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Synnes, K., Lilja, M., Nyman, A., Espinilla, M., Cleland, I., Sanchez Comas, A. G., . . . Nugent, C. (2018). H2Al - The Human Health and Activity Laboratory. In: MDPI (Ed.), 12th International Conference on Ubiquitous Computing and Ambient ‪Intelligence (UCAmI 2018), Punta Cana, Dominican Republic, 4-7 December, 2018.: . Paper presented at International Conference on Ubiquitous Computing and Ambient ‪Intelligence. MDPI, 2, Article ID 1241.
Open this publication in new window or tab >>H2Al - The Human Health and Activity Laboratory
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2018 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
MDPI, 2018
National Category
Computer and Information Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-72988 (URN)10.3390/proceedings2191241 (DOI)
Conference
International Conference on Ubiquitous Computing and Ambient ‪Intelligence
Projects
REMIND
Funder
The Kempe Foundations
Available from: 2019-02-21 Created: 2019-02-21 Last updated: 2019-09-06
Karvonen, N. (2018). Unobtrusive Activity Recognition in Resource-Constrained Environments. (Doctoral dissertation). Luleå: Luleå University of Technology
Open this publication in new window or tab >>Unobtrusive Activity Recognition in Resource-Constrained Environments
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Diskret Aktivitetsigenkänning i Resursbegränsade Miljöer
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.

Abstract [sv]

Denna avhandling diskuterar aktivitetsigenkänning ur ett diskret perspektiv, där enheter bärs eller placeras i miljön utan att vara stigmatiserande eller i vägen. Forskningen fokuserar på att utföra diskret aktivitetsigenkänning när beräknings- och sensor-resurser är knappa. Detta inkluderar att undersöka diskreta sätt att samla in data, samt att anpassa datamodellering och klassificering till små, resursbegränsade enheter.

Arbetet presenterar olika aspekter av datainsamling och datamodellering när man bara använder diskreta sensorer. Detta uppnås genom att överväga hur olika sensorplaceringar påverkar prediktionsprestanda och hur aktivitetsmodeller kan skapas vid användning av en enda sensor eller vid användning av ett antal enkla binära sensorer, för att utföra rörelsesanalys, känna igen vardagliga aktiviteter och utföra stressdetektering. Arbetet undersöker också hur klassificering kan utföras på resursbegränsade enheter, vilket resulterar i en ny beräkningseffektiv klassificeringsalgoritm och en effektiv handgjord klassificeringsmodell. Slutligen sätter arbetet in diskret aktivitetsigenkänning i verkliga sammanhang där det kan användas för interventioner för att minska stress, stillasittande  beteende och symptom på demens.

Resultaten visar att diskret aktivitetsigenkänning är möjligt och att klassificeringen kan utföras även på resursbegränsade enheter. Detta möjliggör övervakning av användarens aktiviteter under längre  perioder, vilket kan användas för att skapa personliga digitala interventioner och tidsanpassad rådgivning som hjälper användarna att göra positiva beteendeförändringar. Sådana digitala hälsointerventioner baserade på diskret aktivitetsigenkänning i resursbegränsade miljöer är viktiga för att ta itu med dagens samhällsutmaningar, såsom stillasittande beteende, stress, fetma och kroniska sjukdomar. En slutsats av arbetet är att diskret aktivitetsigenkänning är en hörnsten som är nödvändig för att få en större användning av digitala hälsointerventioner.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2018
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-71073 (URN)978-91-7790-232-4 (ISBN)978-91-7790-233-1 (ISBN)
Public defence
2018-12-11, C305, Luleå Tekniska Universitet, 97187 Luleå, Luleå, 09:00 (English)
Opponent
Supervisors
Available from: 2018-10-16 Created: 2018-10-15 Last updated: 2018-12-28Bibliographically approved
Karvonen, N., Jimenez, L. L., Gomez Simon, M., Nilsson, J., Kikhia, B. & Hallberg, J. (2017). Classifier Optimized for Resource-constrained Pervasive Systems and Energy-efficiency. Paper presented at 10th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI), San Bartolome de Tirajana, Spain, Nov 29-Dec 2, 2016. International Journal of Computational Intelligence Systems, 10(1), 1272-1279
Open this publication in new window or tab >>Classifier Optimized for Resource-constrained Pervasive Systems and Energy-efficiency
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2017 (English)In: International Journal of Computational Intelligence Systems, ISSN 1875-6891, E-ISSN 1875-6883, Vol. 10, no 1, p. 1272-1279Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Atlantis Press, 2017
Keywords
Cellular Automata, FPGA, Energy-efficient
National Category
Computer Sciences Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Pervasive Mobile Computing; Industrial Electronics
Identifiers
urn:nbn:se:ltu:diva-65869 (URN)10.2991/ijcis.10.1.86 (DOI)000415593600032 ()
Conference
10th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI), San Bartolome de Tirajana, Spain, Nov 29-Dec 2, 2016
Note

Konferensartikel i tidskrift

Available from: 2017-09-28 Created: 2017-09-28 Last updated: 2018-10-15Bibliographically approved
Karvonen, N., Kikhia, B., Jimenez, L. L., Gomez Simon, M. & Hallberg, J. (2016). A Computationally Inexpensive Classifier Merging Cellular Automata and MCP-Neurons. In: Carmelo R. García, Pino Caballero-Gil, Mike Burmester, Alexis Quesada-Arencibia (Ed.), Ubiquitous Computing and Ambient Intelligence: 10th International Conference, UCAmI 2016, San Bartolomé de Tirajana, Gran Canaria, Spain, November 29 – December 2, 2016, Part II. Paper presented at 10th International Conference, UCAmI 2016, San Bartolomé de Tirajana, Gran Canaria, Spain, November 29 – December 2, 2016 (pp. 368-379). Springer, 2
Open this publication in new window or tab >>A Computationally Inexpensive Classifier Merging Cellular Automata and MCP-Neurons
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2016 (English)In: 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, Published 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

Place, publisher, year, edition, pages
Springer, 2016
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10070
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-60640 (URN)10.1007/978-3-319-48799-1_42 (DOI)000389507400042 ()2-s2.0-85009786711 (Scopus ID)978-3-319-48798-4 (ISBN)978-3-319-48799-1 (ISBN)
Conference
10th International Conference, UCAmI 2016, San Bartolomé de Tirajana, Gran Canaria, Spain, November 29 – December 2, 2016
Available from: 2016-11-23 Created: 2016-11-23 Last updated: 2018-07-10Bibliographically 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
Karvonen, N. (2015). Activity recognition in resource-constrained pervasive systems (ed.). (Licentiate dissertation). Paper presented at . : Luleå tekniska universitet
Open this publication in new window or tab >>Activity recognition in resource-constrained pervasive systems
2015 (English)Licentiate 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.

Place, publisher, year, edition, pages
Luleå tekniska universitet, 2015. p. 85
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-26136 (URN)cd2ff2c1-700f-47ee-a3f6-ef93d2044e64 (Local ID)978-91-7583-485-6 (ISBN)978-91-7583-486-3 (ISBN)cd2ff2c1-700f-47ee-a3f6-ef93d2044e64 (Archive number)cd2ff2c1-700f-47ee-a3f6-ef93d2044e64 (OAI)
Note
Godkänd; 2015; 20151021 (nikkar); Nedanstående person kommer att hålla licentiatseminarium för avläggande av teknologie licentiatexamen. Namn: Niklas Karvonen Ämne: Distribuerade datorsystem/Pervasive Mobile Computing Uppsats: Activity Recognition in Resource-Constrained Pervasive Systems Examinator: Professor Kåre Synnes, Institutionen för system- och rymdteknik Avdelning: Datavetenskap, Luleå tekniska universitet Diskutant: Professor Chris Nugent, University of Ulster, Northern Ireland Tid: Tisdag 15 december 2015 kl 15.00 Plats: D770, Luleå tekniska universitetAvailable from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-01-10Bibliographically 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
Hedman, A., Karvonen, N., Hallberg, J. & Merilahti, J. (2014). Designing ICT for Health and Wellbeing (ed.). In: (Ed.), (Ed.), Ambient Assisted Living and Daily Activities: 6th International Work-Conference, IWAAL 2014, Belfast, UK, December 2-5, 2014. Proceedings. Paper presented at International Work-Conference : 02/12/2014 - 05/12/2014 (pp. 244-251). : Encyclopedia of Global Archaeology/Springer Verlag
Open this publication in new window or tab >>Designing ICT for Health and Wellbeing
2014 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Encyclopedia of Global Archaeology/Springer Verlag, 2014
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 8868
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-29758 (URN)10.1007/978-3-319-13105-4_36 (DOI)354d4c74-753a-490d-9a16-6184f8cb11a1 (Local ID)978-3-319-13104-7 (ISBN)978-3-319-13105-4 (ISBN)354d4c74-753a-490d-9a16-6184f8cb11a1 (Archive number)354d4c74-753a-490d-9a16-6184f8cb11a1 (OAI)
Conference
International Work-Conference : 02/12/2014 - 05/12/2014
Note
Validerad; 2015; Nivå 1; 20141218 (qwazi)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-10-15Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8752-2375

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