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Sökning: WFRF:(Hallberg Josef 1976 )

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1.
  • Cleland, Ian, et al. (författare)
  • Collection of a Diverse, Naturalistic and Annotated Dataset for Wearable Activity Recognition
  • 2018
  • Ingår i: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). - IEEE. ; s. 555-560
  • Konferensbidrag (refereegranskat)abstract
    • <p>This paper discusses the opportunities and challenges associated with the collection of a large scale, diverse dataset for Activity Recognition. The dataset was collected by 141 undergraduate students, in a controlled environment. Students collected triaxial accelerometer data from a wearable accelerometer whilst each carrying out 3 of the 18 investigated activities, categorized into 6 scenarios of daily living. This data was subsequently labelled, anonymized and uploaded to a shared repository. This paper presents an analysis of data quality, through outlier detection and assesses the suitability of the dataset for the creation and validation of Activity Recognition models. This is achieved through the application of a range of common data driven machine learning approaches. Finally, the paper describes challenges identified during the data collection process and discusses how these could be addressed. Issues surrounding data quality, in particular, identifying and addressing poor calibration of the data were identified. Results highlight the potential of harnessing these diverse data for Activity Recognition. Based on a comparison of six classification approaches, a Random Forest provided the best classification (F-measure: 0.88). In future data collection cycles, participants will be encouraged to collect a set of “common” activities, to support generation of a larger homogeneous dataset. Future work will seek to refine the methodology further and to evaluate model on new unseen data.</p>
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2.
  • Cruciani, Frederico, et al. (författare)
  • Automatic annotation for human activity recognition in free living using a smartphone
  • 2018
  • Ingår i: Sensors. - MDPI. - 1424-8220 .- 1424-8220. ; 18:7
  • Tidskriftsartikel (refereegranskat)abstract
    • <p>Data annotation is a time-consuming process posing major limitations to the development of Human Activity Recognition (HAR) systems. The availability of a large amount of labeled data is required for supervised Machine Learning (ML) approaches, especially in the case of online and personalized approaches requiring user specific datasets to be labeled. The availability of such datasets has the potential to help address common problems of smartphone-based HAR, such as inter-person variability. In this work, we present (i) an automatic labeling method facilitating the collection of labeled datasets in free-living conditions using the smartphone, and (ii) we investigate the robustness of common supervised classification approaches under instances of noisy data. We evaluated the results with a dataset consisting of 38 days of manually labeled data collected in free living. The comparison between the manually and the automatically labeled ground truth demonstrated that it was possible to obtain labels automatically with an 80–85% average precision rate. Results obtained also show how a supervised approach trained using automatically generated labels achieved an 84% f-score (using Neural Networks and Random Forests); however, results also demonstrated how the presence of label noise could lower the f-score up to 64–74% depending on the classification approach (Nearest Centroid and Multi-Class Support Vector Machine).</p>
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3.
  • Cruciani, Federico, et al. (författare)
  • Personalized Online Training for Physical Activity monitoring using weak labels
  • 2018
  • Ingår i: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). - IEEE. - 978-1-5386-3227-7 ; s. 567-572
  • Konferensbidrag (refereegranskat)abstract
    • <p>The use of smartphones for activity recognition is becoming common practice. Most approaches use a single pretrained classifier to recognize activities for all users. Research studies, however, have highlighted how a personalized trained classifier could provide better accuracy. Data labeling for ground truth generation, however, is a time-consuming process. The challenge is further exacerbated when opting for a personalized approach that requires user specific datasets to be labeled, making conventional supervised approaches unfeasible. In this work, we present early results on the investigation into a weakly supervised approach for online personalized activity recognition. This paper describes: (i) a heuristic to generate weak labels used for personalized training, (ii) a comparison of accuracy obtained using a weakly supervised classifier against a conventional ground truth trained classifier. Preliminary results show an overall accuracy of 87% of a fully supervised approach against a 74% with the proposed weakly supervised approach.</p>
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4.
  • Hedemalm, Emil, et al. (författare)
  • Promoting green transportation via persuasive games
  • 2017
  • Ingår i: International SEEDS Conference 2017 : Sustainable Ecological Engineering Design for Society – 13th &amp; 14th September 2017.
  • Konferensbidrag (refereegranskat)abstract
    • <p>It is now widely accepted that human behaviour accounts for a large portion of total global emissions, and thus influences climate change to a large extent (IPCC, 2014). Changing human behaviour when it comes to mode of transportation is one component which could make a difference in the long term. In order to achieve behavioural change, we investigate the use of a persuasive multiplayer game. Transportation mode recognition is used within the game to provide bonuses and penalties to users based on their daily choices regarding transportation. Preliminary results from testers of the game indicate that using games may be successful in causing positive change in user behaviour.</p>
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5.
  • Hong, Xin, et al. (författare)
  • OpenHome Approaches to Constructing Sharable Datasets within Smart Homes
  • 2009
  • Ingår i: CHI '09 : Extended Abstracts on Human Factors in Computing Systems. - New York : ACM Digital Library. - 978-1-60558-247-4
  • Konferensbidrag (refereegranskat)abstract
    • <p>In this paper we present our initial efforts to develop approaches for structuring and building openly accessible, scalable, shared home behaviour datasets within smart home communities.</p>
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6.
  • Karvonen, Niklas, 1979- (författare)
  • Unobtrusive Activity Recognition in Resource-Constrained Environments
  • 2018
  • Doktorsavhandling (övrigt vetenskapligt)abstract
    • <p>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.</p><p>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.</p><p>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.</p>
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7.
  • Kostenius, Catrine, et al. (författare)
  • Gamification of health education : Schoolchildren’s participation in the development of a serious game to promote health and learning
  • 2018
  • Ingår i: Health Education. - Emerald Group Publishing Limited. - 0965-4283 .- 1758-714X. ; 118:4, s. 354-368
  • Tidskriftsartikel (refereegranskat)abstract
    • <p>Purpose</p><p>The use of modern technology has many challenges and risks. However, by collaborating with schoolchildren, ideas to effectively promote health and learning in school can be identified. This study aimed to examine how a participatory approach can deepen the understanding of how schoolchildren relate to and use gamification as a tool to promote physical activity and learning.</p><p>Design/methodology/approach</p><p>Inspired by the concept and process of empowerment and child participation, the methodological focus of this study was on consulting schoolchildren. During a 2-month period, 18 schoolchildren (10–12-years-old) participated in workshops to create game ideas that would motivate them to be physically active and learn in school.</p><p>Findings</p><p>The phenomenological analysis resulted in one main theme, ‘Playing games for fun to be the best I can be’. This consisted of four themes with two sub-themes each. The findings offer insights on how to increase physical activity and health education opportunities using serious games in school.</p><p>Originality/value</p><p>The knowledge gained provides gamification concepts and combinations of different technological applications to increase health and learning, as well as motivational aspects suggested by the schoolchildren. The findings are discussed with health promotion and health education in mind.</p>
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8.
  • Nugent, Chris, et al. (författare)
  • An initiative for the creation of open datasets within pervasive healthcare
  • 2016
  • Ingår i: Proceedings of the 10th EAI International Conference onPervasive Computing Technologies for Healthcare : 16-19 May 2016, Cancun, Mexico. - ICST, the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. - 9781631900518 ; s. 318-321
  • Konferensbidrag (refereegranskat)abstract
    • <p>In this paper issues surrounding the collection, annotation, management and sharing of data gathered from pervasive health systems are presented. The overarching motivation for this work has been to provide an approach whereby annotated data sets can be made readily accessible to the research community in an effort to assist the advancement of the state-of-the-art in activity recognition and behavioural analysis using pervasive health systems. Recommendations of how this can be made a reality are presented in addition to the initial steps which have been taken to facilitate such an initiative involving the definition of common formats for data storage and a common set of tools for data processing and visualization.</p>
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9.
  • Synnes, Kåre, 1969-, et al. (författare)
  • H2Al - The Human Health and Activity Laboratory
  • 2018
  • Ingår i: REMIND. - MDPI.
  • Konferensbidrag (refereegranskat)abstract
    • <p>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.</p>
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