| 1. |
- Begum, Shahina, et al.
(författare)
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Classification of physiological signals for wheel loader operators using Multi-scale Entropy analysis and case-based reasoning
- 2014
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Ingår i: Expert systems with applications. - : Elsevier BV. - 0957-4174 .- 1873-6793. ; 41:2, s. 295-305
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Tidskriftsartikel (refereegranskat)abstract
- Sensor signal fusion is becoming increasingly important in many areas including medical diagnosis and classification. Today, clinicians/experts often do the diagnosis of stress, sleepiness and tiredness on the basis of information collected from several physiological sensor signals. Since there are large individual variations when analyzing the sensor measurements and systems with single sensor, they could easily be vulnerable to uncertain noises/interferences in such domain; multiple sensors could provide more robust and reliable decision. Therefore, this paper presents a classification approach i.e. Multivariate Multiscale Entropy Analysis-Case-Based Reasoning (MMSE-CBR) that classifies physiological parameters of wheel loader operators by combining CBR approach with a data level fusion method named Multivariate Multiscale Entropy (MMSE). The MMSE algorithm supports complexity analysis of multivariate biological recordings by aggregating several sensor measurements e.g., Inter-beat-Interval (IBI) and Heart Rate (HR) from Electrocardiogram (ECG), Finger Temperature (FT), Skin Conductance (SC) and Respiration Rate (RR). Here, MMSE has been applied to extract features to formulate a case by fusing a number of physiological signals and the CBR approach is applied to classify the cases by retrieving most similar cases from the case library. Finally, the proposed approach i.e. MMSE-CBR has been evaluated with the data from professional drivers at Volvo Construction Equipment, Sweden. The results demonstrate that the proposed system that fuses information at data level could classify 'stressed' and 'healthy' subjects 83.33% correctly compare to an expert's classification. Furthermore, with another data set the achieved accuracy (83.3%) indicates that it could also classify two different conditions 'adapt' (training) and 'sharp' (real-life driving) for the wheel loader operators. Thus, the new approach of MMSE-CBR could support in classification of operators and may be of interest to researchers developing systems based on information collected from different sensor sources.
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| 2. |
- Begum, Shahina, et al.
(författare)
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Driver's Mental State Monitoring System Using CBR Based on Heart Rate Variability Analysis
- 2012
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Ingår i:
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Konferensbidrag (refereegranskat)abstract
- The consequences of tiredness, drowsiness, stress and lack of concentration caused by a variety of different factors such as illness, sleep depletion, drugs and alcohol is a serious problem in traffic and when operating industrial equipment. This is especially important for professional drivers since both expensive equipment and lives may be at stake, e.g. in mining, construction and personal transportation, reduced concentration, stress or tiredness are known to be the cause of many accidents. A system which recognizes the state of the driver and e.g. suggests breaks when stress level is too high or driver is too tired would enable large savings and reduces accident. Today different sensors enable clinician to determine a driver’s status with high accuracy. The aim of the paper is to develop an intelligent system that can monitor drivers’ stress depending on psychological and behavioral conditions/status using heart rate variability. An experienced clinician is able to diagnose a person’s stress level based on sensor readings. Here, we propose a solution using case-based reasoning to diagnose individual driver’s stress. During calibration a number of individual parameters are established. The system also considers the feedback from the driver’s on how well the test was performed The validation of the approach is based on close collaboration with experts and measurements from 18 driver’s from Volvo Construction Equipment are used as reference.
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| 3. |
- Begum, Shahina, et al.
(författare)
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Mental State Monitoring System for the Professional Drivers Based on Heart Rate Variability Analysis and Case-based Reasoning
- 2012
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Ingår i: 2012 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS). - NEW YORK : IEEE. - 9788360810484 ; , s. 35-42
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Konferensbidrag (refereegranskat)abstract
- The consequences of tiredness, drowsiness, stress and lack of concentration caused by a variety of different factors such as illness, sleep depletion, drugs and alcohol is a serious problem in traffic and when operating industrial equipment. A system that recognizes the state of the driver and e. g. suggests breaks when stress level is too high or driver is too tired would enable large savings and reduces accident. So, the aim of the project is to develop an intelligent system that can monitor drivers' stress depending on psychological and behavioral conditions/status using Heart Rate Variability (HRV). Here, we have proposed a solution using Case-Based Reasoning (CBR) to diagnose individual driver's level of stress. The system also considers feedback from the driver's on how well the test was performed. The validation of the approach is based on close collaboration with experts and measurements from 18 drivers from Volvo Construction Equipment (Volvo CE) are used as reference.
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| 4. |
- Filla, Reno, et al.
(författare)
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A Case Study on Quantifying the Workload of Working Machine Operators by Means of Psychophysiological Measurements
- 2013
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Konferensbidrag (refereegranskat)abstract
- In this study of eighteen wheel loader operators, test-driving a machine in three different traction force settings, we examine if a workload index derived from psychophysiological measurements of heart rate, finger temperature, skin conductance, respiration rate and end-tidal CO2-concentration in exhaled air can be easily used to assess operator workload in sufficient detail to use it as a complement to traditional subjective evaluations in machine testing, either of real machines or in a human-in-the-loop simulator. In a longer perspective, such measurements are expected to play a role in a workload-adaptive operator assistance system.However, the findings do not give support for this vision. Instead they indicate that other types of measurements than what have been used in our study should be employed if ease of use for practitioners such as test engineers is in focus, but also that other factors than just machine operability must be considered to have a great influence on the operator workload.
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| 5. |
- Filla, Reno
(författare)
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Quantifying Operability of Working Machines
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In working machines the human operator is essential for the performance of the total system. Productivity and energy efficiency are both dependent not only on inherent machine properties and working place conditions, but also on how the operator manoeuvres the machine. In order to operate energy-efficient the operator has to experience the machine as harmonic. This is important to consider during the development of such working machines.It is necessary to quantify operability and to include this interaction between the human operator and the machine in both the later stages of a development project (where physical prototypes are evaluated by professional test operators) as well as in the earlier stages like concept design (where only virtual prototypes are available).The influence of the human operator is an aspect that is traditionally neglected in dynamic simulations conducted in concept design, because the modelling needs to be extended beyond the technical system. The research presented in this thesis shows two approaches to rule-based simulation models of a wheel loader operator. Both operator models interact with the machine model just as a human operator does with the actual machine. It is demonstrated that both operator models are able to adapt to basic variations in workplace setup and machine capability. A “human element” can thus be introduced into dynamic simulations of working machines, providing more relevant answers with respect to operator-influenced complete-machine properties such as productivity and energy efficiency.While the influence of the human operator is traditionally ignored when evaluating machine properties in the early stages of the product development process, later stages are very reliant on professional test operators using physical prototypes. The presented research demonstrates how the traditional subjective evaluation of a machine’s operability can be complemented by a calculated measure for the operator’s control effort, derived from the recorded control commands of the machine operator. This control effort measure can also be calculated from the control commands of an operator model in a simulation, such as those presented in this thesis. It thus also allows for an assessment of operability already in the concept design phase.In addition, the results of a study of quantifying operator workload by means of measuring psycho-physiological data such as heart rate variability and respiration rate are presented as the first step towards realising workload-adaptive operator assistance functions. Once fully developed, the method itself can also be used as another complement to the traditional subjective evaluations of operability. This approach can then be applied not only in testing of physical prototypes, but also earlier in the product development process in studies on human-in-the-loop simulators.
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| 6. |
- Filla, Reno
(författare)
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Study of a method for assessing operability of working machines in physical and virtual testing
- 2012
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Ingår i: International Journal of Vehicle Systems Modelling and Testing. - : InderScience Publishers. - 1745-6436 .- 1745-6444. ; 7:3, s. 209-234
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Tidskriftsartikel (refereegranskat)abstract
- In this study of eighteen wheel loader operators, test-driving a machine in three different traction force settings, we found strong support for the hypothesis that the operator’s control commands can be used to assess the machine’s operability, at least in form of ease of bucket filling.The methods chosen to derive the control effort worked well and were computationally efficient.
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