Malmö University Publications
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  • 1.
    Akhoundian, Maedeh
    et al.
    Malmö högskola, Faculty of Health and Society (HS), Department of Biomedical Science (BMV).
    Ruter, Axel
    Malmö högskola, Faculty of Health and Society (HS), Department of Biomedical Science (BMV).
    Shinde, Sudhirkumar
    Malmö högskola, Faculty of Health and Society (HS), Department of Biomedical Science (BMV).
    Ultratrace Detection of Histamine Using a Molecularly-Imprinted Polymer-Based Voltammetric Sensor2017In: Sensors, E-ISSN 1424-8220, Vol. 17, no 3Article in journal (Refereed)
    Abstract [en]

    Rapid and cost-effective analysis of histamine, in food, environmental, and diagnostics research has been of interest recently. However, for certain applications, the already-existing biological receptor-based sensing methods have usage limits in terms of stability and costs. As a result, robust and cost-effective imprinted polymeric receptors can be the best alternative. In the present work, molecularly-imprinted polymers (MIPs) for histamine were synthesized using methacrylic acid in chloroform and acetonitrile as two different porogens. The binding affinity of the MIPs with histamine was evaluated in aqueous media. MIPs synthesized in chloroform displayed better imprinting properties for histamine. We demonstrate here histamine MIPs incorporated into a carbon paste (CP) electrode as a MIP-CP electrode sensor platforms for detection of histamine. This simple sensor format allows accurate determination of histamine in the sub-nanomolar range using an electrochemical method. The sensor exhibited two distinct linear response ranges of 1 x 10(-10)-7 x 10(-9) M and 7 x 10(-9)-4 x 10(-7) M. The detection limit of the sensor was calculated equal to 7.4 x 10(-11) M. The specificity of the proposed electrode for histamine is demonstrated by using the analogous molecules and other neurotransmitters such as serotonin, dopamine, etc. The MIP sensor was investigated with success on spiked serum samples. The easy preparation, simple procedure, and low production cost make the MIP sensor attractive for selective and sensitive detection of analytes, even in less-equipped laboratories with minimal training.

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  • 2.
    Alkhabbas, Fahed
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Alsadi, Mohammed
    Department of Computer Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway.
    Alawadi, Sadi
    Department of Information Technology, Uppsala University, 75105 Uppsala, Sweden; Center for Applied Intelligent Systems Research, School of Information Technology, Halmstad University, 30118 Halmstad, Sweden.
    Awaysheh, Feras M
    Institute of Computer Science, Delta Research Centre, University of Tartu, 51009 Tartu, Estonia.
    Kebande, Victor R.
    Department of Computer Science (DBlekinge Institute of Technology, 37179 Karlskrona, Sweden.
    Moghaddam, Mahyar T
    The Maersk Mc-Kinney Moller Institute (MMMI), University of Southern Denmark, 5230 Odense, Denmark.
    ASSERT: A Blockchain-Based Architectural Approach for Engineering Secure Self-Adaptive IoT Systems.2022In: Sensors, E-ISSN 1424-8220, Vol. 22, no 18, article id 6842Article in journal (Refereed)
    Abstract [en]

    Internet of Things (IoT) systems are complex systems that can manage mission-critical, costly operations or the collection, storage, and processing of sensitive data. Therefore, security represents a primary concern that should be considered when engineering IoT systems. Additionally, several challenges need to be addressed, including the following ones. IoT systems' environments are dynamic and uncertain. For instance, IoT devices can be mobile or might run out of batteries, so they can become suddenly unavailable. To cope with such environments, IoT systems can be engineered as goal-driven and self-adaptive systems. A goal-driven IoT system is composed of a dynamic set of IoT devices and services that temporarily connect and cooperate to achieve a specific goal. Several approaches have been proposed to engineer goal-driven and self-adaptive IoT systems. However, none of the existing approaches enable goal-driven IoT systems to automatically detect security threats and autonomously adapt to mitigate them. Toward bridging these gaps, this paper proposes a distributed architectural Approach for engineering goal-driven IoT Systems that can autonomously SElf-adapt to secuRity Threats in their environments (ASSERT). ASSERT exploits techniques and adopts notions, such as agents, federated learning, feedback loops, and blockchain, for maintaining the systems' security and enhancing the trustworthiness of the adaptations they perform. The results of the experiments that we conducted to validate the approach's feasibility show that it performs and scales well when detecting security threats, performing autonomous security adaptations to mitigate the threats and enabling systems' constituents to learn about security threats in their environments collaboratively.

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  • 3.
    Bugeja, Joseph
    et al.
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Jacobsson, Andreas
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Davidsson, Paul
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    PRASH: A Framework for Privacy Risk Analysis of Smart Homes.2021In: Sensors, E-ISSN 1424-8220, Vol. 21, no 19, article id 6399Article in journal (Refereed)
    Abstract [en]

    Smart homes promise to improve the quality of life of residents. However, they collect vasts amounts of personal and sensitive data, making privacy protection critically important. We propose a framework, called PRASH, for modeling and analyzing the privacy risks of smart homes. It is composed of three modules: a system model, a threat model, and a set of privacy metrics, which together are used for calculating the privacy risk exposure of a smart home system. By representing a smart home through a formal specification, PRASH allows for early identification of threats, better planning for risk management scenarios, and mitigation of potential impacts caused by attacks before they compromise the lives of residents. To demonstrate the capabilities of PRASH, an executable version of the smart home system configuration was generated using the proposed formal specification, which was then analyzed to find potential attack paths while also mitigating the impacts of those attacks. Thereby, we add important contributions to the body of knowledge on the mitigations of threat agents violating the privacy of users in their homes. Overall, the use of PRASH will help residents to preserve their right to privacy in the face of the emerging challenges affecting smart homes.

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  • 4.
    Caramaschi, Sara
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Olsson, Carl Magnus
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Orchard, Elizabeth
    Oxford Univ Hosp NHS Fdn Trust, Oxford OX3 7JX, England..
    Molloy, Jackson
    Oxford Univ Hosp NHS Fdn Trust, Oxford OX3 7JX, England..
    Salvi, Dario
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Assessing the Effect of Data Quality on Distance Estimation in Smartphone-Based Outdoor 6MWT2024In: Sensors, E-ISSN 1424-8220, Vol. 24, no 8, article id 2632Article in journal (Refereed)
    Abstract [en]

    As a result of technological advancements, functional capacity assessments, such as the 6-minute walk test, can be performed remotely, at home and in the community. Current studies, however, tend to overlook the crucial aspect of data quality, often limiting their focus to idealised scenarios. Challenging conditions may arise when performing a test given the risk of collecting poor-quality GNSS signal, which can undermine the reliability of the results. This work shows the impact of applying filtering rules to avoid noisy samples in common algorithms that compute the walked distance from positioning data. Then, based on signal features, we assess the reliability of the distance estimation using logistic regression from the following two perspectives: error-based analysis, which relates to the estimated distance error, and user-based analysis, which distinguishes conventional from unconventional tests based on users' previous annotations. We highlight the impact of features associated with walked path irregularity and direction changes to establish data quality. We evaluate features within a binary classification task and reach an F1-score of 0.93 and an area under the curve of 0.97 for the user-based classification. Identifying unreliable tests is helpful to clinicians, who receive the recorded test results accompanied by quality assessments, and to patients, who can be given the opportunity to repeat tests classified as not following the instructions.

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  • 5.
    Dong, Yuji
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Wan, Kaiyu
    Yue, Yong
    A Semantic-Based Belief Network Construction Approach in IoT2020In: Sensors, E-ISSN 1424-8220, Vol. 20, no 20, article id E5747Article in journal (Refereed)
    Abstract [en]

    Uncertainty is intrinsic in most of the complex systems, especially when the systems have to interact with the physical environment; therefore, handling uncertainty is critical in the Internet of Things (IoT). In this paper, we propose a semantic-based approach to build the belief network in IoT systems to handle the uncertainties. Semantics is the functionality description of any system component. Semantic Match mechanisms can construct the appropriate structures to compare the consistency between different sources of data based on the same functionality. In the approach, we define the belief property of every system component and develop the related algorithms to update the belief value. Furthermore, the related mechanisms and algorithms for data fusion and fault detection based on the belief property are described to explain how the approach works in the IoT systems. Several simulation experiments are used to evaluate the proposed approach, and the results indicate that the approach can work as expected. More accurate data are fused from the inaccurate devices and the fault in one node is automatically detected.

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  • 6.
    Engström, Jimmy
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP). Sony Europe BV, S-22362 Lund, Sweden..
    Jevinger, Åse
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Olsson, Carl Magnus
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Persson, Jan A.
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Some Design Considerations in Passive Indoor Positioning Systems2023In: Sensors, E-ISSN 1424-8220, Vol. 23, no 12, article id 5684Article in journal (Refereed)
    Abstract [en]

    User location is becoming an increasingly common and important feature for a wide range of services. Smartphone owners increasingly use location-based services, as service providers add context-enhanced functionality such as car-driving routes, COVID-19 tracking, crowdedness indicators, and suggestions for nearby points of interest. However, positioning a user indoors is still problematic due to the fading of the radio signal caused by multipath and shadowing, where both have complex dependencies on the indoor environment. Location fingerprinting is a common positioning method where Radio Signal Strength (RSS) measurements are compared to a reference database of previously stored RSS values. Due to the size of the reference databases, these are often stored in the cloud. However, server-side positioning computations make preserving the user's privacy problematic. Given the assumption that a user does not want to communicate his/her location, we pose the question of whether a passive system with client-side computations can substitute fingerprinting-based systems, which commonly use active communication with a server. We compared two passive indoor location systems based on multilateration and sensor fusion using an Unscented Kalman Filter (UKF) with fingerprinting and show how these may provide accurate indoor positioning without compromising the user's privacy in a busy office environment.

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  • 7.
    Falk, Magnus
    et al.
    Malmö University, Faculty of Health and Society (HS), Department of Biomedical Science (BMV). Malmö University, Biofilms Research Center for Biointerfaces.
    Nilsson, Emelie J.
    Malmö University, Faculty of Health and Society (HS), Department of Biomedical Science (BMV). Malmö University, Biofilms Research Center for Biointerfaces.
    Cirovic, Stefan
    Malmö University, Faculty of Health and Society (HS), Department of Biomedical Science (BMV). Malmö University, Biofilms Research Center for Biointerfaces.
    Tudosoiu, Bogdan
    Covercast AB, Drottensgatan 4, 222 23 Lund, Sweden.
    Shleev, Sergey
    Malmö University, Faculty of Health and Society (HS), Department of Biomedical Science (BMV). Malmö University, Biofilms Research Center for Biointerfaces.
    Wearable Electronic Tongue for Non-Invasive Assessment of Human Sweat2021In: Sensors, E-ISSN 1424-8220, Vol. 21, no 21, article id 7311Article in journal (Refereed)
    Abstract [en]

    Sweat is a promising biofluid in allowing for non-invasive sampling. Here, we investigate the use of a voltammetric electronic tongue, combining different metal electrodes, for the purpose of non-invasive sample assessment, specifically focusing on sweat. A wearable electronic tongue is presented by incorporating metal electrodes on a flexible circuit board and used to non-invasively monitor sweat on the body. The data obtained from the measurements were treated by multivariate data processing. Using principal component analysis to analyze the data collected by the wearable electronic tongue enabled differentiation of sweat samples of different chemical composition, and when combined with 1H-NMR sample differentiation could be attributed to changing analyte concentrations.

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  • 8.
    Falk, Magnus
    et al.
    Malmö University, Faculty of Health and Society (HS), Department of Biomedical Science (BMV). Malmö University, Biofilms Research Center for Biointerfaces.
    Psotta, Carolin
    Malmö University, Faculty of Health and Society (HS), Department of Biomedical Science (BMV). Malmö University, Biofilms Research Center for Biointerfaces. Aptusens AB.
    Cirovic, Stefan
    Malmö University, Faculty of Health and Society (HS), Department of Biomedical Science (BMV). Malmö University, Biofilms Research Center for Biointerfaces.
    Shleev, Sergey
    Malmö University, Faculty of Health and Society (HS), Department of Biomedical Science (BMV). Malmö University, Biofilms Research Center for Biointerfaces. Aptusens AB.
    Non-Invasive Electrochemical Biosensors Operating in Human Physiological Fluids2020In: Sensors, E-ISSN 1424-8220, Vol. 20, no 21, p. 1-28, article id 6352Article, review/survey (Refereed)
    Abstract [en]

    Non-invasive healthcare technologies are an important part of research and development nowadays due to the low cost and convenience offered to both healthcare receivers and providers. This work overviews the recent advances in the field of non-invasive electrochemical biosensors operating in secreted human physiological fluids, viz. tears, sweat, saliva, and urine. Described electrochemical devices are based on different electrochemical techniques, viz. amperometry, coulometry, cyclic voltammetry, and impedance spectroscopy. Challenges that confront researchers in this exciting area and key requirements for biodevices are discussed. It is concluded that the field of non-invasive sensing of biomarkers in bodily fluid is highly convoluted. Nonetheless, if the drawbacks are appropriately addressed, and the pitfalls are adroitly circumvented, the approach will most certainly disrupt current clinical and self-monitoring practices.

  • 9.
    Hernández, Aura Rocio
    et al.
    Malmö University, Faculty of Health and Society (HS), Department of Biomedical Science (BMV). Malmö University, Biofilms Research Center for Biointerfaces. Department of Pharmacy, Universidad Nacional de Colombia, Bogota 1101, Colombia.
    Vallejo, Bibiana
    Ruzgas, Tautgirdas
    Malmö University, Faculty of Health and Society (HS), Department of Biomedical Science (BMV). Malmö University, Biofilms Research Center for Biointerfaces.
    Björklund, Sebastian
    Malmö University, Faculty of Health and Society (HS), Department of Biomedical Science (BMV). Malmö University, Biofilms Research Center for Biointerfaces.
    The Effect of UVB Irradiation and Oxidative Stress on the Skin Barrier: A New Method to Evaluate Sun Protection Factor Based on Electrical Impedance Spectroscopy2019In: Sensors, E-ISSN 1424-8220, Vol. 19, no 10, article id 2376Article in journal (Refereed)
    Abstract [en]

    Sunlight is vital for several biochemical processes of the skin organ. However, acute or chronic exposure to ultraviolet radiation (UVR) has several harmful effects on the skin structure and function, especially in the case of the failing function of antioxidative enzymes, which may lead to substantial tissue damage due to the increased presence of reactive oxygen species (ROS). The aim of this work was to investigate the combined effect of ultraviolet B (UVB) irradiation and oxidative stress on the skin barrier integrity. For this, we employed electrical impedance spectroscopy (EIS) to characterize changes of the electrical properties of excised pig skin membranes after various exposure conditions of UVB irradiation, oxidative stress, and the inhibition of antioxidative enzymatic processes. The oxidative stress was regulated by adding hydrogen peroxide (HO) as a source of ROS, while sodium azide (NaN) was used as an inhibitor of the antioxidative enzyme catalase, which is naturally present throughout the epidermis. By screening for the combined effect of UVB and oxidative stress on the skin membrane electrical properties, we developed a new protocol for evaluating these parameters in a simple in vitro setup. Strikingly, the results show that exposure to extreme UVB irradiation does not affect the skin membrane resistance, implying that the skin barrier remains macroscopically intact. Likewise, exposure to only oxidative stress conditions, without UVB irradiation, does not affect the skin membrane resistance. In contrast to these observations, the combination of UVB irradiation and oxidative stress conditions results in a drastic decrease of the skin membrane resistance, indicating that the integrity of the skin barrier is compromised. Further, the skin membrane effective capacitance remained more or less unaffected by UVB exposure, irrespective of simultaneous exposure of oxidative stress. The EIS results were concluded to be associated with clear signs of macroscopic tissue damage of the epidermis as visualized with microscopy after exposure to UVB irradiation under oxidative stress conditions. Finally, the novel methodology was tested by performing an assessment of cosmetic sunscreen formulations with varying sun protection factor (SPF), with an overall successful outcome, showing good correlation between SPF value and protection capacity in terms of skin resistance change. The results from this study allow for the development of new skin sensors based on EIS for the detection of skin tissue damage from exposure to UVB irradiation and oxidative stress and provide a new, more comprehensive methodology, taking into account both the influence of UVB irradiation and oxidative stress, for in vitro determination of SPF in cosmetic formulations.

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  • 10.
    Khoshkangini, Reza
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP). Halmstad Univ, Ctr Appl Intelligent Syst Res CAISR, S-30118 Halmstad, Sweden..
    Tajgardan, Mohsen
    Qom Univ Technol, Fac Elect & Comp Engn, Qom 151937195, Iran..
    Lundström, Jens
    Halmstad Univ, Ctr Appl Intelligent Syst Res CAISR, S-30118 Halmstad, Sweden..
    Rabbani, Mahdi
    Univ New Brunswick UNB, Canadian Inst Cybersecur CIC, Fredericton, NB E3B 9W4, Canada..
    Tegnered, Daniel
    Volvo Grp Connected Solut, S-41756 Gothenburg, Sweden..
    A Snapshot-Stacked Ensemble and Optimization Approach for Vehicle Breakdown Prediction2023In: Sensors, E-ISSN 1424-8220, Vol. 23, no 12, article id 5621Article in journal (Refereed)
    Abstract [en]

    Predicting breakdowns is becoming one of the main goals for vehicle manufacturers so as to better allocate resources, and to reduce costs and safety issues. At the core of the utilization of vehicle sensors is the fact that early detection of anomalies facilitates the prediction of potential breakdown issues, which, if otherwise undetected, could lead to breakdowns and warranty claims. However, the making of such predictions is too complex a challenge to solve using simple predictive models. The strength of heuristic optimization techniques in solving np-hard problems, and the recent success of ensemble approaches to various modeling problems, motivated us to investigate a hybrid optimization- and ensemble-based approach to tackle the complex task. In this study, we propose a snapshot-stacked ensemble deep neural network (SSED) approach to predict vehicle claims (in this study, we refer to a claim as being a breakdown or a fault) by considering vehicle operational life records. The approach includes three main modules: Data pre-processing, Dimensionality Reduction, and Ensemble Learning. The first module is developed to run a set of practices to integrate various sources of data, extract hidden information and segment the data into different time windows. In the second module, the most informative measurements to represent vehicle usage are selected through an adapted heuristic optimization approach. Finally, in the last module, the ensemble machine learning approach utilizes the selected measurements to map the vehicle usage to the breakdowns for the prediction. The proposed approach integrates, and uses, the following two sources of data, collected from thousands of heavy-duty trucks: Logged Vehicle Data (LVD) and Warranty Claim Data (WCD). The experimental results confirm the proposed system's effectiveness in predicting vehicle breakdowns. By adapting the optimization and snapshot-stacked ensemble deep networks, we demonstrate how sensor data, in the form of vehicle usage history, contributes to claim predictions. The experimental evaluation of the system on other application domains also indicated the generality of the proposed approach.

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  • 11.
    Kong, Tianjiao
    et al.
    College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; Key Laboratory of Underwater Acoustic Signal Processing, Ministry of Education, Southeast University, Nanjing 210096, China.
    Shao, Jie
    College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; Key Laboratory of Underwater Acoustic Signal Processing, Ministry of Education, Southeast University, Nanjing 210096, China.
    Hu, Jiuyuan
    College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; Key Laboratory of Underwater Acoustic Signal Processing, Ministry of Education, Southeast University, Nanjing 210096, China.
    Yang, Xin
    College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; Key Laboratory of Underwater Acoustic Signal Processing, Ministry of Education, Southeast University, Nanjing 210096, China.
    Yang, Shiyiling
    College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; Key Laboratory of Underwater Acoustic Signal Processing, Ministry of Education, Southeast University, Nanjing 210096, China.
    Malekian, Reza
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    EEG-Based Emotion Recognition Using an Improved Weighted Horizontal Visibility Graph2021In: Sensors, E-ISSN 1424-8220, Vol. 21, no 5, article id 1870Article in journal (Refereed)
    Abstract [en]

    Emotion recognition, as a challenging and active research area, has received considerable awareness in recent years. In this study, an attempt was made to extract complex network features from electroencephalogram (EEG) signals for emotion recognition. We proposed a novel method of constructing forward weighted horizontal visibility graphs (FWHVG) and backward weighted horizontal visibility graphs (BWHVG) based on angle measurement. The two types of complex networks were used to extract network features. Then, the two feature matrices were fused into a single feature matrix to classify EEG signals. The average emotion recognition accuracies based on complex network features of proposed method in the valence and arousal dimension were 97.53% and 97.75%. The proposed method achieved classification accuracies of 98.12% and 98.06% for valence and arousal when combined with time-domain features.

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  • 12.
    Larpant, Nutcha
    et al.
    Biosensors and Bioanalytical Technology for Cells and Innovative Testing Device Research Unit, Chulalongkorn University, Bangkok 10330, Thailand.
    Kalambate, Pramod K
    Biosensors and Bioanalytical Technology for Cells and Innovative Testing Device Research Unit, Chulalongkorn University, Bangkok 10330, Thailand.
    Ruzgas, Tautgirdas
    Malmö University, Biofilms Research Center for Biointerfaces. Malmö University, Faculty of Health and Society (HS), Department of Biomedical Science (BMV).
    Laiwattanapaisal, Wanida
    Biosensors and Bioanalytical Technology for Cells and Innovative Testing Device Research Unit, Chulalongkorn University, Bangkok 10330, Thailand; Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok 10330, Thailand.
    Paper-Based Competitive Immunochromatography Coupled with an Enzyme-Modified Electrode to Enable the Wireless Monitoring and Electrochemical Sensing of Cotinine in Urine2021In: Sensors, E-ISSN 1424-8220, Vol. 21, no 5, article id 1659Article in journal (Refereed)
    Abstract [en]

    This paper proposes a combined strategy of using paper-based competitive immunochromatography and a near field communication (NFC) tag for wireless cotinine determination. The glucose oxidase labeled cotinine antibody specifically binds free cotinine in a sample, whereas the unoccupied antibody attached to BSA-cotinine at the test line on a lateral flow strip. The glucose oxidase on the strip and an assistant pad in the presence of glucose generated H2O2 and imposed the Ag oxidation on the modified electrode. This enabled monitoring of immunoreaction by either electrochemical measurement or wireless detection. Wireless sensing was realized for cotinine in the range of 100–1000 ng/mL (R2 = 0.96) in PBS medium. Undiluted urine samples from non-smokers exhibited an Ag-oxidation rate three times higher than the smoker’s urine samples. For 1:8 diluted urine samples (smokers), the proposed paper-based competitive immunochromatography coupled with an enzyme-modified electrode differentiated positive and negative samples and exhibited cotinine discrimination at levels higher than 12 ng/mL. This novel sensing platform can potentially be combined with a smartphone as a reader unit.

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  • 13.
    Psotta, Carolin
    et al.
    Malmö University, Faculty of Health and Society (HS), Department of Biomedical Science (BMV). Aptusens AB, S-29394 Kyrkhult, Sweden..
    Chaturvedi, Vivek
    Malmö University, Faculty of Health and Society (HS), Department of Biomedical Science (BMV). Malmö University, Biofilms Research Center for Biointerfaces.
    Gonzalez-Martinez, Juan F
    Malmö University, Faculty of Health and Society (HS), Department of Biomedical Science (BMV). Malmö University, Biofilms Research Center for Biointerfaces.
    Sotres, Javier
    Malmö University, Biofilms Research Center for Biointerfaces. Malmö University, Faculty of Health and Society (HS), Department of Biomedical Science (BMV).
    Falk, Magnus
    Malmö University, Faculty of Health and Society (HS), Department of Biomedical Science (BMV). Malmö University, Biofilms Research Center for Biointerfaces.
    Portable Prussian Blue-Based Sensor for Bacterial Detection in Urine2023In: Sensors, E-ISSN 1424-8220, Vol. 23, no 1, article id 388Article in journal (Refereed)
    Abstract [en]

    Bacterial infections can affect the skin, lungs, blood, and brain, and are among the leading causes of mortality globally. Early infection detection is critical in diagnosis and treatment but is a time- and work-consuming process taking several days, creating a hitherto unmet need to develop simple, rapid, and accurate methods for bacterial detection at the point of care. The most frequent type of bacterial infection is infection of the urinary tract. Here, we present a wireless-enabled, portable, potentiometric sensor for E. coli. E. coli was chosen as a model bacterium since it is the most common cause of urinary tract infections. The sensing principle is based on reduction of Prussian blue by the metabolic activity of the bacteria, detected by monitoring the potential of the sensor, transferring the sensor signal via Bluetooth, and recording the output on a laptop or a mobile phone. In sensing of bacteria in an artificial urine medium, E. coli was detected in similar to 4 h (237 +/- 19 min; n = 4) and in less than 0.5 h (21 +/- 7 min, n = 3) using initial E. coli concentrations of similar to 10(3) and 10(5) cells mL(-1), respectively, which is under or on the limit for classification of a urinary tract infection. Detection of E. coli was also demonstrated in authentic urine samples with bacteria concentration as low as 10(4) cells mL(-1), with a similar response recorded between urine samples collected from different volunteers as well as from morning and afternoon urine samples.

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  • 14.
    Shokrollahi, Azad
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Persson, Jan A.
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Malekian, Reza
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Sarkheyli-Hägele, Arezoo
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Karlsson, Fredrik
    Sony Network Commun, S-22362 Lund, Sweden..
    Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches2024In: Sensors, E-ISSN 1424-8220, Vol. 24, no 5, article id 1533Article, review/survey (Refereed)
    Abstract [en]

    Buildings are rapidly becoming more digitized, largely due to developments in the internet of things (IoT). This provides both opportunities and challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor these buildings' status effectively. This monitoring is essential for services that rely on information about the presence and activities of individuals within different areas of these buildings. Occupancy information (including people counting, occupancy detection, location tracking, and activity detection) plays a vital role in the management of smart buildings. In this article, we primarily focus on the use of passive infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to other sensors. Despite numerous literature reviews in the field of occupancy information, there is currently no literature review dedicated to occupancy information derived specifically from PIR sensors. Therefore, this review analyzes articles that specifically explore the application of PIR sensors for obtaining occupancy information. It provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, focusing on applications in people counting, activity detection, and localization (tracking and location). It consolidates findings from articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information. These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The article seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains.

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  • 15.
    Stefansson, Petter
    et al.
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Karlsson, Fredrik
    Sony Network Communications, 223 62 Lund, Sweden.
    Persson, Magnus
    Sony Network Communications, 223 62 Lund, Sweden.
    Olsson, Carl Magnus
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Synthetic generation of passive infrared motion sensor data using a game engine2021In: Sensors, E-ISSN 1424-8220, Vol. 21, no 23, article id 8078Article in journal (Refereed)
    Abstract [en]

    Quantifying the number of occupants in an indoor space is useful for a wide variety of applications. Attempts have been made at solving the task using passive infrared (PIR) motion sensor data together with supervised learning methods. Collecting a large labeled dataset containing both PIR motion sensor data and ground truth people count is however time-consuming, often requiring one hour of observation for each hour of data gathered. In this paper, a method is proposed for generating such data synthetically. A simulator is developed in the Unity game engine capable of producing synthetic PIR motion sensor data by detecting simulated occupants. The accuracy of the simulator is tested by replicating a real-world meeting room inside the simulator and conducting an experiment where a set of choreographed movements are performed in the simulated environment as well as the real room. In 34 out of 50 tested situations, the output from the simulated PIR sensors is comparable to the output from the real-world PIR sensors. The developed simulator is also used to study how a PIR sensor’s output changes depending on where in a room a motion is carried out. Through this, the relationship between sensor output and spatial position of a motion is discovered to be highly non-linear, which highlights some of the difficulties associated with mapping PIR data to occupancy count. 

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  • 16.
    Tegen, Agnes
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Davidsson, Paul
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Mihailescu, Radu-Casian
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Persson, Jan A.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Collaborative Sensing with Interactive Learning using Dynamic Intelligent Virtual Sensors2019In: Sensors, E-ISSN 1424-8220, Vol. 19, no 3, article id 477Article in journal (Refereed)
    Abstract [en]

    Although the availability of sensor data is becoming prevalent across many domains, it still remains a challenge to make sense of the sensor data in an efficient and effective manner in order to provide users with relevant services. The concept of virtual sensors provides a step towards this goal, however they are often used to denote homogeneous types of data, generally retrieved from a predetermined group of sensors. The DIVS (Dynamic Intelligent Virtual Sensors) concept was introduced in previous work to extend and generalize the notion of a virtual sensor to a dynamic setting with heterogenous sensors. This paper introduces a refined version of the DIVS concept by integrating an interactive machine learning mechanism, which enables the system to take input from both the user and the physical world. The paper empirically validates some of the properties of the DIVS concept. In particular, we are concerned with the distribution of different budget allocations for labelled data, as well as proactive labelling user strategies. We report on results suggesting that a relatively good accuracy can be achieved despite a limited budget in an environment with dynamic sensor availability, while proactive labeling ensures further improvements in performance.

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