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Hedemalm, E., Kor, A.-L., Hallberg, J., Andersson, K., Pattinson, C. & Chinnici, M. (2021). Application of Online Transportation Mode Recognition in Games. Applied Sciences, 11(19), Article ID 8901.
Open this publication in new window or tab >>Application of Online Transportation Mode Recognition in Games
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2021 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 11, no 19, article id 8901Article in journal (Refereed) Published
Abstract [en]

It is widely accepted that human activities largely contribute to global emissions and thus, greatly impact climate change. Awareness promotion and adoption of green transportation mode could make a difference in the long term. To achieve behavioural change, we investigate the use of a persuasive game utilising online transportation mode recognition to afford bonuses and penalties to users based on their daily choices of transportation mode. To facilitate an easy identification of transportation mode, classification predictive models are built based on accelerometer and gyroscope historical data. Preliminary results show that the classification true-positive rate for recognising 10 different transportation classes can reach up to 95% when using a historical set (66% without). Results also reveal that the random tree classification model is a viable choice compared to random forest in terms of sustainability. Qualitative studies of the trained classifiers and measurements of Android-device gravity also raise several issues that could be addressed in future work. This research work could be enhanced through acceleration normalisation to improve device and user ambiguity.

Place, publisher, year, edition, pages
MDPI, 2021
Keywords
accelerometer, android, gyroscope, history set, machine-learning algorithms, random tree, random forest, transportation mode recognition, green transportation
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-87239 (URN)10.3390/app11198901 (DOI)000726252700001 ()2-s2.0-85115816399 (Scopus ID)
Note

Validerad;2021;Nivå 2;2021-09-27 (beamah);

Forskningafinansiär: EU funded EMJMD PERCCOM (2012–2019)

Available from: 2021-09-27 Created: 2021-09-27 Last updated: 2023-09-05Bibliographically approved
Sanchez-Comas, A., Synnes, K. & Hallberg, J. (2020). Hardware for Recognition of Human Activities: A Review of Smart Home and AAL Related Technologies. Sensors, 20(15), Article ID 4227.
Open this publication in new window or tab >>Hardware for Recognition of Human Activities: A Review of Smart Home and AAL Related Technologies
2020 (English)In: Sensors, E-ISSN 1424-8220, Vol. 20, no 15, article id 4227Article, review/survey (Refereed) Published
Abstract [en]

Activity recognition (AR) from an applied perspective of ambient assisted living (AAL) and smart homes (SH) has become a subject of great interest. Promising a better quality of life, AR applied in contexts such as health, security, and energy consumption can lead to solutions capable of reaching even the people most in need. This study was strongly motivated because levels of development, deployment, and technology of AR solutions transferred to society and industry are based on software development, but also depend on the hardware devices used. The current paper identifies contributions to hardware uses for activity recognition through a scientific literature review in the Web of Science (WoS) database. This work found four dominant groups of technologies used for AR in SH and AAL—smartphones, wearables, video, and electronic components—and two emerging technologies: Wi-Fi and assistive robots. Many of these technologies overlap across many research works. Through bibliometric networks analysis, the present review identified some gaps and new potential combinations of technologies for advances in this emerging worldwide field and their uses. The review also relates the use of these six technologies in health conditions, health care, emotion recognition, occupancy, mobility, posture recognition, localization, fall detection, and generic activity recognition applications. The above can serve as a road map that allows readers to execute approachable projects and deploy applications in different socioeconomic contexts, and the possibility to establish networks with the community involved in this topic. This analysis shows that the research field in activity recognition accepts that specific goals cannot be achieved using one single hardware technology, but can be using joint solutions, this paper shows how such technology works in this regard.

Place, publisher, year, edition, pages
MDPI, 2020
Keywords
smart home, AAL, ambient assisted living, activity recognition, hardware, review
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-80485 (URN)10.3390/s20154227 (DOI)000559122700001 ()32751345 (PubMedID)2-s2.0-85088922937 (Scopus ID)
Projects
Remind Project
Funder
EU, Horizon 2020, 734355
Note

Validerad;2020;Nivå 2;2020-08-20 (alebob)

Available from: 2020-08-20 Created: 2020-08-20 Last updated: 2023-09-05Bibliographically approved
Cruciani, F., Nugent, C. D., Medina Quero, J., Cleland, I., McCullagh, P., Synnes, K. & Hallberg, J. (2020). Personalizing Activity Recognition with a Clustering based Semi-Population Approach. IEEE Access, 8, 207794-207804
Open this publication in new window or tab >>Personalizing Activity Recognition with a Clustering based Semi-Population Approach
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2020 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 207794-207804Article in journal (Refereed) Published
Abstract [en]

Smartphone-based approaches for Human Activity Recognition have become prevalent in recent years. Despite the amount of research undertaken in the field, issues such as cross-subject variability are still posing an obstacle to the deployment of solutions in large scale, free-living settings. Personalized methods (i.e. aiming to adapt a generic classifier to a specific target user) attempt to solve this problem. The lack of labeled data for training purposes, however, represents a major barrier. This is especially the case when taking into consideration that personalization generally requires labeled data to be user-specific. This paper presents a novel personalization method combining a semi-population based approach with user adaptation. Personalization is achieved through the following. Firstly, the proposed method identifies a subset of users from the available population as best candidates for initializing the classifier to the target user. Subsequently, a semi-population Neural Network classifier is trained using data from this subset of users. The classifier’s network weights are then updated using a small amount of labeled data from the target user subsequently implementing personalization. This approach was validated on a large publicly available dataset collected in a free-living scenario. The personalized approach using the proposed method has shown to improve the overall F-score to 74.4% compared to 70.9% when using a generic non-personalized approach. Results obtained, with statistical significance being confirmed on a set of 57 users, indicate that model initialization using the semi-population approach can reduce the amount of labeled data required for personalization. As such, the proposed method for model initialization could facilitate the real-world deployment of systems implementing personalization by reducing the amount of data needed for personalization.

Place, publisher, year, edition, pages
IEEE, 2020
Keywords
Free-living, Human Activity Recognition, Neural Networks, Personalized Machine Learning, Smartphones
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-81479 (URN)10.1109/ACCESS.2020.3038084 (DOI)000594445700001 ()2-s2.0-85097300434 (Scopus ID)
Note

Validerad;2020;Nivå 2;2020-12-03 (alebob)

Available from: 2020-11-20 Created: 2020-11-20 Last updated: 2023-09-05Bibliographically approved
Cruciani, F., Cleland, I., Nugent, C., McCullagh, P., Synnes, K. & Hallberg, J. (2018). Automatic annotation for human activity recognition in free living using a smartphone. Sensors, 18(7), Article ID 2203.
Open this publication in new window or tab >>Automatic annotation for human activity recognition in free living using a smartphone
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2018 (English)In: Sensors, E-ISSN 1424-8220, Vol. 18, no 7, article id 2203Article in journal (Refereed) Published
Abstract [en]

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).

Place, publisher, year, edition, pages
MDPI, 2018
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-70137 (URN)10.3390/s18072203 (DOI)000441334300225 ()29987218 (PubMedID)2-s2.0-85050029995 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-07-19 (inah)

Available from: 2018-07-19 Created: 2018-07-19 Last updated: 2023-09-05Bibliographically approved
Kostenius, C., Hallberg, J. & Lindqvist, A.-K. (2018). Gamification of health education: Schoolchildren’s participation in the development of a serious game to promote health and learning. Health Education, 118(4), 354-368
Open this publication in new window or tab >>Gamification of health education: Schoolchildren’s participation in the development of a serious game to promote health and learning
2018 (English)In: Health Education, ISSN 0965-4283, E-ISSN 1758-714X, Vol. 118, no 4, p. 354-368Article in journal (Refereed) Published
Abstract [en]

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2018
National Category
Other Health Sciences Physiotherapy Media and Communication Technology
Research subject
Health Science; Physiotherapy; Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-68485 (URN)10.1108/HE-10-2017-0055 (DOI)000433598700005 ()2-s2.0-85047988587 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-06-01 (svasva)

Available from: 2018-04-24 Created: 2018-04-24 Last updated: 2023-09-05Bibliographically approved
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: José Bravo, Oresti Baños (Ed.), Proceedings, 2018, UCAmI 2018: The 12th International Conference on Ubiquitous Computing and Ambient ‪Intelligence (UCAmI 2018). Paper presented at 12th International Conference on Ubiquitous Computing and Ambient ‪Intelligence (UCAmI 2018), 4-7 December, 2018, Punta Cana Dominican Republic. MDPI, 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: Proceedings, 2018, UCAmI 2018: The 12th International Conference on Ubiquitous Computing and Ambient ‪Intelligence (UCAmI 2018) / [ed] José Bravo, Oresti Baños, MDPI, 2018, 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
Series
Proceedings, ISSN 2504-3900, E-ISSN 2504-3900 ; 2(19)
Keywords
smart home environment lab, pervasive computing, ehealth, ambient assisted living
National Category
Occupational Therapy Media and Communication Technology
Research subject
Pervasive Mobile Computing; Occupational therapy
Identifiers
urn:nbn:se:ltu:diva-72988 (URN)10.3390/proceedings2191241 (DOI)
Conference
12th International Conference on Ubiquitous Computing and Ambient ‪Intelligence (UCAmI 2018), 4-7 December, 2018, Punta Cana Dominican Republic
Projects
REMIND
Funder
The Kempe Foundations
Available from: 2019-02-21 Created: 2019-02-21 Last updated: 2023-09-05Bibliographically approved
Cruciani, F., Cleland, I., Nugent, C., McCullagh, P., Synnes, K. & Hallberg, J. (2018). Personalized Online Training for Physical Activity monitoring using weak labels. In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops): . Paper presented at 2nd International Workshop on Annotation of useR Data for UbiquitOUs Systems (ARDUOUS 2018), Athens, Greece, March 19-23, 2018 (pp. 567-572). IEEE
Open this publication in new window or tab >>Personalized Online Training for Physical Activity monitoring using weak labels
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2018 (English)In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), IEEE, 2018, p. 567-572Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
data annotation, weakly supervised learning, smartphone activity recognition
National Category
Computer and Information Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-68146 (URN)10.1109/PERCOMW.2018.8480292 (DOI)2-s2.0-85050025511 (Scopus ID)978-1-5386-3227-7 (ISBN)
Conference
2nd International Workshop on Annotation of useR Data for UbiquitOUs Systems (ARDUOUS 2018), Athens, Greece, March 19-23, 2018
Available from: 2018-04-03 Created: 2018-04-03 Last updated: 2023-09-05Bibliographically approved
Kikhia, B., Stavropoulos, T. G., Meditskos, G., Kompatsiaris, I., Hallberg, J., Sävenstedt, S. & Melander, C. (2018). Utilizing ambient and wearable sensors to monitor sleep and stress for people with BPSD in nursing homes (ed.). Journal of Ambient Intelligence and Humanized Computing, 9(2), 261-273
Open this publication in new window or tab >>Utilizing ambient and wearable sensors to monitor sleep and stress for people with BPSD in nursing homes
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2018 (English)In: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, Vol. 9, no 2, p. 261-273Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2018
National Category
Media and Communication Technology Nursing
Research subject
Pervasive Mobile Computing; Nursing
Identifiers
urn:nbn:se:ltu:diva-9225 (URN)10.1007/s12652-015-0331-6 (DOI)000429249200005 ()2-s2.0-85017323557 (Scopus ID)7c97293a-e058-4224-b5bb-882faec2867e (Local ID)7c97293a-e058-4224-b5bb-882faec2867e (Archive number)7c97293a-e058-4224-b5bb-882faec2867e (OAI)
Note

Validerad;2018;Nivå 2;2018-04-04 (rokbeg)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2023-09-09Bibliographically 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: 2023-09-09Bibliographically approved
Hedemalm, E., Hallberg, J., Kor, A.-L., Andersson, K. & Pattinson, C. (2017). Promoting Green Transportation via Persuasive Games. In: Sustainable Ecological Engineering Design for Society (SEEDS): Conference Proceedings from the Third International Conference. Paper presented at International conference on Sustainable Ecological Engineering Design for Society (SEEDS), Leeds, United Kingdom, September 13–14, 2017 (pp. 259-267). LSIPublishing
Open this publication in new window or tab >>Promoting Green Transportation via Persuasive Games
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2017 (English)In: Sustainable Ecological Engineering Design for Society (SEEDS): Conference Proceedings from the Third International Conference, LSIPublishing , 2017, p. 259-267Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
LSIPublishing, 2017
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-64774 (URN)
Conference
International conference on Sustainable Ecological Engineering Design for Society (SEEDS), Leeds, United Kingdom, September 13–14, 2017
Note

ISBN för värdpublikation: 978-0-9955690-2-7

Available from: 2017-07-04 Created: 2017-07-04 Last updated: 2023-09-05Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3191-8335

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