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Abbaspour Asadollah, S., Lindén, M., Gholamhosseini, H., Naber, A. & Ortiz-Catalan, M. (2020). Evaluation of surface EMG-based recognition algorithms for decoding hand movements. Medical and Biological Engineering and Computing, 58(1), 83-100
Open this publication in new window or tab >>Evaluation of surface EMG-based recognition algorithms for decoding hand movements
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2020 (English)In: Medical and Biological Engineering and Computing, ISSN 0140-0118, E-ISSN 1741-0444, Vol. 58, no 1, p. 83-100Article in journal (Refereed) Published
Abstract [en]

Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins’ set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands.

Place, publisher, year, edition, pages
Springer, 2020
Keywords
Classification, Dimensionality reduction, Electromyography, Feature extraction, Myoelectric pattern recognition, Classification (of information), Decoding, Discriminant analysis, Maximum likelihood estimation, Myoelectrically controlled prosthetics, Nearest neighbor search, Pattern recognition, Support vector machines, Correlation coefficient, Hjorth parameters, K-nearest neighbors, Linear discriminant analysis, Motion recognition, Recognition algorithm, Root Mean Square, Motion estimation, article, controlled study, hand movement, k nearest neighbor, maximum likelihood method, motion, reaction time, support vector machine, waveform
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-47110 (URN)10.1007/s11517-019-02073-z (DOI)2-s2.0-85075364573 (Scopus ID)
Available from: 2020-02-20 Created: 2020-02-20 Last updated: 2020-02-20Bibliographically approved
Köckemann, U., Alirezaie, M., Renoux, J., Tsiftes, N., Ahmed, M. U., Morberg, D., . . . Loutfi, A. (2020). Open-source data collection and data sets for activity recognition in smart homes. Sensors, 20(3), Article ID 879.
Open this publication in new window or tab >>Open-source data collection and data sets for activity recognition in smart homes
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2020 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 20, no 3, article id 879Article in journal (Refereed) Published
Abstract [en]

As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods. While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning. It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.

Place, publisher, year, edition, pages
MDPI AG, 2020
Keywords
Data collection software, Prototype installation, Smart home data sets, Automation, Data acquisition, Intelligent buildings, Open source software, Pattern recognition, Software prototyping, Activities of Daily Living, Activity recognition, Configuration planning, Method development, Physical environments, Smart homes, Technical infrastructure, Open Data
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-47106 (URN)10.3390/s20030879 (DOI)2-s2.0-85079189175 (Scopus ID)
Available from: 2020-02-21 Created: 2020-02-21 Last updated: 2020-02-21Bibliographically approved
Afifi, S., Gholamhosseini, H., Sinha, R. & Lindén, M. (2019). A Novel Medical Device for Early Detection of Melanoma. Studies in Health Technology and Informatics, 261, 122-127
Open this publication in new window or tab >>A Novel Medical Device for Early Detection of Melanoma
2019 (English)In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 261, p. 122-127Article in journal (Refereed) Published
Abstract [en]

Melanoma is the deadliest form of skin cancer. Early detection of melanoma is vital, as it helps in decreasing the death rate as well as treatment costs. Dermatologists are using image-based diagnostic tools to assist them in decision-making and detecting melanoma at an early stage. We aim to develop a novel handheld medical scanning device dedicated to early detection of melanoma at the primary healthcare with low cost and high performance. However, developing this particular device is very challenging due to the complicated computations required by the embedded diagnosis system. In this paper, we propose a hardware-friendly design for implementing an embedded system by exploiting the recent hardware advances in reconfigurable computing. The developed embedded system achieved optimized implementation results for the hardware resource utilization, power consumption, detection speed and processing time with high classification accuracy rate using real data for melanoma detection. Consequently, the proposed embedded diagnosis system meets the critical embedded systems constraints, which is capable for integration towards a cost- and energy-efficient medical device for early detection of melanoma.

Place, publisher, year, edition, pages
NLM (Medline), 2019
Keywords
Diagnosis system, Embedded System, Medical device, Melanoma detection
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-44665 (URN)31156102 (PubMedID)2-s2.0-85067129247 (Scopus ID)
Available from: 2019-06-27 Created: 2019-06-27 Last updated: 2019-06-27Bibliographically approved
Ghareh Baghi, A., Lindén, M. & Babic, A. (2019). An artificial intelligent-based model for detecting systolic pathological patterns of phonocardiogram based on time-growing neural network. Applied Soft Computing, 83, Article ID 105615.
Open this publication in new window or tab >>An artificial intelligent-based model for detecting systolic pathological patterns of phonocardiogram based on time-growing neural network
2019 (English)In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 83, article id 105615Article in journal (Refereed) Published
Abstract [en]

This paper presents a novel hybrid model for classifying time series of heart sound signal using time-growing neural network. The proposed hybrid model takes segmental behaviour of heart sound signal into account by combining two different deep learning methods, the Static and the Moving Time-Growing Neural Network, which we call STGNN and MTGNN, respectively. Flexibility of the model in learning both deterministic and stochastic segments of signal allows it to learn those complicated characteristics of heart sound signal caused by any obstruction on semilunar heart valve. The model is trained to distinguish between a patient group and a reference group. The patient group is comprised of the subjects with the semilunar heart valve abnormalities including aortic stenosis, pulmonary stenosis and bicuspid aortic valve, whereas the reference group which is composed of the individuals with the heart abnormalities other than those of the reference group or the healthy ones. The model is validated using two different databases: one comprised of 140 children with various heart conditions, and the other one constituted of 50 elderly patients with aortic stenosis. Both the datasets were collected from the referrals to the University hospitals. The overall accuracy and sensitivity of the model are estimated to be 84.2% and 82.8%, respectively. The results show that the model exhibits sufficient capability to distinguish between the patient and the reference group in such a demanding clinical application. 

Place, publisher, year, edition, pages
Elsevier Ltd, 2019
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-44965 (URN)10.1016/j.asoc.2019.105615 (DOI)000488100900035 ()2-s2.0-85069874639 (Scopus ID)
Available from: 2019-08-08 Created: 2019-08-08 Last updated: 2019-10-17Bibliographically approved
Baig, M. M., GholamHosseini, H., Moqeem, A. A., Mirza, F. & Lindén, M. (2019). Clinical decision support systems in hospital care using ubiquitous devices: Current issues and challenges. Health Informatics Journal, 25(3), 1091-1104
Open this publication in new window or tab >>Clinical decision support systems in hospital care using ubiquitous devices: Current issues and challenges
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2019 (English)In: Health Informatics Journal, ISSN 1460-4582, E-ISSN 1741-2811, Vol. 25, no 3, p. 1091-1104Article in journal (Refereed) Published
Abstract [en]

Supporting clinicians in decision making using advanced technologies has been an active research area in biomedical engineering during the past years. Among a wide range of ubiquitous systems, smartphone applications have been increasingly developed in healthcare settings to help clinicians as well as patients. Today, many smartphone applications, from basic data analysis to advanced patient monitoring, are available to clinicians and patients. Such applications are now increasingly integrating into healthcare for clinical decision support, and therefore, concerns around accuracy, stability, and dependency of these applications are rising. In addition, lack of attention to the clinicians' acceptability, as well as the low impact on the medical professionals' decision making, are posing more serious issues on the acceptability of smartphone applications. This article reviews smartphone-based decision support applications, focusing on hospital care settings and their overall impact of these applications on the wider clinical workflow. Additionally, key challenges and barriers of the current ubiquitous device-based healthcare applications are identified. Finally, this article addresses current challenges, future directions, and the adoption of mobile healthcare applications.

Place, publisher, year, edition, pages
SAGE PUBLICATIONS INC, 2019
Keywords
decision support systems, healthcare applications, smartphone applications, ubiquitous devices in healthcare
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-46035 (URN)10.1177/1460458217740722 (DOI)000492276100046 ()29148314 (PubMedID)2-s2.0-85071280190 (Scopus ID)
Available from: 2019-11-14 Created: 2019-11-14 Last updated: 2019-12-12Bibliographically approved
Tomasic, I., Petrovic, N., Lindén, M. & Rashkovska, A. (2019). Comparison of publicly available beat detection algorithms performanances on the ECGs obtained by a patch ECG device. In: Koricic, M Butkovic, Z Skala, K Car, Z CicinSain, M Babic, S Sruk, V Skvorc, D Ribaric, S Gros, S Vrdoljak, B Mauher, M Tijan, E Pale, P Huljenic, D Grbac, TG Janjic, M (Ed.), 2019 42ND INTERNATIONAL CONVENTION ON INFORMATION AND COMMUNICATION TECHNOLOGY, ELECTRONICS AND MICROELECTRONICS (MIPRO): . Paper presented at 2019 42ND INTERNATIONAL CONVENTION ON INFORMATION AND COMMUNICATION TECHNOLOGY, ELECTRONICS AND MICROELECTRONICS (MIPRO) (pp. 275-278). IEEE
Open this publication in new window or tab >>Comparison of publicly available beat detection algorithms performanances on the ECGs obtained by a patch ECG device
2019 (English)In: 2019 42ND INTERNATIONAL CONVENTION ON INFORMATION AND COMMUNICATION TECHNOLOGY, ELECTRONICS AND MICROELECTRONICS (MIPRO) / [ed] Koricic, M Butkovic, Z Skala, K Car, Z CicinSain, M Babic, S Sruk, V Skvorc, D Ribaric, S Gros, S Vrdoljak, B Mauher, M Tijan, E Pale, P Huljenic, D Grbac, TG Janjic, M, IEEE , 2019, p. 275-278Conference paper, Published paper (Refereed)
Abstract [en]

Eight ECG beat detection algorithms, from the PhysioNet's WFDR and Cardiovascular Signal toolboxes, were tested on twenty measurements, obtained by the Savvy patch ECG device, for their accuracy in beat detection. On each subject, one measurement is obtained while sitting and one while running. Each measurement lasted from thirty seconds to one minute. The measurements obtained while running were more challenging for all the algorithms, as most of them almost perfectly detected all the beats on the measurements obtained in sitting position. However, when applied on the measurements obtained while running, all the algorithms have performed with decreased accuracy. Considering overall percentage of the faulty detected peaks, the four best algorithms were jqrs, from the Cardiovascular Signal Toolbox, and ecgpuwave, gqrs, and wqrs, from the WEDB Toolbox, with percentages of faulty detected beats 1.7, 2.3, 2.9, and 3, respectively.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Patch ECG, R-peaks, heat detection, heart rate, telemonitoring, remote health monitoring
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-45375 (URN)000484544500052 ()978-953-233-098-4 (ISBN)
Conference
2019 42ND INTERNATIONAL CONVENTION ON INFORMATION AND COMMUNICATION TECHNOLOGY, ELECTRONICS AND MICROELECTRONICS (MIPRO)
Available from: 2019-10-03 Created: 2019-10-03 Last updated: 2019-10-03Bibliographically approved
Tomasic, I., Petrovic, N., Lindén, M. & Rashkovska, A. (2019). Comparison of publicly available beat detection algorithms performances on the ECGs obtained by a patch ECG device. In: 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2019 - Proceedings: . Paper presented at 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2019, 20 May 2019 through 24 May 2019 (pp. 275-278). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Comparison of publicly available beat detection algorithms performances on the ECGs obtained by a patch ECG device
2019 (English)In: 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 275-278Conference paper, Published paper (Refereed)
Abstract [en]

Eight ECG beat detection algorithms, from the PhysioNet's WFDB and Cardiovascular Signal toolboxes, were tested on twenty measurements, obtained by the Savvy patch ECG device, for their accuracy in beat detection. On each subject, one measurement is obtained while sitting and one while running. Each measurement lasted from thirty seconds to one minute. The measurements obtained while running were more challenging for all the algorithms, as most of them almost perfectly detected all the beats on the measurements obtained in sitting position. However, when applied on the measurements obtained while running, all the algorithms have performed with decreased accuracy. Considering overall percentage of the faulty detected peaks, the four best algorithms were jqrs, from the Cardiovascular Signal Toolbox, and ecgpuwave, gqrs, and wqrs, from the WFDB Toolbox, with percentages of faulty detected beats 1.7, 2.3, 2.9, and 3, respectively. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
Beat detection, Heart rate, Patch ECG, R-peaks, Remote health monitoring, Telemonitoring, Electrocardiography, Microelectronics, Telemedicine, Heart rates, Tele-monitoring, Signal detection
National Category
Other Medical Engineering Medical Laboratory and Measurements Technologies Signal Processing Medical Equipment Engineering
Identifiers
urn:nbn:se:mdh:diva-45016 (URN)10.23919/MIPRO.2019.8756769 (DOI)2-s2.0-85070300696 (Scopus ID)9789532330984 (ISBN)
Conference
42nd International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2019, 20 May 2019 through 24 May 2019
Available from: 2019-08-15 Created: 2019-08-15 Last updated: 2019-08-15Bibliographically approved
Trobec, R., Jan, M., Lindén, M. & Tomasic, I. (2019). Detection and Treatment of Atrial Irregular Rhythm with Body Gadgets and 35-channel ECG. In: 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2019 - Proceedings: . Paper presented at 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2019, 20 May 2019 through 24 May 2019 (pp. 301-308). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Detection and Treatment of Atrial Irregular Rhythm with Body Gadgets and 35-channel ECG
2019 (English)In: 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 301-308Conference paper, Published paper (Refereed)
Abstract [en]

The atrial irregular rhythm, often reflected in atrial fibrillation, undulation or flutter, is recognized as one of the major causes of brain stroke and entails an increased risk of thromboembolic events because it increases the likelihood of blood clots formation. Its early detection is becoming an increasingly important preventive measure. The paper presents a simple methodology for the detection of atrial irregular rhythm by ECG body gadget that can perform long-term measurements, e.g. several weeks or more. Multichannel ECG, on the body surface, gives a more detailed insight into the atrial activity in comparison to standard 12-lead ECG. The information from MECG is compared with single-channel patch ECG. The obtained results suggest that the proposed methodology could be useful in treatments of atrial irregular rhythm. One can obtain a reliable information about the time and duration of fibrillation events, or determine arrhythmic focuses and conductive pathways in heart atria, or study the effects of antiarrhythmic drugs on existing arrhythmias and on an eventual development of new types of arrhythmias. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
Atrial rhythm, Body surface potential map, ECG body sensor, Gadget, Patch ECG, Microelectronics, Antiarrhythmic drug, Atrial fibrillation, Body sensors, Body surface potential maps, Long-term measurements, Preventive measures, Electrocardiography
National Category
Other Medical Engineering Cardiac and Cardiovascular Systems Biomedical Laboratory Science/Technology Medical Laboratory and Measurements Technologies
Identifiers
urn:nbn:se:mdh:diva-45017 (URN)10.23919/MIPRO.2019.8756779 (DOI)000484544500057 ()2-s2.0-85070287718 (Scopus ID)9789532330984 (ISBN)
Conference
42nd International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2019, 20 May 2019 through 24 May 2019
Available from: 2019-08-15 Created: 2019-08-15 Last updated: 2019-10-03Bibliographically approved
Petrovic, N., Tomasic, I., Lindén, M. & Risman, P. O. (2019). Detection of Human Bodypart Abnormalities by Microwaves – A New Approach. In: 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics MIPRO 2019: . Paper presented at 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics MIPRO 2019, 20 May 2019, Opatija, Croatia (pp. 270-274).
Open this publication in new window or tab >>Detection of Human Bodypart Abnormalities by Microwaves – A New Approach
2019 (English)In: 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics MIPRO 2019, 2019, p. 270-274Conference paper, Published paper (Refereed)
Abstract [en]

We present modified antenna-like devices - applicators - for direct detection of internal inhomogeneities such as breast tumours and brain haemorrhages, at a frequency about 1 GHz. This direct detection provides the possibility of using a simple microwave generator and simple rectification and position registration of the received signals. Direct readouts are thus possible, without any massive computing resources as with tomographic imaging. The transmitting applicator is non-contacting and in free air close to the object. It generates an essentially quasistatic axial magnetic field which induces a circular electric field in the tissue. The receiving 3D contacting applicator contains a high-permittivity ceramic and is resonant. Its mode field provides the desired polarisation sensitivity and filters out the main electric field. The overall system sensitivity for detection of internal inhomogeneities is accomplished by optimised use of the orthogonality of the primary magnetic, induced electric, and diffracted electric fields. When developments are completed, the system will replace or complement existing commercial technologies at a low cost.

National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-43939 (URN)10.23919/MIPRO.2019.8757093 (DOI)000484544500051 ()2-s2.0-85070266490 (Scopus ID)
Conference
42nd International Convention on Information and Communication Technology, Electronics and Microelectronics MIPRO 2019, 20 May 2019, Opatija, Croatia
Projects
ESS-H - Embedded Sensor Systems for Health Research Profile
Available from: 2019-06-20 Created: 2019-06-20 Last updated: 2019-10-03Bibliographically approved
Lindén, M., Kristoffersson, A. & Björkman, M. (2019). Embedded Sensor Systems for Health Plus (ESS-H+) An overview of scientific areas and interdisciplinary target. In: Abstractbok: . Paper presented at Medicinteknikdagarna 2019 (pp. 17-17).
Open this publication in new window or tab >>Embedded Sensor Systems for Health Plus (ESS-H+) An overview of scientific areas and interdisciplinary target
2019 (English)In: Abstractbok, 2019, p. 17-17Conference paper, Oral presentation with published abstract (Refereed)
Abstract [sv]

ESS-H+ will continue the work in the KKS research profile Embedded Sensor Systems for Health (ESS-H) at Mälardalen University. ESS-H has during six years provided important collaboration between researchers, industrial partners and healthcare organizations within three focus areas (Health monitoring at home, Health monitoring at work, and Infrastructure and communication).

The focus of the new research profile ESS-H+ is that monitoring of humans should be able to be performed anytime, anywhere. Our research challenges are focused to the areas of Reliable acquisition and management of physiological data, and Reliable distributed data analysis. Reliable acquisition and management of physiological data is a fundamental prerequisite for advancing anytime, anywhere health monitoring, towards enabling remote monitoring of more serious health conditions than what is safely possible today. Reliable distributed data analysis is a fundamental prerequisite for enabling large-scale deployment of anytime, anywhere health monitoring. Both research challenges are complex and require multi-disciplinary research.

The following important research goals will be addressed within ESS-H+: 

  • Reliable data acquisition, and design of suitable sensor systems for achieving this. 
  • Development of analysis and classification algorithms for physiological parameters. 
  • Achieve efficient distributed data fusion and decision support. 
  • Better utilization of the compute power of sensor nodes, and increased communication reliability: safety as well as security. 
  • Efficient integration of scientific results, from different scientific areas, to an efficient and user-friendly embedded sensor system. 

The research in ESS-H+ will include research within the scientific areas (Biomedical sensor technology, Biomedical signal processing, Intelligent decision support, and Reliable and secure data communication) but also a strong integration between these areas, our collaborating companies, and the end users. Our main challenge within ESS-H+ will be this interdisciplinary integration that aims towards fully operating systems, thud providing efficient integration of scientific results, from different scientific areas, to an efficient and user-friendly embedded sensor system.

National Category
Medical Engineering
Research subject
Electronics
Identifiers
urn:nbn:se:mdh:diva-45435 (URN)
Conference
Medicinteknikdagarna 2019
Funder
Knowledge Foundation
Available from: 2019-10-07 Created: 2019-10-07 Last updated: 2019-10-11Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-1940-1747

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