Publikationer från Malmö universitet
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  • 1.
    Caramaschi, Sara
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Olsson, Carl Magnus
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, 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 universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Assessing the Effect of Data Quality on Distance Estimation in Smartphone-Based Outdoor 6MWT2024Ingår i: Sensors, E-ISSN 1424-8220, Vol. 24, nr 8, artikel-id 2632Artikel i tidskrift (Refereegranskat)
    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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  • 2.
    Caramaschi, Sara
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP). Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy.
    Papini, Gabriele B.
    Department of Patient Care & Monitoring, Philips Research, 5656 AE Eindhoven, The Netherlands;Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.
    Caiani, Enrico G.
    Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy;Istituto Auxologico Italiano, IRCCS, S. Luca Hospital, 20149 Milan, Italy.
    Device Orientation Independent Human Activity Recognition Model for Patient Monitoring Based on Triaxial Acceleration2023Ingår i: Applied Sciences, E-ISSN 2076-3417, Vol. 13, nr 7, s. 4175-4175Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Tracking a person’s activities is relevant in a variety of contexts, from health and group-specific assessments, such as elderly care, to fitness tracking and human–computer interaction. In a clinical context, sensor-based activity tracking could help monitor patients’ progress or deterioration during their hospitalization time. However, during routine hospital care, devices could face displacements in their position and orientation caused by incorrect device application, patients’ physical peculiarities, or patients’ day-to-day free movement. These aspects can significantly reduce algorithms’ performances. In this work, we investigated how shifts in orientation could impact Human Activity Recognition (HAR) classification. To reach this purpose, we propose an HAR model based on a single three-axis accelerometer that can be located anywhere on the participant’s trunk, capable of recognizing activities from multiple movement patterns, and, thanks to data augmentation, can deal with device displacement. Developed models were trained and validated using acceleration measurements acquired in fifteen participants, and tested on twenty-four participants, of which twenty were from a different study protocol for external validation. The obtained results highlight the impact of changes in device orientation on a HAR algorithm and the potential of simple wearable sensor data augmentation for tackling this challenge. When applying small rotations (<20 degrees), the error of the baseline non-augmented model steeply increased. On the contrary, even when considering rotations ranging from 0 to 180 along the frontal axis, our model reached a f1-score of 0.85±0.110.85±0.11 against a baseline model f1-score equal to 0.49±0.120.49±0.12.

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