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  • 1.
    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 Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS).
    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 Segmentatio2019In: Sensors, ISSN 1424-8220, 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.

  • 2.
    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, ISSN 1424-8220, 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/).

  • 3.
    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, ISSN 1424-8220, 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.

  • 4.
    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, ISSN 1424-8220, 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

  • 5.
    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, ISSN 1424-8220, 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.

  • 6.
    Orand, Abbas
    et al.
    Halmstad University, School of Information Technology.
    Aksoy, Eren
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Miyasaka, Hiroyuki
    Fujita Health University.
    Weeks Levy, Carolyn
    Simon Fraser University.
    Zhang, Xin
    Simon Fraser University.
    Bilateral tactile feedback-enabled training for stroke survivors using microsoft kinect2019In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 16, article id 3474Article in journal (Refereed)
    Abstract [en]

    Rehabilitation and mobility training of post-stroke patients is crucial for their functionalrecovery. While traditional methods can still help patients, new rehabilitation and mobility trainingmethods are necessary to facilitate better recovery at lower costs. In this work, our objectivewas 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, “contralateralarm 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 and60 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 angleclose to 30 degrees. He was then requested to keep the unaffected arm at the specified angle whiletrying to match the position with the affected arm. Whenever the two arms matched, a vibration wasgiven on both brachialis muscles. For the “both arms moving together” protocol, the two arms werefirst set approximately to an angle of either 30 or 60 degrees. The participant was asked to returnboth 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 clinicalassessments 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 (shoulderabduction, shoulder flexion, and elbow flexion), were used. Movements were performed at twoangles, 30 and 60 degrees. For both tasks, the same procedure was used. For example, in the case ofthe ipsilateral arm matching task, an experimenter positioned the affected arm of the participant at30 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 participantmoved the affected arm back to the remembered position. An experimenter measured this shoulderabduction angle manually using a goniometer. The same procedure was repeated for the 60 degreeangle and for the other two movements. We applied a low-cost Kinect to extract the participant’sbody joint position data. Tactile feedback was given based on the arm position detected by the Kinectsensor. 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 athome. The results of the FMA, WMFT, and goniometer angle measurements showed improvementsin several tasks, suggesting a positive effect of the training system and its feasibility for furtherapplication for stroke survivors’ rehabilitatio.

  • 7.
    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, ISSN 1424-8220, 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.

  • 8.
    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, ISSN 1424-8220, 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.

  • 9.
    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, ISSN 1424-8220, 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.

  • 10.
    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, ISSN 1424-8220, 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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