Assessment of the Uncertainty in Human Exposure to Vibration: An ...

474

IEEE SENSORS JOURNAL, VOL. 14, NO. 2, FEBRUARY 2014

Assessment of the Uncertainty in Human Exposure to Vibration: An Experimental Study Francesco Adamo, Filippo Attivissimo, Member, IEEE, Anna M. L. Lanzolla, Member, IEEE, Fabrizio Saponaro, and Vito Cervellera

Abstract— The purpose of this paper is to analyze and to quantify the contributions of measurement uncertainty in the human exposure to vibrations. Typically, the high-level vibrations exposure in workplace can cause the onset of pathologies affecting musculoskeletal, vascular, and neurological systems. Regulations and standards lay down the assessment of the health risks arising from vibrations using specific instruments and a proper measurement procedure. This paper proposes a methodology for the uncertainty evaluation of exposure to hand-arm and wholebody vibrations; as a main contribution, the uncertainty analysis of daily exposure hand-arm vibrations and whole-body vibrations is provided to estimate the vibrations exposition and to reduce the risks of the worker. This activity was developed in collaboration with Military Navy Arsenalin Taranto (South Italy), which is active all along in the protection of health and the safety in the workplace. Index Terms— Human exposure, hand-arm vibration, whole body vibration, measurement uncertainty.

I. I NTRODUCTION

V

IBRATIONS are mechanical oscillations around a reference point that are caused by pressure waves transmitted through solids; in many workplaces, mechanical vibrations may be transmitted to the human body through the part in contact with the vibrating surface, such as in the use of handheld vibrating tools, heavy machinery or transport vehicles [1], [2]. A typical vibration system with multiple degrees of freedom can be used to represent the interaction between the body and the vibration source. The human body may be thought of as a series suspended elements (head, chest, arm, pelvis) linked by damping systems (ligaments, muscles, intervertebral discs) [1], [3], [4]. Each part of the human body has its own natural oscillation frequency; therefore the response to vibration may differ according to the different regions of the body involved. In resonance condition, the vibrating source

Manuscript received July 15, 2013; accepted September 25, 2013. Date of publication October 2, 2013; date of current version December 5, 2013. The associate editor coordinating the review of this paper and approving it for publication was Dr. Anupama Kaul. F. Adamo, F. Attivissimo, A. M. L. Lanzolla, and F. Saponaro are with the Department of Electrical and Information Engineering, Politecnico di Bari, Bari 70125, Italy (e-mail: [email protected]; [email protected]; [email protected]; saponaro@misure. poliba.it). V. Cervellera is with the Arsenale Militare di Taranto, Taranto 74100, Italy (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSEN.2013.2284257

transfers the maximum amount of energy. The result is a large oscillation within the body structures that creates potentially harmful stress. At low frequencies, the human body responds as a single and homogenous mass, and skeletal muscles easily compensate the applied force. Between 2 Hz and 80 Hz, however, the muscles cannot control at length the oscillating movements of the various parts of the body, which behaves as several masses subject to relative movement. Above 80 Hz, vibrations are effectively dampened and the region involved is limited to the one immediately surroundings of the vibrating surface [1]–[6]. In the last few decades, numerous medical studies have been carried out for health risks and human safety assessing [7]–[13]. Some works have shown a clear correlation between the continued exposure to high levels of vibration and the onset of pathologies concerning the sensible parts of human body. Two typologies of vibration exposure have been identified as particularly deleterious: Hand-Arm Vibration (HAV) and Whole-Body Vibration (WBV). Worker exposure to high levels of HAV from hand-held power tools can cause adverse circulatory and neural effects in fingers and hands, muscle weakness or common pathologies such as vibration-induced white finger and carpal tunnel syndrome [2], [3]. High levels of WBV are common in workers who operate with heavy machinery, material handler or transport vehicles [3]–[6]. The most frequently reported pathological effects are lowback pain, early degeneration of the lumbar spinal system and herniated lumbar disc [1]–[6]. In Europe, according to latest Working Conditions Survey, the percentage of the workforce exposed to vibration is widely varying in the different countries, from 14% to 34% [9]. An efficient way of managing the risks related to vibration is to adopt a strategy based on the evaluation of risks, whereby employers are required to assess the magnitude of vibrations and to adopt a plan for prevention and safety on workplace. For this purpose, Directive 2002/44/EC of the European Parliament and of the Council “On the minimum health and safety requirements regarding the exposure of workers to the risks arising from physical agents (vibration)” lays down minimum requirements for the protection of workers from risks to their health and safety arising from exposure to mechanical vibration during the work activities [15]. Evaluation of the vibration exposure may be carried out by means of an estimation based on information provided

1530-437X © 2013 IEEE

ADAMO et al.: ASSESSMENT OF THE UNCERTAINTY IN HUMAN EXPOSURE TO VIBRATION: AN EXPERIMENTAL STUDY

by the manufacturers about the level of emission from the work equipment or obtained by using an online database support. However, the reference method is usually based on field measurements. In this case, the experimental evaluation of both the exposure measurement value and its uncertainty is an important aspect to correctly evaluate the workers exposure and to improve the “overall” quality of life. International Directive requires the assessment of the level of exposure to HAV and WBV in compliance to specific measurement methods defined in ISO standards [16]–[18], but a specific methodology for the calculation of the overall measurement uncertainty is not defined. On the other hand, a detailed uncertainty analysis can give some information about how each single component contributes to stress and can suggest suitable operating condition for the worker. To this aims, the proposed work focus on an experimental analysis of the uncertainty; the vibration measurements were performed in the main areas of Military Navy Arsenal during typically working phases in which workers are exposed to vibration. The post-processing phase is focused on the statistical analysis of data. The paper is organized as follows. Section 2 outlines main aspects of the vibration exposure. In Section 3 the characteristic of the adopted experimental setup is described. Section 4, provides the statistical tests. In Section 5 the experimental results with relevant uncertainty analysis are presented. Section 6 reports an analysis of the experimental results. Finally, Section 7 presents the conclusions. II. DAILY V IBRATION E XPOSURE Vibrations influence the human body in many different ways. The response to a vibration exposure is primarily dependent on the amplitude, the frequency and the exposure time. Moreover, the direction of vibration must be taken into account because it characterizes the propagation depth in the human body [1]–[6]. The amplitude of vibration is expressed in terms of root-mean-square value (RMS) of acceleration that is related to the vibration energy and then to its potential damage. The risk of damage is not equal in all frequencies, and then the guidelines for human vibration evaluating recommend weighting the frequencies of the measured accelerations according to the possible deleterious effects associated with each frequency. Human frequency response to vibrations is typically analyzed using one-third octave bands of acceleration spectrum. The frequencies of interest in determining the potential injury are included in the range (6.3 Hz–1250 Hz) for HAV, and (0.5 Hz–80 Hz) for WBV. For this purpose, the measurement instrumentation implements weighting and bandlimiting filters for each vibration axis as function the exposure typology [16], [17]. According to Directive 2002/44/EC [15], the assessment of vibration exposure is based on the calculation of the daily exposure expressed as equivalent continuous acceleration over an eight-hour work period , and conventionally denoted by the symbol A(8).

Fig. 1.

475

Wh filter for HAV.

It is not mandatory to perform the measurements over on eight hours. Therefore it is sufficient to make short-time during representative work steps which is able to ensure a reasonable statistical accuracy of data. The minimum measurement time should be 1 minute for HAV and 3 minutes for WBV. In the case of HAV, A(8) is defined in ISO standard 5349-1 [16] as the total value ahv (vector sum) of the frequencyweighted RMS acceleration values, measured on the orthogonal axes, awx , awy and awz , and normalized to eight hours:   Te 2 + a2 + a2 A (8)HAV = ahv ; ahv = awx (1) wy wz T0 where Te is the exposure time during one work day and T0 is the reference duration of 8 hours. The frequency weighting is performed by the Wh filter on each axis, since the risk of damage is considered equal in all directions. The Wh curve is shown in Figure 1. In the case of machines which need to be held with both hands, the vibrations measurements must be made on each hand and the higher value is taken into account for exposure evaluation [16], [18]. For WBV exposure, A(8) is obtained from the highest RMS value of the frequency-weighted accelerations determined on three orthogonal axes. It is defined in ISO standard 2631-1 [17] for a seated or standing worker:  Te A (8)WBV = awmax T0   (2) awmax = max 1.4awx ; 1.4awy ; awz Since the risk of damage is not equal in all axes, a multiplying factor and a specific weighting filter must be applied to balance the contributions on the different directions. Therefore, the RMS acceleration values for the x and y axes are multiplied by 1.4 and weighted by the Wd filter, whereas the z axis acceleration is multiplied by 1 and weighted by the Wk filter [17], [18]. The curves of these weighted filters are shown in Fig. 2. The European Directive sets the exposure action values (EAV), above which it requires employers to control the vibration risks to their workforce. Moreover it defines the

476

IEEE SENSORS JOURNAL, VOL. 14, NO. 2, FEBRUARY 2014

Fig. 2.

Wd and Wk filters for WBV. TABLE I

R ISK C LASSES FOR HAV

Fig. 3.

TABLE II R ISK C LASSES FOR WBV

exposure limit values (ELV), representing the limit value whom workers shall not be exposed. In Table I and Table II the EAV and the ELV are listed, with the related risk classes, respectively for the exposure to hand-arm and whole body vibrations [15]. III. M EASUREMENT I NSTRUMENT In the proposed work, the vibration measurements were carried out using a specific instrument provided by the physicalelectrical laboratory of Military Navy Arsenal. Measuring system is complied with the ISO 8041 [19] and integrates the analytical methods for the evaluation of the human exposure to HAV and WBV. For this purpose, two different types of ICP tri-axial accelerometers are used for allowing the simultaneous acquisition of vibration levels on three orthogonal axes. Both ICP accelerometers include a piezoelectric ceramic crystal with a shear mode configuration and a conditioning integrated circuit. SEN040F accelerometer is used for HAV measurements and it is mounted between hand and grip of the vibrating tool using specific adaptors for a better coupling. SEN027 accelerometer

Measurement system.

is used for WBV measurements and it is integrated into a semirigid adaptor mounted on the seat of vibrating machinery [20]. Cable with a 4-pin LEMO connector allows the transfer of data to the 4-channels F&V 8440 analyzer which stores the acceleration amplitude levels for each axis and calculates the frequency-weighted RMS value. The wireless Bluetooth module allows configuring the instrument and carrying out measurements from a remote tablet using Noise & Vibration Work software. Calibrator VC20 is used for testing the sensitivity of accelerometers, before and after the measurements, in compliance with the references described in the ISO 8041 [18]. Figure 3 shows the measurement setup adopted. Table III reports the main specifications according to the manufacturer datasheets information and calibration certificates. IV. S TATISTICAL T ESTS The evaluation of the measurement uncertainty is required for estimating the level of exposure to vibration with a definite confidence level, so that an efficient managing the risks related can be adopted. In order to evaluate both the level of daily exposure A(8) and the measurement uncertainty, a set of measurements was performed during the use of machinery in same working phases which are mainly performed in Military Navy Arsenal in Taranto and which potentially expose to vibration. For HAV exposures the following activities were considered: – Grass cutting with brush cutter; – Large tree trunks cutting with chainsaw; – Tree branches cutting with chainsaw; – Hedges cutting with hedge-trimmer. The measurements were performed for both hands in according to the practical procedures described in the ISO

ADAMO et al.: ASSESSMENT OF THE UNCERTAINTY IN HUMAN EXPOSURE TO VIBRATION: AN EXPERIMENTAL STUDY

477

TABLE III T ECHNICAL C HARACTERISTICS OF THE M AIN C OMPONENTS OF M EASUREMENT S YSTEM

Fig. 5.

Fig. 6.

(a) WBV coordinate system and (b) SEN027 mounting.

Fig. 7.

Fig. 4.

(a) HAV coordinate system and (b) SEN040F mounting.

5349-2 [16]. Figure 4 shows the basicentric coordinate system for a proper mounting of the SEN040F accelerometer. Figure 5 shows an example of the time history graph of a HAV measurement that returns a frequency-weighted RMS acceleration value. For WBV exposures the following working activities were considered: – Heavy load handling with lorry crane; – Wood handling with backhoe loader; – Brush mowing with Sickle bar mower.

HAV time history graph.

WBV time history graph.

In order to obtain an objective analysis, each test was performed in a sitting position according to the practical procedures described in the ISO 2631 and ISO 5349 [17]–[19]; this simple standard protocol makes repeatable and reliable the tests. Figure 6 shows the coordinate system for a proper mounting of the SEN027 accelerometer. Figure 7 shows an example of the time history graph of a HAV measurement that returns a frequency-weighted RMS acceleration value. For each HAV and WBV test, a minimum of 30 measurements of the RMS accelerations value were acquired for uncertainty evaluation. A preliminary descriptive analysis was performed to verify the degree of dispersion and the skewness in the data. Box plot test is a graphical tool for examining key statistical properties of the data. In particular, the box plot allows the detection of possible outliers that are not considered

478

IEEE SENSORS JOURNAL, VOL. 14, NO. 2, FEBRUARY 2014

Fig. 8.

Boxplot for right-HAV.

Fig. 11.

Fig. 9.

Boxplot for left-HAV.

Fig. 12.

Fig. 10.

Quantile-quantile of the RMS z-acceleration for right-HAV.

Boxplot for WBV.

representative. This happens especially in physical continued exposure, wherevery high acceleration values can be acquired because of occasional shock, load variation, wrong instrument use or malfunction of machinery. These values involve a substantial increase of the RMS value (high outlier) making it inconsistent if compared to the other. Low outliers may occur when the measurement starts before the effective exposure to vibration, or in presence of long waiting times or load variation. The acquisition of these low acceleration values involves a substantial reduction of the RMS acceleration making it also inconsistent. The results of HAV and WBV are shown in Figures 8–10. An in-depth analysis of the experimental data shows the presence of outliers on the graphics tests. In particular, there are high outliers in right HAV (awz = 1.89 m/s2 ) and left HAV tests (awx = 11.53 m/s2 , awz = 6.42 m/s2 ); a low outlier in the same experiment (awx = 3.94 m/s2 ). These values should be considered inconsistent with respect to the dataset. Figures 11 and 12 show the Quantile-Quantile (Q-Q) plots of the right HAV RMS acceleration in z direction, and left HAV RMS acceleration in the x and z directions. The obtained results highlight that, if the outliers are suppressed, the

Quantile-quantile of the RMS (x-z)-acceleration for left-HAV, s.

experimental data exhibit a bell-shaped distribution which reasonably approximates the Gaussian distribution. Therefore, the corrected Box and Q-Q plots, omitted here for brevity, both show a good approximation to the normal distribution and no further outliers. Finally, to test the goodness normal distribution fitting, the Shapiro-Wilk (S-W) test has been applied by choosing a significance level value equal to 5%. Moreover, for each comparison test, the following hypotheses have been assumed: (i) the samples are independent and (ii) the mean and the variance of distributions are unknowns. For each of seven performed tests at least thirty samples have been considered. The S-W normality test seems to confirm the hypothesis of normality for each analyzed parameters. The following outputs have been generated: S − W test statistic values : W ∈ [0.956 ÷ 0.967] S − W test critical value : pv ∈ [0.1218 ÷ 0.1389] V. M ETROLOGICAL C HARACTERIZATION A crucial point for a correct evaluation of the health risks arising from vibrations is the performance of the measurement chain. The evaluation of uncertainty measurement was developed according to the Guide to the Expression of Uncertainty in Measurement (GUM), [20]–[22] in agreement

ADAMO et al.: ASSESSMENT OF THE UNCERTAINTY IN HUMAN EXPOSURE TO VIBRATION: AN EXPERIMENTAL STUDY

479

TABLE IV U NCERTAINTY B UDGET OF THE A CCELERATION IN HAV AND WBV T ESTS

with the latest edition of the International Vocabulary of Metrology [23]. To assure very reliable estimation of the uncertainty the authors have executed a calibration procedure before each test following the guidelines reported in [16] and using a VC20 Calibrator [24]. The Type A of the standard uncertainty was evaluated by considering, the average value and the sample standard deviation of the measured acceleration for each axis. 1  awx j ,w y j ,wzj N j =1   N  

 1 = awx j ,w y j ,wzj − a¯ wx ,w y ,wz (3) N (N − 1)

TABLE V RMS A CCELERATION AND I TS U NCERTAINTY IN HAV T ESTS

N

a¯ wx ,w y ,wz =

σawx ,w y ,wz

TABLE VI RMS A CCELERATION AND I TS U NCERTAINTY IN WBV T ESTS

j =1

After verifying the hypothesis of normality for sample function distribution, (by means of chi-square test with a significance level of 5%), the expanded uncertainties have been calculated, considering a t-Student distribution Uax ,a y ,az = 2.04 · σawx ,awy ,awz

(4)

where 2.04 is the t-score associated with a confidence level of 95% and 29 degrees of freedom since 30 measurement values have been considered (Table IV). Previous expressions take into account only the standard uncertainty statistically evaluated from repeated observations. According to the GUM, there are two components that contribute to overall standard uncertainty budget of the acceleration (Table III). They consist of the relative expanded uncertainty with a coverage factor k = 2 [20] due to both the ICP triaxial accelerometers u s and to the analyzer u c . These uncertainty components are estimated from Type B evaluations [20] by taking into account the calibration certificates of the instruments adopted in the measurement process and by considering the expanded relative uncertainties at a coverage probability of 95%:  (5) u bx ,b y ,bz = u 2s + u 2c So, the type B absolute acceleration uncertainties, for each axes {Ubx , Ub y , Ubz }, are been evaluated considering the product of the relative standard uncertainties and the sample means. Therefore, the combined standard uncertainties are evaluated, for each axis, applying the expression (10) in the GUM:  Ucx ,c y ,cz = Ua2x ,a y ,az + Ub2x ,b y ,bz (6) Finally, the expressions (2) and (4) have been considered to estimate the level of exposure to vibration and its uncertainty.

Specifically, the expanded standard uncertainty of the RMS acceleration in HAV (ahv ) and the highest RMS value in WBV tests (awmax ) are calculated (Tables V and VI), according to the uncertainty propagation theory, through the following equation, for independent and uncorrelated variables [20], [22]: 

∂ a¯ hv ∂ a¯ hv ∂ a¯ hv Uc2x + Uc2y + Uc2z (7) Uv = ∂ a¯ wx ∂ a¯ wy ∂ a¯ wz where the partial derivatives represent the sensitivity coefficients, which describe how the output estimate varies with the changes in the values of the input estimate. VI. D ISCUSSION In order to evaluate the experimental health risks arising from vibrations both the parameters A(8)HAV in (1) and A(8)WBV in (2) have been calculated taking into account the contribution of expanded uncertainty to assess the worst case conditions. It must be considered that RMS acceleration uncertainty depends on the number of measurements, mainly in the evaluation of the type A standard uncertainty. Experimental results have proved that the uncertainty remains almost constant when the measurement number is greater than twenty. The results obtained (Table VII) show that greater risks for human health arise from both right and left HAV. Particularly, no health risks are taken when the continuous exposure time (Te ) during one work day is less than one hour; there is a medium risk when Te is less than four hours and the risk is

480

IEEE SENSORS JOURNAL, VOL. 14, NO. 2, FEBRUARY 2014

TABLE VII E XPERIMENTAL N ORMALIZED E XPOSURE L EVEL FOR HAV AND WBV T EST

high when Te is greater than six hours. In the WBV case, the vibrations are much lower with respect to previous case and for Te equal to height the risk remains low. Practically, in both right and left HAV, the EAV value which requires a control of the vibration risk is about five hours; this condition is never reached in WBV exposition. VII. C ONCLUSION In this work an experimental setup for vibration measurement in human body is carried out; basing on the technique developed, an assessment procedure of the measurement uncertainty estimation is proposed. Since the described procedure is an interesting candidate for quantitative vibration monitoring, the results deriving from this analysis can be useful for a metrological characterization of the instruments used in the study and represent a valid tool for risks management. Starting from the evaluation of the experimental acceleration uncertainty in both HAV and WBV exposure, the estimation of the health human risks are investigated. In particular, the experimental analysis shows that the HAV exposures are more harmful for workers with respect WBV vibrations and this can cause the onset of pathologies affecting musculoskeletal, vascular and neurological systems. R EFERENCES [1] M. J. Griffin, Handbook of Human Vibration. London, U.K.: Academic, 1990. [2] R. Morello, C. De Capua, and A. Meduri, “A wireless measurement system for estimation of human exposure to vibration during the use of handheld percussion machines,” IEEE Trans. Instrum. Meas., vol. 59, no. 10, pp. 2531–2521, Oct. 2010. [3] C.-C. Liang and C.-F. Chiang, “A study on biodynamic models of seated human subjects exposed to vertical vibration,” Int. J. Ind. Ergonom., vol. 36, no. 10, pp. 869–890, Oct. 2006. [4] C.-C. Liang and C.-F. Chiang, “Modeling of a seated human body exposed to vertical vibrations in various automotive postures,” Ind. Health, vol. 46, no. 2, pp. 125–137, 2008 [5] C. Druga, D. Barbu, and S. Lache, “Vibration and the human body,” Manag. Technol. Eng., vol. 6, pp. 168–173, Oct. 2007. [6] K. Ruotsalainen, T. Rantaharju, A. Partanen, and P. Romppainen, “Wireless system for the continuous observation of whole-body vibration in heavy machinery,” IEEE Instrum. Meas. Mag., vol. 16, no. 2, pp. 26–32, Apr. 2013. [7] G. Andria, F. Attivissimo, G. Cavone, and A. M. L. Lanzolla, “Acquisition times in magnetic resonance imaging: Optimization in clinical use,” IEEE Trans. Instrum. Meas., vol. 58, no. 9, pp. 3140–3148, Sep. 2009. [8] F. Attivissimo, G. Cavone, A. M. L. Lanzolla, and M. Spadavecchia, “A technique to improve the image quality in computer tomography,” IEEE Trans. Instrum. Meas., vol. 59, no. 5, pp. 1251–1257, May 2010. [9] L.-E. Aime, G. Vendramin, and A. Trotta, “Spirometric measurement post-processing: Expiration data recovery,” IEEE Sensors J., vol. 10, no. 1, pp. 25–33, Jan. 2010.

[10] J. Gu, D. Barker, and M. Pecht, “Health monitoring and prognostics of electronics subject to vibration load conditions,” IEEE Sensors J., vol. 9, no. 11, pp. 1479–1485, Nov. 2009. [11] A. Massaro, F. Spano, A. Lay-Ekuakille, P. Cazzato, R. Cingolani, and A. Athanassiou, “Design and characterization of nanocomposite pressure sensor implemented in tactile robotic system,” IEEE Trans. Instrum. Meas., vol. 60, no. 8, pp. 2967–2975, Aug. 2011. [12] C.-C. Wang, S. B. Trivedi, F. Jin, S. Serguei, Z. Chen, J. Khurgin, et al., “Human life signs detection using high-sensitivity pulsed laser vibrometer,” IEEE Sensors J., vol. 7, no. 9, pp. 1370–1376, Sep. 2007. [13] R. Morello and C. De Capua, “A wearable measurement system for the risk assessment due to physical agents: Whole body mechanical vibration injuries,” in Wearable and Autonomous Biomedical Devices and Systems for Smart Environment (Lecture Notes in Electrical Engineering), vol. 75, A. Lay-Ekuakille and S. C. Mukhopadhyay. New York, NY, USA: Springer-Verlag, 2010, pp. 195–206. [14] A. Parent-Thirion, G. Vermeylen, G. van Houten, M. Lyly-Yrjänäinen, I. Biletta, and J. Cabrita, “Overview report,” in Proc. 5th EWCS, Apr. 2012, pp. 1–159. [15] On the Minimum Health and Safety Requirements Regarding the Exposure of Workers to the Risks Arising from Physical Agents (Vibration), Standard 2002/44/EC, Jun. 2002. [16] Measurement and Evaluation of Human Exposure to Hand-Transmitted Vibration-Part 1: General Requirements, ISO Standard 5349-1, 2001. [17] Mechanical Vibration and Shock—Evaluation of Human Exposure to Whole-Body Vibration—Part 1: General Requirements, ISO Standard 2631-1, 1997. [18] Measurement and Evaluation of Human Exposure to Hand-Transmitted Vibration—Part 2: Practical Guidance for Measurement at the Workplace, ISO Standard 5349-2, 2001. [19] Human Response to Vibration: Measuring Instrumentation, ISO Standard 8041, 2005. [20] (2013). Spectra Equipment, F&W 8440, Specifications [Online]. Available: http://www.spectra.it/FV8440.htm [21] Uncertainty of Measurement – Part 1: Introduction to the Expression of Uncertainty of Measurement, ISO/IEC Standard, Guide 98-1: 2009, 2009. [22] Uncertainty of Measurement – Part 3: Guide to the Expression of Uncertainty in Measurement (GUM), ISO/IEC Guide Standard 98-3: 2008, 2008. [23] International Vocabulary of Metrology-Basic and General Concepts and Associated Terms (VIM), Standard ISO/IEC Guide 99:2007, 2007. [24] Monitran Sensors for Industry. (2013). MTN/VC20 Vibration Calibrator, London, U.K. [Online]. Available: http://www.monitran. com/products/mtnvc20

Francesco Adamo received the M.S. degree in electronic engineering from the Polytechnic of Bari, Bari, Italy, in 2000. Presently, he is an Assistant Professor with the Department of Electrical and Information Engineering, Polytechnic of Bari. His research interests are in the field of electronic measurements on devices and systems, with special regards to digital signal processing for measurements, A-to-D converter modeling, characterization and optimization, and computerand microcontroller-based measurement systems. Dr. Adamo is a member of the Italian Group of Electrical and Electronic Measurements (GMEE).

ADAMO et al.: ASSESSMENT OF THE UNCERTAINTY IN HUMAN EXPOSURE TO VIBRATION: AN EXPERIMENTAL STUDY

Filippo Attivissimo received the M.S. and Ph.D. degrees in electronic engineering from the Polytechnic of Bari, Bari, Italy, in 1993 and 1997 respectively. Since 1993, he has worked on research projects in the field of digital signal processing for measurements with the Polytechnic of Bari. He is presently an Associate Professor of electric and electronic measurements with the Department of Electrical and Information Engineering, Polytechnic of Bari. His main research interests are in the field of electric and electronic measurement on devices and systems, estimation theory, uncertainty evaluation, design of sensors for agriculture, medicine and transportation, digital measurements on power electronic systems, computer vision and A-to-D converter modelling, characterization and optimization. Dr. Attivissimo is a member of the Italian Group of Electrical and Electronic Measurements (GMEE).

Anna M. L. Lanzolla received the M.S. degree in electronic engineering and the Ph.D. degree in electric engineering from the Polytechnic of Bari, Bari, Italy, in 1995 and 1999, respectively. Since 1995, she has worked on research projects in the field of digital signal processing for measurements with the Polytechnic of Bari. She is presently an Assistant Professor with the Department of Electrical and Information Engineering. Her research interests are in the field of electric and electronic measurement on devices and systems, including estimation theory, optimization of spectral estimation algorithms for monitoring of distortion levels in power systems, modeling techniques for monitoring and controlling of environmental quantities. Dr. Lanzolla is a member of the Italian Group of Electrical and Electronic Measurements (GMEE).

481

Fabrizio Saponaro received the M.S. degree in information engineering from the Polytechnic of Bari, Bari, Italy, in 2013. During his master thesis, he collaborated with the Military Navy Arsenal in Taranto and the Department of Electrical and Information Engineering for the study of mechanical vibration and their effects on the human body, vibration sensors and measuring system, post-processing and assessment of uncertainty.

Vito Cervellera received the M.S. degree in physics from the University of Bari, Bari, Italy, in 1999. He has worked on research projects in computational fluid dynamics at the Centre of Studies for the Plasma Chemistry CNR Bari. He specializes in software design and development and has worked on systems of command and control of combat systems of the Italian Navy. Since 2006, he has been the Director of the Technological and Physical Electric Laboratory of Naval Arsenal of Taranto. He is a member of Italian Physical Society.