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  • 1.
    Alkhabbas, Fahed
    et al.
    Malmö University, Malmo, Sweden; Malmö University, Malmo, Sweden.
    Alsadi, Mohammed
    Norwegian University Of Science And Technology, Trondheim, Norway.
    Alawadi, Sadi
    Halmstad University, School of Information Technology. Uppsala University, Uppsala, Sweden.
    Awaysheh, Feras M.
    University Of Tartu, Tartu, Estonia.
    Kebande, Victor R.
    Blekinge Institute Of Technology, Karlskrona, Sweden.
    Moghaddam, Mahyar T.
    University Of Southern Denmark, Odense, Denmark.
    ASSERT: A Blockchain-Based Architectural Approach for Engineering Secure Self-Adaptive IoT Systems2022In: 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. © 2022 by the authors.

  • 2.
    Chen, Kunru
    et al.
    Halmstad University, School of Information Technology.
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Technology.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology.
    Pashami, Sepideh
    Halmstad University, School of Information Technology.
    Johansson, Emilia
    Toyota Material Handling Manufacturing Sweden AB, Mjölby, Sweden.
    Sternelöv, Gustav
    Toyota Material Handling Manufacturing Sweden AB, Mjölby, Sweden.
    Semi-Supervised Learning for Forklift Activity Recognition from Controller Area Network (CAN) Signals2022In: Sensors, E-ISSN 1424-8220, Vol. 22, no 11, article id 4170Article in journal (Refereed)
    Abstract [en]

    Machine Activity Recognition (MAR) can be used to monitor manufacturing processes and find bottlenecks and potential for improvement in production. Several interesting results on MAR techniques have been produced in the last decade, but mostly on construction equipment. Forklift trucks, which are ubiquitous and highly important industrial machines, have been missing from the MAR research. This paper presents a data-driven method for forklift activity recognition that uses Controller Area Network (CAN) signals and semi-supervised learning (SSL). The SSL enables the utilization of large quantities of unlabeled operation data to build better classifiers; after a two-step post-processing, the recognition results achieve balanced accuracy of 88% for driving activities and 95% for load-handling activities on a hold-out data set. In terms of the Matthews correlation coefficient for five activity classes, the final score is 0.82, which is equal to the recognition results of two non-domain experts who use videos of the activities. A particular success is that context can be used to capture the transport of small weight loads that are not detected by the forklift’s built-in weight sensor. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

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  • 3.
    Fu, Ying
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Yager, Tom
    Institute of Solid State Physics, University of Latvia, Riga, Latvia.
    Chikvaidze, George
    Institute of Solid State Physics, University of Latvia, Riga, Latvia.
    Iyer, Srinivasan
    Senseair AB, Delsbo, Sweden.
    Wang, Qin
    RISE Research Institutes of Sweden AB, Kista, Sweden.
    Time-resolved FDTD and experimental FTIR study of gold micropatch arrays for wavelength-selective mid-infrared optical coupling2021In: Sensors, E-ISSN 1424-8220, Vol. 21, no 15, article id 5203Article in journal (Refereed)
    Abstract [en]

    Infrared radiation reflection and transmission of a single layer of gold micropatch two-dimensional arrays, of patch length similar to 1.0 mu m and width similar to 0.2 mu m, have been carefully studied by a finite-difference time-domain (FDTD) method, and Fourier-transform infrared spectroscopy (FTIR). Through precision design of the micropatch array structure geometry, we achieve a significantly enhanced reflectance (85%), a substantial diffraction (10%), and a much reduced transmittance (5%) for an array of only 15% surface metal coverage. This results in an efficient far-field optical coupling with promising practical implications for efficient mid-infrared photodetectors. Most importantly we find that the propagating electromagnetic fields are transiently concentrated around the gold micropatch array in a time duration of tens of ns, providing us with a novel efficient near-field optical coupling. © 2021 by the authors.

  • 4.
    Hettiarachchi, Pasan
    et al.
    Department of Medical Sciences, Occupational and Environmental Medicine, Uppsala University, Uppsala, Sweden.
    Aili, Katarina
    Halmstad University, School of Health and Welfare, Centre of Research on Welfare, Health and Sport (CVHI), Health and Sport. Spenshult Research and Development Center, Halmstad, Sweden.
    Holtermann, Andreas
    National Research Centre for the Working Environment, Copenhagen, Denmark; Department of Sport Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark.
    Stamatakis, Emmanuel
    Charles Perkins Centre, School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia.
    Svartengren, Magnus
    Department of Medical Sciences, Occupational and Environmental Medicine, Uppsala University, Uppsala, Sweden.
    Palm, Peter
    Department of Medical Sciences, Occupational and Environmental Medicine, Uppsala University, Uppsala, Sweden.
    Validity of a Non-Proprietary Algorithm for Identifying Lying Down Using Raw Data from Thigh-Worn Triaxial Accelerometers2021In: Sensors, E-ISSN 1424-8220, Vol. 21, no 3, article id 904Article in journal (Refereed)
    Abstract [en]

    Body postural allocation during daily life is important for health, and can be assessed with thigh-worn accelerometers. An algorithm based on sedentary bouts from the proprietary ActivePAL software can detect lying down from a single thigh-worn accelerometer using rotations of the thigh. However, it is not usable across brands of accelerometers. This algorithm has the potential to be refined. Aim: To refine and assess the validity of an algorithm to detect lying down from raw data of thigh-worn accelerometers. Axivity-AX3 accelerometers were placed on the thigh and upper back (reference) on adults in a development dataset (n = 50) and a validation dataset (n = 47) for 7 days. Sedentary time from the open Acti4-algorithm was used as input to the algorithm. In addition to the thigh-rotation criterion in the existing algorithm, two criteria based on standard deviation of acceleration and a time duration criterion of sedentary bouts were added. The mean difference (95% agreement-limits) between the total identified lying time/day, between the refined algorithm and the reference was +2.9 (-135,141) min in the development dataset and +6.5 (-145,159) min in the validation dataset. The refined algorithm can be used to estimate lying time in studies using different accelerometer brands. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

  • 5.
    Khan, Taha
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Lundgren, Lina
    Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS). Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Järpe, Eric
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Olsson, M. Charlotte
    Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS).
    Wiberg, Pelle
    Raytelligence AB, Halmstad, Sweden.
    A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation2019In: Sensors, E-ISSN 1424-8220, Vol. 19, no 21, article id 4729Article in journal (Refereed)
    Abstract [en]

    Blood lactate accumulation is a crucial fatigue indicator during sports training. Previous studies have predicted cycling fatigue using surface-electromyography (sEMG) to non-invasively estimate lactate concentration in blood. This study used sEMG to predict muscle fatigue while running and proposes a novel method for the automatic classification of running fatigue based on sEMG. Data were acquired from 12 runners during an incremental treadmill running-test using sEMG sensors placed on the vastus-lateralis, vastus-medialis, biceps-femoris, semitendinosus, and gastrocnemius muscles of the right and left legs. Blood lactate samples of each runner were collected every two minutes during the test. A change-point segmentation algorithm labeled each sample with a class of fatigue level as (1) aerobic, (2) anaerobic, or (3) recovery. Three separate random forest models were trained to classify fatigue using 36 frequency, 51 time-domain, and 36 time-event sEMG features. The models were optimized using a forward sequential feature elimination algorithm. Results showed that the random forest trained using distributive power frequency of the sEMG signal of the vastus-lateralis muscle alone could classify fatigue with high accuracy. Importantly for this feature, group-mean ranks were significantly different (p < 0.01) between fatigue classes. Findings support using this model for monitoring fatigue levels during running. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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  • 6.
    Khoshkangini, Reza
    et al.
    Halmstad University, School of Information Technology. Malmö University, Malmo, Sweden.
    Tajgardan, Mohsen
    Qom University Of Technology, Qom, Iran.
    Lundström, Jens
    Halmstad University, School of Information Technology.
    Rabbani, Mahdi
    Canadian Institute For Cybersecurity, Fredericton, Canada.
    Tegnered, Daniel
    Volvo Group, 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. © 2023 by the authors.

  • 7.
    Lampoltshammer, Thomas J.
    et al.
    School of Information Technology and Systems Management, Salzburg University of Applied Sciences, Puch/Salzburg, Austria.
    Pignaton de Freitas, Edison
    Department of Applied Computing, Federal University of Santa Maria, Santa Maria, Brazil.
    Nowotny, Thomas
    School of Information Technology and Systems Management, Salzburg University of Applied Sciences, Puch/Salzburg, Austria.
    Plank, Stefan
    School of Information Technology and Systems Management, Salzburg University of Applied Sciences, Puch/Salzburg, Austria.
    Carvalho Lustosa da Costa, João Paulo
    Laboratory of Array Signal Processing, Department of Electrical Engineering, University of Brasilia, Campus Universitário Darcy Ribeiro, Brasília, Brazil.
    Larsson, Tony
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Heistracher, Thomas
    School of Information Technology and Systems Management, Salzburg University of Applied Sciences, Puch/Salzburg, Austria.
    Use of Local Intelligence to Reduce Energy Consumption of Wireless Sensor Nodes in Elderly Health Monitoring Systems2014In: Sensors, E-ISSN 1424-8220, Vol. 14, no 3, p. 4932-4947Article in journal (Refereed)
    Abstract [en]

    The percentage of elderly people in European countries is increasing. Such conjuncture affects socio-economic structures and creates demands for resourceful solutions, such as Ambient Assisted Living (AAL), which is a possible methodology to foster health care for elderly people. In this context, sensor-based devices play a leading role in surveying, e.g., health conditions of elderly people, to alert care personnel in case of an incident. However, the adoption of such devices strongly depends on the comfort of wearing the devices. In most cases, the bottleneck is the battery lifetime, which impacts the effectiveness of the system. In this paper we propose an approach to reduce the energy consumption of sensors’ by use of local sensors’ intelligence. By increasing the intelligence of the sensor node, a substantial decrease in the necessary communication payload can be achieved. The results show a significant potential to preserve energy and decrease the actual size of the sensor device units. © 2014 by the authors; licensee MDPI, Basel, Switzerland.

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  • 8.
    Muhammad, Naveed
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Åstrand, Björn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Intention Estimation Using Set of Reference Trajectories as Behaviour Model2018In: Sensors, E-ISSN 1424-8220, Vol. 18, no 12, article id 4423Article in journal (Refereed)
    Abstract [en]

    Autonomous robotic systems operating in the vicinity of other agents, such as humans, manually driven vehicles and other robots, can model the behaviour and estimate intentions of the other agents to enhance efficiency of their operation, while preserving safety. We propose a data-driven approach to model the behaviour of other agents, which is based on a set of trajectories navigated by other agents. Then, to evaluate the proposed behaviour modelling approach, we propose and compare two methods for agent intention estimation based on: (i) particle filtering; and (ii) decision trees. The proposed methods were validated using three datasets that consist of real-world bicycle and car trajectories in two different scenarios, at a roundabout and at a t-junction with a pedestrian crossing. The results validate the utility of the data-driven behaviour model, and show that decision-tree based intention estimation works better on a binary-class problem, whereas the particle-filter based technique performs better on a multi-class problem, such as the roundabout, where the method yielded an average gain of 14.88 m for correct intention estimation locations compared to the decision-tree based method. © 2018 by the authors

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  • 9.
    Muhammad, Naveed
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Åstrand, Björn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Predicting Agent Behaviour and State for Applications in a Roundabout-Scenario Autonomous Driving2019In: Sensors, E-ISSN 1424-8220, Vol. 19, no 19, article id 4279Article in journal (Refereed)
    Abstract [en]

    As human drivers, we instinctively employ our understanding of other road users' behaviour for enhanced efficiency of our drive and safety of the traffic. In recent years, different aspects of assisted and autonomous driving have gotten a lot of attention from the research and industrial community, including the aspects of behaviour modelling and prediction of future state. In this paper, we address the problem of modelling and predicting agent behaviour and state in a roundabout traffic scenario. We present three ways of modelling traffic in a roundabout based on: (i) the roundabout geometry; (ii) mean path taken by vehicles inside the roundabout; and (iii) a set of reference trajectories traversed by vehicles inside the roundabout. The roundabout models are compared in terms of exit-direction classification and state (i.e., position inside the roundabout) prediction of query vehicles inside the roundabout. The exit-direction classification and state prediction are based on a particle-filter classifier algorithm. The results show that the roundabout model based on set of reference trajectories is better suited for both the exit-direction and state prediction.

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  • 10.
    Orand, Abbas
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Erdal Aksoy, Eren
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Miyasaka, Hiroyuki
    Department of Rehabilitation, Fujita Health University, Nanakuri Memorial Hospital, Tsu, Japan.
    Weeks Levy, Carolyn
    Schools of Mechatronics Systems Engineering and Engineering Science, Simon Fraser University, Surrey, Canada.
    Zhang, Xin
    Schools of Mechatronics Systems Engineering and Engineering Science, Simon Fraser University, Surrey, Canada.
    Menon, Carlo
    Schools of Mechatronics Systems Engineering and Engineering Science, Simon Fraser University, Surrey, Canada.
    Bilateral Tactile Feedback-Enabled Training for Stroke Survivors Using Microsoft KinectTM2019In: Sensors, E-ISSN 1424-8220, Vol. 19, no 16, article id 3474Article in journal (Refereed)
    Abstract [en]

    Rehabilitation and mobility training of post-stroke patients is crucial for their functional recovery. While traditional methods can still help patients, new rehabilitation and mobility training methods are necessary to facilitate better recovery at lower costs. In this work, our objective was to design and develop a rehabilitation training system targeting the functional recovery ofpost-stroke users with high efficiency. To accomplish this goal, we applied a bilateral training method, which proved to be effective in enhancing motor recovery using tactile feedback for the training. One participant with hemiparesis underwent six weeks of training. Two protocols, “contralater alarm matching” and “both arms moving together”, were carried out by the participant. Each ofthe protocols consisted of “shoulder abduction” and “shoulder flexion” at angles close to 30 and 60 degrees. The participant carried out 15 repetitions at each angle for each task. For example, in the“contralateral arm matching” protocol, the unaffected arm of the participant was set to an angle close to 30 degrees. He was then requested to keep the unaffected arm at the specified angle while trying to match the position with the affected arm. Whenever the two arms matched, a vibration was given on both brachialis muscles. For the “both arms moving together” protocol, the two arms were first set approximately to an angle of either 30 or 60 degrees. The participant was asked to return both arms to a relaxed position before moving both arms back to the remembered specified angle.The arm that was slower in moving to the specified angle received a vibration. We performed clinical assessments before, midway through, and after the training period using a Fugl-Meyer assessment (FMA), a Wolf motor function test (WMFT), and a proprioceptive assessment. For the assessments, two ipsilateral and contralateral arm matching tasks, each consisting of three movements (shoulder abduction, shoulder flexion, and elbow flexion), were used. Movements were performed at two angles, 30 and 60 degrees. For both tasks, the same procedure was used. For example, in the case of the ipsilateral arm matching task, an experimenter positioned the affected arm of the participant at 30 degrees of shoulder abduction. The participant was requested to keep the arm in that positionfor ~5 s before returning to a relaxed initial position. Then, after another ~5-s delay, the participant moved the affected arm back to the remembered position. An experimenter measured this shoulder abduction angle manually using a goniometer. The same procedure was repeated for the 60 degree angle and for the other two movements. We applied a low-cost Kinect to extract the participant’s body joint position data. Tactile feedback was given based on the arm position detected by the Kinect sensor. By using a Kinect sensor, we demonstrated the feasibility of the system for the training ofa post-stroke user. The proposed system can further be employed for self-training of patients at home. The results of the FMA, WMFT, and goniometer angle measurements showed improvements in several tasks, suggesting a positive effect of the training system and its feasibility for further application for stroke survivors’ rehabilitation. © 2019 by the authors.

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  • 11.
    Ortiz-Barrios, Miguel
    et al.
    Universidad De La Costa, Barranquilla, Colombia.
    Järpe, Eric
    Halmstad University, School of Information Technology.
    García-Constantino, Matías
    University Of Ulster, Coleraine, United Kingdom.
    Cleland, Ian
    University Of Ulster, Coleraine, United Kingdom.
    Nugent, Chris
    University Of Ulster, Coleraine, United Kingdom.
    Arias-Fonseca, Sebastián
    Universidad De La Costa, Barranquilla, Colombia.
    Jaramillo-Rueda, Natalia
    Universidad De La Costa, Barranquilla, Colombia.
    Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia Patients2022In: Sensors, E-ISSN 1424-8220, Vol. 22, no 14, article id 5410Article in journal (Refereed)
    Abstract [en]

    The accurate recognition of activities is fundamental for following up on the health progress of people with dementia (PwD), thereby supporting subsequent diagnosis and treatments. When monitoring the activities of daily living (ADLs), it is feasible to detect behaviour patterns, parse out the disease evolution, and consequently provide effective and timely assistance. However, this task is affected by uncertainties derived from the differences in smart home configurations and the way in which each person undertakes the ADLs. One adjacent pathway is to train a supervised classification algorithm using large-sized datasets; nonetheless, obtaining real-world data is costly and characterized by a challenging recruiting research process. The resulting activity data is then small and may not capture each person's intrinsic properties. Simulation approaches have risen as an alternative efficient choice, but synthetic data can be significantly dissimilar compared to real data. Hence, this paper proposes the application of Partial Least Squares Regression (PLSR) to approximate the real activity duration of various ADLs based on synthetic observations. First, the real activity duration of each ADL is initially contrasted with the one derived from an intelligent environment simulator. Following this, different PLSR models were evaluated for estimating real activity duration based on synthetic variables. A case study including eight ADLs was considered to validate the proposed approach. The results revealed that simulated and real observations are significantly different in some ADLs (p-value &lt; 0.05), nevertheless synthetic variables can be further modified to predict the real activity duration with high accuracy (R2(pred)&gt;90%). © 2022 by the authors.

  • 12.
    Ourique de Morais, Wagner
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Embedded Systems (CERES).
    Lundström, Jens
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Wickström, Nicholas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Active In-Database Processing to Support Ambient Assisted Living Systems2014In: Sensors, E-ISSN 1424-8220, Vol. 14, no 8, p. 14765-14785Article in journal (Refereed)
    Abstract [en]

    As an alternative to the existing software architectures that underpin the development of smart homes and ambient assisted living (AAL) systems, this work presents a database-centric architecture that takes advantage of active databases and in-database processing. Current platforms supporting AAL systems use database management systems (DBMSs) exclusively for data storage. Active databases employ database triggers to detect and react to events taking place inside or outside of the database. DBMSs can be extended with stored procedures and functions that enable in-database processing. This means that the data processing is integrated and performed within the DBMS. The feasibility and flexibility of the proposed approach were demonstrated with the implementation of three distinct AAL services. The active database was used to detect bed-exits and to discover common room transitions and deviations during the night. In-database machine learning methods were used to model early night behaviors. Consequently, active in-database processing avoids transferring sensitive data outside the database, and this improves performance, security and privacy. Furthermore, centralizing the computation into the DBMS facilitates code reuse, adaptation and maintenance. These are important system properties that take into account the evolving heterogeneity of users, their needs and the devices that are characteristic of smart homes and AAL systems. Therefore, DBMSs can provide capabilities to address requirements for scalability, security, privacy, dependability and personalization in applications of smart environments in healthcare.

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  • 13.
    Pedrollo, Guilherme
    et al.
    Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
    Aparecida Konzen, Andréa
    Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
    Ourique de Morais, Wagner
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pignaton de Freitas, Edison
    Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
    Using smart virtual-sensor nodes to improve the robustness of indoor localization systems2021In: Sensors, E-ISSN 1424-8220, Vol. 21, no 11, article id 3912Article in journal (Refereed)
    Abstract [en]

    Young, older, frail, and disabled individuals can require some form of monitoring or assistance, mainly when critical situations occur, such as falling and wandering. Healthcare facilities are increasingly interested in e-health systems that can detect and respond to emergencies on time. Indoor localization is an essential function in such e-health systems, and it typically relies on wireless sensor networks (WSN) composed of fixed and mobile nodes. Nodes in the network can become permanently or momentarily unavailable due to, for example, power failures, being out of range, and wrong placement. Consequently, unavailable sensors not providing data can compromise the system’s overall function. One approach to overcome the problem is to employ virtual sensors as replacements for unavailable sensors and generate synthetic but still realistic data. This paper investigated the viability of modelling and artificially reproducing the path of a monitored target tracked by a WSN with unavailable sensors. Particularly, the case with just a single sensor was explored. Based on the coordinates of the last measured positions by the unavailable node, a neural network was trained with 4 min of not very linear data to reproduce the behavior of a sensor that become unavailable for about 2 min. Such an approach provided reasonably successful results, especially for areas close to the room’s entrances and exits, which are critical for the security monitoring of patients in healthcare facilities. © 2021 by the authors.

  • 14.
    Pignaton de Freitas, Edison
    et al.
    Electrical Engineering Department, University of Brasília, Brazil.
    Heimfarth, Tales
    Department of Computer Science, Federal University of Lavras, CP 3037, Lavras 37200-000, Brazil.
    Vinel, Alexey
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Rech Wagner, Flavio
    Electrical Engineering Department and Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil .
    Pereira, Carlos Eduardo
    Electrical Engineering Department and Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil .
    Larsson, Tony
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Cooperation among wirelessly connected static and mobile sensor nodes for surveillance applications2013In: Sensors, E-ISSN 1424-8220, Vol. 13, no 10, p. 12903-12928Article in journal (Refereed)
    Abstract [en]

    This paper presents a bio-inspired networking strategy to support the cooperation between static sensors on the ground and mobile sensors in the air to perform surveillance missions in large areas. The goal of the proposal is to provide a low overhead in the communication among sensor nodes, while allocating the mobile sensors to perform sensing activities requested by the static ones. Simulations have shown that the strategy is efficient in maintaining low overhead and achiving the desired coordination. © 2013 by the authors; licensee MDPI, Basel, Switzerland.

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  • 15.
    Ražanskas, Petras
    et al.
    Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Kaunas University of Technology, Kaunas, Lithuania.
    Olsson, Charlotte
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS).
    Wiberg, Per-Arne
    Swedish Adrenaline, Halmstad, Sweden.
    Predicting Blood Lactate Concentration and Oxygen Uptake from sEMG Data during Fatiguing Cycling Exercise2015In: Sensors, E-ISSN 1424-8220, Vol. 15, no 8, p. 20480-20500Article in journal (Refereed)
    Abstract [en]

    This article presents a study of the relationship between electromyographic (EMG) signals from vastus lateralis, rectus femoris, biceps femoris and semitendinosus muscles, collected during fatiguing cycling exercises, and other physiological measurements, such as blood lactate concentration and oxygen consumption. In contrast to the usual practice of picking one particular characteristic of the signal, e.g., the median or mean frequency, multiple variables were used to obtain a thorough characterization of EMG signals in the spectral domain. Based on these variables, linear and non-linear (random forest) models were built to predict blood lactate concentration and oxygen consumption. The results showed that mean and median frequencies are sub-optimal choices for predicting these physiological quantities in dynamic exercises, as they did not exhibit significant changes over the course of our protocol and only weakly correlated with blood lactate concentration or oxygen uptake. Instead, the root mean square of the original signal and backward difference, as well as parameters describing the tails of the EMG power distribution were the most important variables for these models. Coefficients of determination ranging from R2 = 0:77 to R2 = 0:98 (for blood lactate) and from R2 = 0:81 to R2 = 0:97 (for oxygen uptake) were obtained when using random forest regressors.

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  • 16.
    Saeed, Nausheen
    et al.
    School of Technology and Business Studies, Dalarna University, Borlänge, Sweden.
    Nyberg, Roger G.
    School of Technology and Business Studies, Dalarna University, Borlänge, Sweden.
    Alam, Moudud
    School of Technology and Business Studies, Dalarna University, Borlänge, Sweden.
    Dougherty, Mark
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Jooma, Diala
    School of Technology and Business Studies, Dalarna University, Borlänge, Sweden.
    Rebreyend, Pascal
    School of Technology and Business Studies, Dalarna University, Borlänge, Sweden.
    Classification of the acoustics of loose gravel2021In: Sensors, E-ISSN 1424-8220, Vol. 21, no 14, article id 4944Article in journal (Refereed)
    Abstract [en]

    Road condition evaluation is a critical part of gravel road maintenance. One of the assessed parameters is the amount of loose gravel, as this determines the driving quality and safety. Loose gravel can cause tires to slip and the driver to lose control. An expert assesses the road conditions subjectively by looking at images and notes. This method is labor-inten-sive and subject to error in judgment; therefore, its reliability is questionable. Road management agencies look for automated and objective measurement systems. In this study, acoustic data on gravel hitting the bottom of a car was used. The connection between the acoustics and the condition of loose gravel on gravel roads was assessed. Traditional supervised learning algorithms and convolution neural network (CNN) were applied, and their performances are compared for the classification of loose gravel acoustics. The advantage of using a pre-trained CNN is that it selects relevant features for training. In addition, pre-trained networks offer the advantage of not requiring days of training or colossal training data. In supervised learning, the accuracy of the ensemble bagged tree algorithm for gravel and non-gravel sound classification was found to be 97.5%, whereas, in the case of deep learning, pre-trained network GoogLeNet accuracy was 97.91% for classifying spectrogram images of the gravel sounds. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

  • 17.
    Sarmadi, Hamid
    et al.
    Halmstad University, School of Information Technology.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology.
    Prytz, Rune
    Stratio Company, Lisbon, Portugal.
    Simão, Miguel
    Stratio Company, Lisbon, Portugal.
    Attention Horizon as a Predictor for the Fuel Consumption Rate of Drivers2022In: Sensors, E-ISSN 1424-8220, Vol. 22, no 6, article id 2301Article in journal (Refereed)
    Abstract [en]

    Understanding the operation of complex assets such heavy-duty vehicles is essential for improving the efficiency, sustainability, and safety of future industry. Specifically, reducing energy consumption of transportation is crucially important for fleet operators, due to the impact it has on decreasing energy costs and lowering greenhouse gas emissions. Drivers have a high influence on fuel usage. However, reliably estimating driver performance is challenging. This is a key component of many eco-driving tools used to train drivers. Some key aspects of good, or efficient, drivers include being more aware of the surroundings, adapting to the road situations, and anticipating likely developments of the traffic conditions. With the development of IoT technologies and possibility of collecting high-precision and high-frequency data, even such vague concepts can be qualitatively measured, or at least approximated. In this paper, we demonstrate how the driver’s degree of attention to the road can be automatically extracted from onboard sensor data. More specifically, our main contribution is introduction of a new metric, called attention horizon (AH); it can, fully automatically and based on readily-available IoT data, capture, differentiate, and evaluate a driver’s behavior as the vehicle approaches a red traffic light. We suggest that our measure encapsulates complex concepts such as driver’s “awareness” and “carefulness” in itself. This metric is extracted from the pedal positions in a 150 m trajectory just before stopping. We demonstrate that this metric is correlated with normalized fuel consumption rate (FCR) in the long term, making it a suitable tool for ranking and evaluating drivers. For example, over weekly periods we found a negative median correlation between AH and FCR with the absolute value of 0.156; while using monthly data, the value was 0.402. © 2022 by the authors.

  • 18.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Kaunas University of Technology, Kaunas, Lithuania.
    Vaiciukynas, Evaldas
    Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    Parker, James
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS).
    Olsson, M. Charlotte
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS).
    Electromyographic Patterns during Golf Swing: Activation Sequence Profiling and Prediction of Shot Effectiveness2016In: Sensors, E-ISSN 1424-8220, Vol. 16, no 4, article id 592Article in journal (Refereed)
    Abstract [en]

    This study analyzes muscle activity, recorded in an eight-channel electromyographic (EMG) signal stream, during the golf swing using a 7-iron club and exploits information extracted from EMG dynamics to predict the success of the resulting shot. Muscles of the arm and shoulder on both the left and right sides, namely flexor carpi radialis, extensor digitorum communis, rhomboideus and trapezius, are considered for 15 golf players (∼5 shots each). The method using Gaussian filtering is outlined for EMG onset time estimation in each channel and activation sequence profiling. Shots of each player revealed a persistent pattern of muscle activation. Profiles were plotted and insights with respect to player effectiveness were provided. Inspection of EMG dynamics revealed a pair of highest peaks in each channel as the hallmark of golf swing, and a custom application of peak detection for automatic extraction of swing segment was introduced. Various EMG features, encompassing 22 feature sets, were constructed. Feature sets were used individually and also in decision-level fusion for the prediction of shot effectiveness. The prediction of the target attribute, such as club head speed or ball carry distance, was investigated using random forest as the learner in detection and regression tasks. Detection evaluates the personal effectiveness of a shot with respect to the player-specific average, whereas regression estimates the value of target attribute, using EMG features as predictors. Fusion after decision optimization provided the best results: the equal error rate in detection was 24.3% for the speed and 31.7% for the distance; the mean absolute percentage error in regression was 3.2% for the speed and 6.4% for the distance. Proposed EMG feature sets were found to be useful, especially when used in combination. Rankings of feature sets indicated statistics for muscle activity in both the left and right body sides, correlation-based analysis of EMG dynamics and features derived from the properties of two highest peaks as important predictors of personal shot effectiveness. Activation sequence profiles helped in analyzing muscle orchestration during golf shot, exposing a specific avalanche pattern, but data from more players are needed for stronger conclusions. Results demonstrate that information arising from an EMG signal stream is useful for predicting golf shot success, in terms of club head speed and ball carry distance, with acceptable accuracy. Surface EMG data, collected with a goal to automatically evaluate golf player’s performance, enables wearable computing in the field of ambient intelligence and has potential to enhance exercising of a long carry distance drive.

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