assisted Living and Daily Activities: 6th International Work-Conference, IWAAL 2014, Belfast, UK, December 2-5, 2014. Proceedings, Encyclopedia of Global Archaeology/Springer Verlag, 2014, p. 244-251Conference paper (Refereed)
    Abstract [en]

    We are developing a monitoring and coaching app for health and wellbeing based on (1) an allostatic model of adaption combined with (2) behavioural change theory and (3) user-oriented design. The (1) allostatic model comes from stress research and was introduced to explain how human health and wellbeing can be maintained. It suggests that human health and wellbeing is a complex multidimensional phenomenon that needs to be understood holistically. We have used this model to incorporate the dimensions of human health and wellbeing that are key for stress reduction: physical and social activity and sleep. The allostatic model can allow us to understand human health and wellbeing but it does not tell us how to support the behavioural changes needed in order to reach a healthy state of allostasis. For this we rely on (2) theory of behavioural change. This article describes how we have integrated (1-3) into the system design and reports from an initial workshop with users.

  • 2.
    Karvonen, Niklas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Activity recognition in resource-constrained pervasive systems2015Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    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. Data collected from these devices includes important information about users’ movements, locations, physiological status, and environment. This data can be analysed in order to recognise users’ activities and thus provide contextual information for services. Such activity recognition is an important tool for personalising and adapting assistive services and thereby increasing the usefulness of them.This licentiate thesis focuses on three important aspects for activity recognition usingwearable, resource constrained, devices in pervasive services. Firstly, it is investigated how to perform activity recognition unobtrusively by using a single tri-axial accelerometer. This involves finding the best combination of sensor placement and machine learning algorithm for the activities to be recognized. The best overall placement was found to be on the wrist using the random forest algorithm for detecting Strong-Light, Free-Bound and Sudden-Sustained movement activities belonging to the Laban Effort Framework.Secondly, this thesis proposes a novel machine learning algorithm suitable for resource-constrained devices commonly found in wearable and pervasive systems. The proposed algorithm is computationally inexpensive, parallelizable, has a small memory footprint, and is suitable for implementation in hardware. Due to this, it can reduce battery usage, increase responsiveness, and also make it possible to distribute the machine learning task, which enables balancing computational costs against data traffic costs. The proposed algorithm is shown to have a comparable accuracy to that of more advanced machine learning algorithms mainly for datasets with two classes.Thirdly, activity recognition is applied in a personalised and pervasive service for im-proving health and wellbeing. Two monitoring prototypes and one coaching prototype were proposed for achieving positive behaviour change. The three prototypes were evaluated in a user workshop with 12 users aging between 20 and 60. Participants of the workshop believed that the proposed health and wellbeing app is something people are likely to use on a permanent basis.By applying results from this thesis, systems can be made more energy efficient andless obtrusive while still maintaining a high activity recognition accuracy. It also shows that pervasive and wearable systems using activity recognition have the potential of relieving some problems in health and wellbeing that society face today.

  • 3.
    Karvonen, Niklas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Unobtrusive Activity Recognition in Resource-Constrained Environments2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis discusses activity recognition from a perspective of unobtrusiveness, where devices are worn or placed in the environment without being stigmatising or in the way. The research focuses on performing unobtrusive activity recognition when computational and sensing resources are scarce. This includes investigating unobtrusive ways to gather data, as well as adapting data modelling and classification to small, resource-constrained, devices.

    The work presents different aspects of data collection and data modelling when only using unobtrusive sensing. This is achieved by considering how different sensor placements affects prediction performance and how activity models can be created when using a single sensor, or when using a number of simple binary sensors, to perform movement analysis, recognise everyday activities, and perform stress detection. The work also investigates how classification can be performed on resource-constrained devices, resulting in a novel computation-efficient classifier and an efficient hand-made classification model. The work finally sets unobtrusive activity recognition into real-life contexts where it can be used for interventions to reduce stress, sedentary behaviour and symptoms of dementia.

    The results indicate that activities can be recognised unobtrusively and that classification can be performed even on resource-constrained devices. This allows for monitoring a user’s activities over extensive periods, which could be used for creating highly personal digital interventions and in-time advice that help users make positive behaviour changes. Such digital health interventions based on unobtrusive activity recognition for resource-constrained environments are important for addressing societal challenges of today, such as sedentary behaviour, stress, obesity, and chronic diseases. The final conclusion is that unobtrusive activity recognition is a cornerstone necessary for bringing many digital health interventions into a wider use.

  • 4.
    Karvonen, Niklas
    et al.
    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.
    Gomez Simon, Miguel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Nilsson, Joakim
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Kikhia, Basel
    Faculty of Health and Sport Sciences, University of Agder 4879 Grimstad, Norway.
    Hallberg, Josef
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Classifier Optimized for Resource-constrained Pervasive Systems and Energy-efficiency2017In: International Journal of Computational Intelligence Systems, ISSN 1875-6891, E-ISSN 1875-6883, Vol. 10, no 1, p. 1272-1279Article in journal (Refereed)
    Abstract [en]

    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.

  • 5.
    Karvonen, Niklas
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Kikhia, Basel
    Jimenez, Lara Lorna
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Gomez Simon, Miguel
    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.
    A Computationally Inexpensive Classifier Merging Cellular Automata and MCP-Neurons2016In: Ubiquitous Computing and Ambient Intelligence: 10th International Conference, UCAmI 2016, San Bartolomé de Tirajana, Gran Canaria, Spain, November 29 – December 2, 2016, Part II / [ed] Carmelo R. García, Pino Caballero-Gil, Mike Burmester, Alexis Quesada-Arencibia, Springer, 2016, Vol. 2, p. 368-379Conference paper (Refereed)
    Abstract [en]

    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

  • 6.
    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.

  • 7.
    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.

  • 8.
    Synnes, Kåre
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Lilja, Margareta
    Luleå University of Technology, Department of Health Sciences, Health and Rehabilitation.
    Nyman, Anneli
    Luleå University of Technology, Department of Health Sciences, Health and Rehabilitation.
    Espinilla, Macarena
    Cleland, Ian
    Sanchez Comas, Andres Gabriel
    Comas Gonzalez, Zhoe Vanessa
    Hallberg, Josef
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. 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.
    Ourique de Morais, Wagner
    Cruciani, Federico
    Nugent, Chris
    H2Al - The Human Health and Activity Laboratory2018In: 12th International Conference on Ubiquitous Computing and Ambient ‪Intelligence (UCAmI 2018), Punta Cana, Dominican Republic, 4-7 December, 2018. / [ed] MDPI, MDPI, 2018, Vol. 2, article id 1241Conference paper (Refereed)
    Abstract [en]

    The Human Health and Activity Laboratory (H2Al) is a new research facility at Luleå University of Technology implemented during 2018 as a smart home environment in an educational training apartment for nurses and therapists at the Luleå campus. This paper presents the design and implementation of the lab together with a discussion on potential impact. The aim is to identify and overcome economical, technical and social barriers to achieve an envisioned good and equal health and welfare within and from home environments. The lab is equipped with multiple sensor and actuator systems in the environment, worn by persons and based on digital information. The systems will allow for advanced capture, filtering, analysis and visualization of research data such as A/V, EEG, ECG, EMG, GSR, respiration and location while being able to detect falls, sleep apnea and other critical health and wellbeing issues. The resulting studies will be aimed towards supporting and equipping future home environments and care facilities, spanning from temporary care to primary care at hospitals, with technologies for activity and critical health and wellness issue detection. The work will be conducted at an International level and within a European context, based on a collaboration with other smart labs, such that experiments can be replicated at multiple sites. This paper presents some initial lessons learnt including design, setup and configuration for comparison of sensor placements and configurations as well as analytical methods.

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