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Kristoffersson, AnnicaORCID iD iconorcid.org/0000-0002-4368-4751
Publications (10 of 57) Show all publications
Zolfaghari, S., Kristoffersson, A., Folke, M., Lindén, M. & Riboni, D. (2024). Unobtrusive Cognitive Assessment in Smart-Homes: Leveraging Visual Encoding and Synthetic Movement Traces Data Mining. Sensors, 24(5), 1381-1381
Open this publication in new window or tab >>Unobtrusive Cognitive Assessment in Smart-Homes: Leveraging Visual Encoding and Synthetic Movement Traces Data Mining
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2024 (English)In: Sensors, E-ISSN 1424-8220, Vol. 24, no 5, p. 1381-1381Article in journal (Refereed) Published
Abstract [en]

The ubiquity of sensors in smart-homes facilitates the support of independent living for older adults and enables cognitive assessment. Notably, there has been a growing interest in utilizing movement traces for identifying signs of cognitive impairment in recent years. In this study, we introduce an innovative approach to identify abnormal indoor movement patterns that may signal cognitive decline. This is achieved through the non-intrusive integration of smart-home sensors, including passive infrared sensors and sensors embedded in everyday objects. The methodology involves visualizing user locomotion traces and discerning interactions with objects on a floor plan representation of the smart-home, and employing different image descriptor features designed for image analysis tasks and synthetic minority oversampling techniques to enhance the methodology. This approach distinguishes itself by its flexibility in effortlessly incorporating additional features through sensor data. A comprehensive analysis, conducted with a substantial dataset obtained from a real smart-home, involving 99 seniors, including those with cognitive diseases, reveals the effectiveness of the proposed functional prototype of the system architecture. The results validate the system’s efficacy in accurately discerning the cognitive status of seniors, achieving a macro-averaged F1-score of 72.22% for the two targeted categories: cognitively healthy and people with dementia. Furthermore, through experimental comparison, our system demonstrates superior performance compared with state-of-the-art methods.

Keywords
trajectory mining, visual feature extraction, smart environments, machine learning, environmental sensors, ambient sensing, ambient assisted living
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-66143 (URN)10.3390/s24051381 (DOI)
Available from: 2024-02-28 Created: 2024-02-28 Last updated: 2024-02-28Bibliographically approved
Abdullah, S., Hafid, A., Folke, M., Lindén, M. & Kristoffersson, A. (2023). A Novel Fiducial Point Extraction Algorithm to Detect C and D Points from the Acceleration Photoplethysmogram (CnD). Electronics, 12(5), Article ID 1174.
Open this publication in new window or tab >>A Novel Fiducial Point Extraction Algorithm to Detect C and D Points from the Acceleration Photoplethysmogram (CnD)
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2023 (English)In: Electronics, E-ISSN 2079-9292, Vol. 12, no 5, article id 1174Article in journal (Refereed) Published
Abstract [en]

The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task. Various feature extraction methods have been proposed in the literature. In this study, we present a novel fiducial point extraction algorithm to detect c and d points from the acceleration photoplethysmogram (APG), namely “CnD”. The algorithm allows for the application of various pre-processing techniques, such as filtering, smoothing, and removing baseline drift; the possibility of calculating first, second, and third photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting APG fiducial points. An evaluation of the CnD indicated a high level of accuracy in the algorithm’s ability to identify fiducial points. Out of 438 APG fiducial c and d points, the algorithm accurately identified 434 points, resulting in an accuracy rate of 99%. This level of accuracy was consistent across all the test cases, with low error rates. These findings indicate that the algorithm has a high potential for use in practical applications as a reliable method for detecting fiducial points. Thereby, it provides a valuable new resource for researchers and healthcare professionals working in the analysis of photoplethysmography signals.

National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-62004 (URN)10.3390/electronics12051174 (DOI)000947098400001 ()2-s2.0-85149747017 (Scopus ID)
Available from: 2023-03-03 Created: 2023-03-03 Last updated: 2023-04-12Bibliographically approved
Akalin, N., Kiselev, A., Kristoffersson, A. & Loutfi, A. (2023). A Taxonomy of Factors Influencing Perceived Safety in Human–Robot Interaction. International Journal of Social Robotics
Open this publication in new window or tab >>A Taxonomy of Factors Influencing Perceived Safety in Human–Robot Interaction
2023 (English)In: International Journal of Social Robotics, ISSN 1875-4791, E-ISSN 1875-4805Article in journal (Refereed) Published
Abstract [en]

Safety is a fundamental prerequisite that must be addressed before any interaction of robots with humans. Safety has been generally understood and studied as the physical safety of robots in human–robot interaction, whereas how humans perceive these robots has received less attention. Physical safety is a necessary condition for safe human–robot interaction. However, it is not a sufficient condition. A robot that is safe by hardware and software design can still be perceived as unsafe. This article focuses on perceived safety in human–robot interaction. We identified six factors that are closely related to perceived safety based on the literature and the insights obtained from our user studies. The identified factors are the context of robot use, comfort, experience and familiarity with robots, trust, the sense of control over the interaction, and transparent and predictable robot actions. We then made a literature review to identify the robot-related factors that influence perceived safety. Based the literature, we propose a taxonomy which includes human-related and robot-related factors. These factors can help researchers to quantify perceived safety of humans during their interactions with robots. The quantification of perceived safety can yield computational models that would allow mitigating psychological harm.

Place, publisher, year, edition, pages
Springer Science and Business Media B.V., 2023
Keywords
Comfort, Human–robot interaction, Perceived safety, Sense of control, Trust, Human robot interaction, Machine design, Software design, Condition, Hardware and software design, Humans-robot interactions, Related factors, Safe human-robot interaction, User study, Taxonomies
National Category
Robotics
Identifiers
urn:nbn:se:mdh:diva-63893 (URN)10.1007/s12369-023-01027-8 (DOI)001024550100001 ()2-s2.0-85164166548 (Scopus ID)
Available from: 2023-07-19 Created: 2023-07-19 Last updated: 2023-08-23Bibliographically approved
Ehn, M. & Kristoffersson, A. (2023). Clinical sensor-based fall risk assessment at an orthopedic clinic: A case study of the staff’s views on utility and effectiveness. Sensors, 23(4), Article ID 1904.
Open this publication in new window or tab >>Clinical sensor-based fall risk assessment at an orthopedic clinic: A case study of the staff’s views on utility and effectiveness
2023 (English)In: Sensors, E-ISSN 1424-8220, Vol. 23, no 4, article id 1904Article in journal (Refereed) Published
Abstract [en]

In-hospital falls are a serious threat to patient security and fall risk assessment (FRA) is important to identify high-risk patients. Although sensor-based FRA (SFRA) can provide objective FRA, its clinical use is very limited and research to identify meaningful SFRA methods is required. This study aimed to investigate whether examples of SFRA methods might be relevant for FRA at an orthopedic clinic. Situations where SFRA might assist FRA were identified in a focus group interview with clinical staff. Thereafter, SFRA methods were identified in a literature review of SFRA methods developed for older adults. These were screened for potential relevance in the previously identified situations. Ten SFRA methods were considered potentially relevant in the identified FRA situations. The ten SFRA methods were presented to staff at the orthopedic clinic, and they provided their views on the SFRA methods by filling out a questionnaire. Clinical staff saw that several SFRA tasks could be clinically relevant and feasible, but also identified time constraints as a major barrier for clinical use of SFRA. The study indicates that SFRA methods developed for community-dwelling older adults may be relevant also for hospital inpatients and that effectiveness and efficiency are important for clinical use of SFRA.

Keywords
falls, healthcare, hospital, prevention, fall risk, assessment, inertial sensors, wearable sensors, technology adoption
National Category
Other Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-61809 (URN)10.3390/s23041904 (DOI)000941750500001 ()36850500 (PubMedID)2-s2.0-85148970681 (Scopus ID)
Available from: 2023-02-08 Created: 2023-02-08 Last updated: 2023-04-12Bibliographically approved
Abdelakram, H., Kristoffersson, A. & Abdullah, S. (2023). Edu-Mphy: A Low-Cost Multi-Physiological Recording System for Education and Research in Healthcare and Engineering. In: Abstracts: Medicinteknikdagarna 2023. Paper presented at Medicinteknikdagarna 2023, Stockholm, Sweden, 9-11 oktober, 2023 (pp. 117-117).
Open this publication in new window or tab >>Edu-Mphy: A Low-Cost Multi-Physiological Recording System for Education and Research in Healthcare and Engineering
2023 (English)In: Abstracts: Medicinteknikdagarna 2023, 2023, p. 117-117Conference paper, Oral presentation with published abstract (Other academic)
National Category
Computer Systems Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-64768 (URN)
Conference
Medicinteknikdagarna 2023, Stockholm, Sweden, 9-11 oktober, 2023
Available from: 2023-11-17 Created: 2023-11-17 Last updated: 2023-11-29Bibliographically approved
Abdelakram, H., Abdullah, S., Lindén, M., Kristoffersson, A. & Folke, M. (2023). Impact of Activities in Daily Living on Electrical Bioimpedance Measurements for Bladder Monitoring. In: : . Paper presented at 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), 22-24 June 2023, L'Aquila, Italy.
Open this publication in new window or tab >>Impact of Activities in Daily Living on Electrical Bioimpedance Measurements for Bladder Monitoring
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2023 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Accurate bladder monitoring is critical in the management of conditions such as urinary incontinence, voiding dysfunction, and spinal cord injuries. Electrical bioimpedance (EBI) has emerged as a cost-effective and non-invasive approach to monitoring bladder activity in daily life, with particular relevance to patient groups who require measurement of bladder urine volume (BUV) to prevent urinary leakage. However, the impact of activities in daily living (ADLs) on EBI measurements remains incompletely characterized. In this study, we investigated the impact of normal ADLs such as sitting, standing, and walking on EBI measurements using the MAX30009evkit system with four electrodes placed on the lower abdominal area. We developed an algorithm to identify artifacts caused by the different activities from the EBI signals. Our findings demonstrate that various physical activities clearly affected the EBI measurements, indicating the necessity of considering them during bladder monitoring with EBI technology performed during physical activity (or normal ADLs). We also observed that several specific activities could be distinguished based on their impedance values and waveform shapes. Thus, our results provide a better understanding of the impact of physical activity on EBI measurements and highlight the importance of considering such physical activities during EBI measurements in order to enhance the reliability and effectiveness of EBI technology for bladder monitoring.

National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-64033 (URN)10.1109/CBMS58004.2023.00316 (DOI)001037777900135 ()2-s2.0-85166470920 (Scopus ID)979-8-3503-1224-9 (ISBN)
Conference
2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), 22-24 June 2023, L'Aquila, Italy
Available from: 2023-08-16 Created: 2023-08-16 Last updated: 2023-10-25Bibliographically approved
Abdullah, S. & Kristoffersson, A. (2023). Machine learning approaches for cardiovascular hypertension stage estimation using photoplethysmography and clinical features. Frontiers in Cardiovascular Medicine, 10, Article ID 1285066.
Open this publication in new window or tab >>Machine learning approaches for cardiovascular hypertension stage estimation using photoplethysmography and clinical features
2023 (English)In: Frontiers in Cardiovascular Medicine, E-ISSN 2297-055X, Vol. 10, article id 1285066Article in journal (Refereed) Published
Abstract [en]

Cardiovascular diseases (CVDs) are a leading cause of death worldwide, with hypertension emerging as a significant risk factor. Early detection and treatment of hypertension can significantly reduce the risk of developing CVDs and related complications. This work proposes a novel approach employing features extracted from the acceleration photoplethysmography (APG) waveform, alongside clinical parameters, to estimate different stages of hypertension. The current study used a publicly available dataset and a novel feature extraction algorithm to extract APG waveform features. Three distinct supervised machine learning algorithms were employed in the classification task, namely: Decision Tree (DT), Linear Discriminant Analysis (LDA), and Linear Support Vector Machine (LSVM). Results indicate that the DT model achieved exceptional training accuracy of 100% during cross-validation and maintained a high accuracy of 96.87% on the test dataset. The LDA model demonstrated competitive performance, yielding 85.02% accuracy during cross-validation and 84.37% on the test dataset. Meanwhile, the LSVM model exhibited robust accuracy, achieving 88.77% during cross-validation and 93.75% on the test dataset. These findings underscore the potential of APG analysis as a valuable tool for clinicians in estimating hypertension stages, supporting the need for early detection and intervention. This investigation not only advances hypertension risk assessment but also advocates for enhanced cardiovascular healthcare outcomes.

National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-64933 (URN)10.3389/fcvm.2023.1285066 (DOI)001129286700001 ()2-s2.0-85180122781 (Scopus ID)
Available from: 2023-12-05 Created: 2023-12-05 Last updated: 2024-01-10Bibliographically approved
Abdullah, S., Abdelakram, H., Lindén, M., Folke, M. & Kristoffersson, A. (2023). Machine Learning-Based Classification of Hypertension using CnD Features from Acceleration Photoplethysmography and Clinical Parameters. In: Proceedings - IEEE Symposium on Computer-Based Medical Systems: . Paper presented at 36th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2023, Aquila, 22 June 2023 through 24 June 2023 (pp. 923-924). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Machine Learning-Based Classification of Hypertension using CnD Features from Acceleration Photoplethysmography and Clinical Parameters
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2023 (English)In: Proceedings - IEEE Symposium on Computer-Based Medical Systems, Institute of Electrical and Electronics Engineers Inc. , 2023, p. 923-924Conference paper, Published paper (Refereed)
Abstract [en]

Cardiovascular diseases (CVDs) are a leading cause of death worldwide, and hypertension is a major risk factor for acquiring CVDs. Early detection and treatment of hypertension can significantly reduce the risk of developing CVDs and related complications. In this study, a linear SVM machine learning model was used to classify subjects as normal or at different stages of hypertension. The features combined statistical parameters derived from the acceleration plethysmography waveforms and clinical parameters extracted from a publicly available dataset. The model achieved an overall accuracy of 87.50% on the validation dataset and 95.35% on the test dataset. The model's true positive rate and positive predictivity was high in all classes, indicating a high accuracy, and precision. This study represents the first attempt to classify cardiovascular conditions using a combination of acceleration photoplethysmogram (APG) features and clinical parameters The study demonstrates the potential of APG analysis as a valuable tool for early detection of hypertension.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Keywords
acceleration photoplethysmography, cardiovascular, fiducial points, hypertension, PPG, Acceleration, Classification (of information), Learning systems, Statistical tests, Support vector machines, Cardiovascular disease, Causes of death, Clinical parameters, Machine-learning, Photoplethysmogram, Photoplethysmography
National Category
Cardiac and Cardiovascular Systems Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-63964 (URN)10.1109/CBMS58004.2023.00344 (DOI)001037777900162 ()2-s2.0-85166469701 (Scopus ID)9798350312249 (ISBN)
Conference
36th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2023, Aquila, 22 June 2023 through 24 June 2023
Available from: 2023-08-16 Created: 2023-08-16 Last updated: 2023-10-25Bibliographically approved
Abdullah, S., Hafid, A., Folke, M., Lindén, M. & Kristoffersson, A. (2023). PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points. Frontiers in Bioengineering and Biotechnology, 11, Article ID 1199604.
Open this publication in new window or tab >>PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points
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2023 (English)In: Frontiers in Bioengineering and Biotechnology, E-ISSN 2296-4185, Vol. 11, article id 1199604Article in journal (Refereed) Published
Abstract [en]

Photoplethysmography is a non-invasive technique used for measuring several vital signs and for the identification of individuals with an increased disease risk. Its principle of work is based on detecting changes in blood volume in the microvasculature of the skin through the absorption of light. The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task, where various feature extraction methods have been proposed in the literature. In this work, we present PPGFeat, a novel MATLAB toolbox supporting the analysis of raw photoplethysmography waveform data. PPGFeat allows for the application of various preprocessing techniques, such as filtering, smoothing, and removal of baseline drift; the calculation of photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting photoplethysmography fiducial points. PPGFeat includes a graphical user interface allowing users to perform various operations on photoplethysmography signals and to identify, and if required also adjust, the fiducial points. Evaluating the PPGFeat’s performance in identifying the fiducial points present in the publicly available PPG-BP dataset, resulted in an overall accuracy of 99% and 3038/3066 fiducial points were correctly identified. PPGFeat significantly reduces the risk of errors in identifying inaccurate fiducial points. Thereby, it is providing a valuable new resource for researchers for the analysis of photoplethysmography signals.

Keywords
photoplethysmography, PPG features, fiducial points, MATLAB, toolbox, signal processing, acceleration photoplethysmography, velocity photoplethysmography
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-63035 (URN)10.3389/fbioe.2023.1199604 (DOI)001020124900001 ()2-s2.0-85163601193 (Scopus ID)
Available from: 2023-06-09 Created: 2023-06-09 Last updated: 2023-07-26Bibliographically approved
Abdullah, S., Abdelakram, H., Kristoffersson, A., Bilal Saeed, M. & Saad, S. (2023). Real-Time Portable Raspberry Pi-Based System for Sickle Cell Anemia Detection. In: Abstracts: Medicinteknikdagarna 2023. Paper presented at Medicinteknikdagarna 2023, Stockholm, Sweden, 9-11 oktober, 2023 (pp. 118-118).
Open this publication in new window or tab >>Real-Time Portable Raspberry Pi-Based System for Sickle Cell Anemia Detection
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2023 (English)In: Abstracts: Medicinteknikdagarna 2023, 2023, p. 118-118Conference paper, Oral presentation with published abstract (Other academic)
National Category
Computer Systems Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-64765 (URN)
Conference
Medicinteknikdagarna 2023, Stockholm, Sweden, 9-11 oktober, 2023
Available from: 2023-11-17 Created: 2023-11-17 Last updated: 2023-11-29Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-4368-4751

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