| 1. |
- Cooray, Vernon, 1952-, et al.
(författare)
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Electromagnetic fields of accelerating charges : Applications in lightning protection
- 2017
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Ingår i: Electric power systems research. - : Elsevier BV. - 0378-7796 .- 1873-2046. ; 145, s. 234-247
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Tidskriftsartikel (refereegranskat)abstract
- Electromagnetic fields generated by accelerating charges can be utilized to evaluate the electromagnetic fields generated by systems where moving charges and/or propagating currents are present. The technique can be used easily to evaluate the electromagnetic fields generated by systems in which propagating currents are present. This is illustrated by utilizing the equations to derive expressions for the electromagnetic fields generated by systems in which current pulses injected by lightning flashes are propagating.
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| 2. |
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| 3. |
- Pfeiffer, Christoph, 1989, et al.
(författare)
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Localizing on-scalp MEG sensors using an array of magnetic dipole coils
- 2018
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Ingår i: Plos One. - : Public Library of Science (PLoS). - 1932-6203. ; 13:5
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Tidskriftsartikel (refereegranskat)abstract
- Accurate estimation of the neural activity underlying magnetoencephalography (MEG) signals requires co-registration i.e., determination of the position and orientation of the sensors with respect to the head. In modern MEG systems, an array of hundreds of low- T c SQUID sensors is used to localize a set of small, magnetic dipole-like (head-position indicator, HPI) coils that are attached to the subject's head. With accurate prior knowledge of the positions and orientations of the sensors with respect to one another, the HPI coils can be localized with high precision, and thereby the positions of the sensors in relation to the head. With advances in magnetic field sensing technologies, e.g., high-T-c SQUIDs and optically pumped magnetometers (OPM), that require less extreme operating temperatures than low- T-c SQUID sensors, on-scalp MEG is on the horizon. To utilize the full potential of on-scalp MEG, flexible sensor arrays are preferable. Conventional co-registration is impractical for such systems as the relative positions and orientations of the sensors to each other are subject-specific and hence not known a priori. Herein, we present a method for co-registration of on-scalp MEG sensors. We propose to invert the conventional co-registration approach and localize the sensors relative to an array of HPI coils on the subject's head. We show that given accurate prior knowledge of the positions of the HPI coils with respect to one another, the sensors can be localized with high precision. We simulated our method with realistic parameters and layouts for sensor and coil arrays. Results indicate co-registration is possible with sub-millimeter accuracy, but the performance strongly depends upon a number of factors. Accurate calibration of the coils and precise determination of the positions and orientations of the coils with respect to one another are crucial. Finally, we propose methods to tackle practical challenges to further improve the method.
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| 4. |
- Abtahi, Farhad, 1981-, et al.
(författare)
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Development and preliminary evaluation of an Android based heart rate variability biofeedback system
- 2014
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Ingår i: Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE. - : IEEE. - 9781424479290 ; 2014, s. 3382-5
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Konferensbidrag (refereegranskat)abstract
- The reduced Heart Rate Variability (HRV) is believed to be associated with several diseases such as congestive heart failure, diabetes and chronic kidney diseases (CKD). In these cases, HRV biofeedback may be a potential intervention method to increase HRV which in turn is beneficial to these patients. In this work, a real-time Android biofeedback application based on a Bluetooth enabled ECG and thoracic electrical bioimpedance (respiration) measurement device has been developed. The system performance and usability have been evaluated in a brief study with eight healthy volunteers. The result demonstrates real-time performance of system and positive effects of biofeedback training session by increased HRV and reduced heart rate. Further development of the application and training protocol is ongoing to investigate duration of training session to find an optimum length and interval of biofeedback sessions to use in potential interventions.
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| 5. |
- Hafid, Abdelakram, et al.
(författare)
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Sensorized T-Shirt with Intarsia-Knitted Conductive Textile Integrated Interconnections: Performance Assessment of Cardiac Measurements during Daily Living Activities
- 2023
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Ingår i: Sensors. - 1424-8220. ; 23:22
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Tidskriftsartikel (refereegranskat)abstract
- The development of smart wearable solutions for monitoring daily life health status is increasingly popular, with chest straps and wristbands being predominant. This study introduces a novel sensorized T-shirt design with textile electrodes connected via a knitting technique to a Movesense device. We aimed to investigate the impact of stationary and movement actions on electrocardiography (ECG) and heart rate (HR) measurements using our sensorized T-shirt. Various activities of daily living (ADLs), including sitting, standing, walking, and mopping, were evaluated by comparing our T-shirt with a commercial chest strap. Our findings demonstrate measurement equivalence across ADLs, regardless of the sensing approach. By comparing ECG and HR measurements, we gained valuable insights into the influence of physical activity on sensorized T-shirt development for monitoring. Notably, the ECG signals exhibited remarkable similarity between our sensorized T-shirt and the chest strap, with closely aligned HR distributions during both stationary and movement actions. The average mean absolute percentage error was below 3%, affirming the agreement between the two solutions. These findings underscore the robustness and accuracy of our sensorized T-shirt in monitoring ECG and HR during diverse ADLs, emphasizing the significance of considering physical activity in cardiovascular monitoring research and the development of personal health applications.
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| 6. |
- Marquez, Juan Carlos, 1976-, et al.
(författare)
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Textile electrode straps for wrist-to-ankle bioimpedance measurements for Body Composition Analysis : Initial validation & experimental results
- 2010
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Ingår i: 2010 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC). - : IEEE Engineering in Medicine and Biology Society. - 9781424441235 ; 2010, s. 6385-8
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Konferensbidrag (refereegranskat)abstract
- Electrical Bioimpedance (EBI) is one of the non-invasive monitoring technologies that could benefit from the emerging textile based measurement systems. If reliable and reproducible EBI measurements could be done with textile electrodes, that would facilitate the utilization of EBI-based personalized healthcare monitoring applications. In this work the performance of a custom-made dry-textile electrode prototype is tested. Four-electrodes ankle-to-wrist EBI measurements have been taken on healthy subjects with the Impedimed spectrometer SFB7 in the frequency range 5 kHz to 1 MHz. The EBI spectroscopy measurements taken with dry electrodes were analyzed via the Cole and Body Composition Analysis (BCA) parameters, which were compared with EBI measurements obtained with standard electrolytic electrodes. The analysis of the obtained results indicate that even when dry textile electrodes may be used for EBI spectroscopy measurements, the measurements present remarkable differences that influence in the Cole parameter estimation process and in the final production of the BCA parameters. These initial results indicate that more research work must be done to in order to obtain a textile-based electrode that ensures reliable and reproducible EBI spectroscopy measurements.
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| 7. |
- Sandberg, David, 1980, et al.
(författare)
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Detecting driver sleeepiness using optimized non-linear combinations of sleepiness indicators
- 2011
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Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 12:1, s. 97-108
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Tidskriftsartikel (refereegranskat)abstract
- This paper addresses the problem of detecting sleepiness in car drivers. First, a variety of sleepiness indicators (based on driving behavior) proposed in the literature were evaluated. These indicators were then subjected to parametric optimization using stochastic optimization methods. To improve performance, the functional form of some of the indicators was generalized before optimization. Next, using a neural network, the best performing sleepiness indicators were combined with a mathematical model of sleepiness, i.e., the sleep/wake predictor (SWP). The analyses were based on data obtained from a study that involved 12 test subjects at the moving-base driving simulator at the Swedish National Road and Transportation Research Institute (VTI), Linkping, Sweden. The data were derived from 12 1-h driving sessions for each test subject, with varying degrees of sleepiness. The performance measure (range [0,1]) for indicators was taken as the average of sensitivity and specificity. Starting with indicators proposed in the literature, the best such indicator, i.e., the standard deviation of the yaw angle, reached a performance score of 0.72 on previously unseen test data. It was found that indicators based on a given signal gave essentially equal performance after parametric optimization, but in no case was it better than 0.72. The best generalized indicator (the generic variability indicator) obtained a performance score of 0.74. SWP achieved a score of 0.78. However, by nonlinearly combining SWP with the generic variability indicator, a score of 0.83 was obtained. Thus, the results imply that a nonlinear combination of a measure based on driving behavior with a model of sleepiness significantly improves driver sleepiness detection.
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| 8. |
- Stöggl, Thomas, et al.
(författare)
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Automatic classification of the Sub-Techniques (Gears) used in cross-country ski skating employing a mobile phone
- 2014
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 14:11, s. 20589-20601
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Tidskriftsartikel (refereegranskat)abstract
- The purpose of the current study was to develop and validate an automatic algorithm for classification of cross-country (XC) ski-skating gears (G) using Smartphone accelerometer data. Eleven XC skiers (seven men, four women) with regional-to-international levels of performance carried out roller skiing trials on a treadmill using fixed gears (G2left, G2right, G3, G4left, G4right) and a 950-m trial using different speeds and inclines, applying gears and sides as they normally would. Gear classification by the Smartphone (on the chest) and based on video recordings were compared. Formachine-learning, a collective database was compared to individual data. The Smartphone application identified the trials with fixed gears correctly in all cases. In the 950-m trial, participants executed 140 ± 22 cycles as assessed by video analysis, with the automatic Smartphone application giving a similar value. Based on collective data, gears were identified correctly 86.0% ± 8.9% of the time, a value that rose to 90.3% ± 4.1% (P < 0.01) with machine learning from individual data. Classification was most often incorrect during transition between gears, especially to or from G3. Identification was most often correct for skiers who made relatively few transitions between gears. The accuracy of the automatic procedure for identifying G2left, G2right, G3, G4left and G4right was 96%, 90%, 81%, 88% and 94%, respectively. The algorithm identified gears correctly 100% of the time when a single gear was used and 90% of the time when different gears were employed during a variable protocol. This algorithm could be improved with respect to identification of transitions between gears or the side employed within a given gear.
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| 9. |
- Liu, Yixing, et al.
(författare)
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Weight Distribution of a Knee Exoskeleton Influences Muscle Activities During Movements
- 2021
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 9, s. 91614-91624
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Tidskriftsartikel (refereegranskat)abstract
- Lower extremity powered exoskeletons help people with movement disorders to perform daily activities and are used increasingly in gait retraining and rehabilitation. Studies of powered exoskeletons often focus on technological aspects such as actuators, control methods, energy and effects on gait. Limited research has been conducted on how different mechanical design parameters can affect the user. In this paper, we study the effects of weight distributions of knee exoskeleton components on simulated muscle activities during three functional movements. Four knee exoskeleton CAD models were developed based on actual motor and gear reducer products. Different placements of the motor and gearbox resulted in different weight distributions. One unilateral knee exoskeleton prototype was fabricated and tested on 5 healthy subjects. Simulation results were compared to observed electromyography signals. Muscle activities varied among weight distributions and movements, wherein no one physical design was optimal for all movements. We describe how a powered exoskeleton's core components can be expected to affect a user's ability and performance. Exoskeleton physical design should ideally take the user's activity goals and ability into consideration.
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| 10. |
- Zhang, Longbin, et al.
(författare)
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Lower-Limb Joint Torque Prediction Using LSTM Neural Networks and Transfer Learning
- 2022
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Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 1534-4320 .- 1558-0210. ; 30, s. 600-609
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Tidskriftsartikel (refereegranskat)abstract
- Estimation of joint torque during movement provides important information in several settings, such as effect of athletes' training or of a medical intervention, or analysis of the remaining muscle strength in a wearer of an assistive device. The ability to estimate joint torque during daily activities using wearable sensors is increasingly relevant in such settings. In this study, lower limb joint torques during ten daily activities were predicted by long short-term memory (LSTM) neural networks and transfer learning. LSTM models were trained with muscle electromyography signals and lower limb joint angles. Hip flexion/extension, hip abduction/adduction, knee flexion/extension and ankle dorsiflexion/plantarflexion torques were predicted. The LSTM models' performance in predicting torque was investigated in both intra-subject and inter-subject scenarios. Each scenario was further divided into intra-task and inter-task tests. We observed that LSTM models could predict lower limb joint torques during various activities accurately with relatively low error (root mean square error <= 0.14 Nm/kg, normalized root mean square error <= 8.7%) either through a uniform model or through ten separate models in intra-subject tests. Furthermore, a transfer learning technique was adopted in the inter-task and inter-subject tests to further improve the generalizability of LSTM models by pre-training a model on multiple subjects and/or tasks and transferring the learned knowledge to a target task/subject. Particularly in the inter-subject tests, we could predict joint torques accurately in several movements after training from only a few movements from new subjects.
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