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UKHRV2018 Conference - Cyclists hand arm vibration exposure paper
Presented at the 53rd United Kingdom Conference on Human Responses to Vibration
Hosted by Andreas Stihl UK & the Health and Safety Executive, Ascot. 11th - 13th September 2018
Taylor, M.D.1, Oliver, C.W.2. & Bayram, J.3
1 Edinburgh Napier University, Transport Research Institute, 10 Colinton Road, Edinburgh, EH10
5DT, United Kingdom,
2 The University of Edinburgh, Physical Activity for Health Research Centre, St Leonard's Land,
Edinburgh, EH8 8AQ, United Kingdom,
3 Edinburgh Medical School, The University of Edinburgh Medical School, 47 Little France Crescent,
Edinburgh, EH16 4TJ, United Kingdom,
Cycling infrastructure and, in particular, a well maintained pavement surface
contributes to a safe and comfortable ride. However, defective pavement surfaces
and insufficient maintenance can expose cyclists to excessive hand-arm vibration.
Limited data is available regarding cyclists’ exposure to hand-arm vibration.
Advances in low-cost electronics engineering has provided a range of vibration
sensors and recording media. Instrumented probe bicycles can be constructed
with low-cost apparatus to allow a broad range of data to be collected. Details of
the design and construction of a low-cost hand-arm vibration measurement system
are provided. Measurements comply with EN ISO 5339-1:2001 with a sample rate
of 5 kHz and the application of frequency weighting filters (Wh). Partial exposure
data (A(8)t ms-2 r.m.s.) are provided for a range of cycling infrastructure surfaces in
Edinburgh. Preliminary findings of a medical screening survey (n = 555) are also
presented. The results show that there is a potential public health issue
associated with cycle delivery couriers, commuters and recreational cyclists riding
on unsuitable and poorly maintained pavement surfaces for prolonged periods of
1. Introduction
The management of pavement surfaces for walking and cycling is currently a labour intensive task
and relies upon user reporting defects and direct visual inspection. Local authorities are being
pressed to cut budgets and reduce annual expenditure. Therefore, pavement surfaces associated
with walking and cycling are seldom considered to be an investment priority. A defective pavement
surface discourages cycling activity and vibration exposure has been identified as a consequence of
poor cycle track quality (Bíl et al., 2015). Through an online survey of experienced cyclists (>2000 km
per year), Ayachi et al. (2015) conducted principal component analysis of the results and identified
that road surface condition, bicycle saddle and frame design contribute significantly to rider comfort.
Gao et al. (2018) conducted a surveys of pavement surface quality using a combination of user
perception questionnaire surveys and an instrumented bicycle.
Previous research has assessed the relative contribution of bicycle components on the vibration
induced in the hands and buttocks of cyclists. Lépine et al. (2015) assessed the relative contribution
of vibration through measurement in three different locations. These included the vertical force and
acceleration transmitted via the saddle, force and acceleration transmitted through the handle bars
Rev: 1st August 2018
and, finally, the force and acceleration transmitted to the hands on break hoods and the handle bars
under the hands. Gomes and Savionek (2014) conducted hand-arm vibration exposure on three
pavement surface types: asphalt, precast concrete and interlocking concrete blocks. Using a tri-axial
piezoelectric accelerometer fixed to the handle bars, daily exposure to vibration (A(8)) for a daily
duration of exposure of two hours (T=2 hrs) was considered to represent an average exposure time
for leisure cycling purposes. Parkin and Eugenie Sainte (2014) provided a study of comfort and
health factors including the nature of vibrations from riding in different circumstances in the city of
London. Munera et al. (2014) summarised the different standards and guidelines associated with the
evaluation of vibration and exposure limits whilst cycling. They focused upon physiological and
pathological disorders in performance athletes. The research identified the application of the
Directive 2002/44/EC11 in defining the limit of exposure and the limit triggering action for cyclists’
vibration exposure. Furthermore, they identified ISO 5349-1 a suitable method for examining cyclists’
vibration exposure. Munera et al. (2018) analysed the dynamic and physiological response of the
human body under different vibration frequencies whilst cycling.
Hölzel et al. (2012) measured cyclists’ exposure to vibration induced by four different cycle path
pavement surfaces: asphalt, concrete paving slabs, cobblestones and self-binding gravel. They
concluded that cycling pavement surfaces constructed from asphalt improve rider comfort and may
encourage greater uptake of cycling. In a review of instrumented probe bicycle (IPB) research,
Mohanty et al. (2014) summarised the development of comfort and safety prediction models
highlighted the need to collect more accurate and continuous real-time data that represents the
cycling experience.
The research aims to contribute improved data collection procedures for the maintenance of cycling
infrastructure provision. The present study examines the public health implications of defective
pavement infrastructure to professional, commuter and recreational cyclists. A self-reported vibration
exposure symptom survey was also conducted (n=555). The questionnaire explored cyclists’
experience of vibration exposure symptoms with specific questions providing medical screening of
comorbid disease and medical procedures. The preliminary findings of field measurements and the
self-reported symptom questionnaire survey are presented. The results provide an insight into the
potential prevalence of hand-arm vibration exposure symptoms among recreational and commuter
cyclists in Edinburgh.
2. Data collection methods
Two research methods were adopted: (i) the instrumented bicycle and (ii) an online survey of self-
reported vibration exposure symptoms. The following sections provide specific details of the methods
adopted for the study.
2.1. Instrumented bicycle
An aluminium framed Trek 6000 (m = 13.9 kg) was selected as the bicycle platform for the
instrumented probe. The bicycle was selected as a typical commuting, sports and recreational bicycle
type witnessed in the Edinburgh. There are many variables associated with the power supplied by a
Rev: 1st August 2018
cyclist to provide the locomotive force. These include the mechanical efficiency of the bicycle, the
mass of the rider, the mass of the bicycle, the coefficient of rolling resistance, the gradient of the
surface, aerodynamic drag, frontal area of the rider and the headwind velocity. Furthermore,
parameters associated with the tyre tread pattern, tyre pressure and the movement of shock
absorbers can significantly vary the repeatability of the data collected. Figure 1 shows the bicycle and
equipment configuration.
Figure 1 Instrumented probe bicycle equipment configuration.
It is essential that human vibration exposure is quantified by the vibration conditions at the interface
between the environment and the human body: not by the vibration at any other arbitrary position on
the body or in the vibration environment (Griffin, 1990). Therefore, a grip adaptor was constructed
from a stereolithography file using a 3D printer and was printed from acrylonitrile butadiene styrene
(ABS) thermoplastic polymer. Figure 2 shows the grip adaptor and the mounting position on the
Figure 2 Handle bar grip adaptor mount position.
Two three axis micro-electro mechanical accelerometers (LIS3DH) were mounted on the handle bars
using the constructed grip adaptor. The accelerometers sample rate was 5 kHz. A micro controller
(Teensy 3.2) and compact computer (Raspberry Pi 3) were used to control data capture and storage.
A bespoke GPS device including a (MTK3339), micro controller and micro SD card was mounted on
the rear luggage rack to assist with gathering location information for future analysis.
All digital signal processing was undertaken using Matlab 2017a. Toolbox add-ons included the
Control System Toolbox (Version 10.2), Digital Signal Toolbox (Version 9.4) and Signal Processing
Rev: 1st August 2018
Toolbox (Version 7.4). Digital filters (Wh) were constructed in accordance with ISO 5349 (BSI, 2001)
using continuous time transfer functions.
2.2. Self-reporting vibration symptom questionnaire survey
The target population was Edinburgh based commuter and recreational cyclists. The survey was
piloted in March 2018 on a pilot sample of members of the target population. This process allowed
identification of respondents having issues understanding the questions or the specific logical
sequence of questions. Pilot respondents provided feedback in relation to these matters and the
survey instrument was amended.
Non-random convenience sampling was considered for the survey. The sample was constructed of
individuals who were easiest to recruit, e.g. University colleagues, students, medical staff and those
active on social media. Social media was used to advertise the survey with support from Spokes and
other Scottish cycling interest groups. Snowballing of survey responses was also undertaken; as one
respondent completed the survey they were encouraged to recommend other suitable respondents to
be surveyed.
The research considered commuter and recreational cyclists exposure to hand-arm vibration and their
potential development of symptoms associated with excessive hand-arm vibration exposure. The
questionnaire was divided into three sections: (i) cycling activity, (ii) self-reported HAV symptoms and
(iii) medical screening.
The first section collected data concerning general cycling activity and examined continuous and
categorical data associated with exposure to cycling and riding position. Suspension was also
considered as this has significant implications for vibration exposure whilst cycling. The questions
specifically requested information relating to the number of years the respondent has been cycling;
number of days in a week spend cycling; hours cycling per day; bicycle riding position; type of bicycle
ridden most often; and does the bicycle most often used have suspension.
The second section questioned respondents on their experience of hand-arm vibration exposure
symptoms. These were categorised as: blanching; cold sensation; stiffness; swelling; pain; tingling;
numbness (lack of sensation); and weakness. Respondents were questioned regarding the longevity
of their symptoms and if the symptoms experienced were associated with work related activities or
cycling. The third section examined medical conditions associated with neuropathies of the hands
and vibrating tool use. Medical diagnosis of hand-arm injuries, Type 1 diabetes, Type 2 diabetes,
carpal tunnel syndrome, cervical radiculopathy, ulnar nerve entrapment, Raynaud’s disease,
rheumatoid arthritis and osteoarthritis (of the hands, wrist, elbow or shoulder). Information relating to
respondents use of vibration emitting power tools in the workplace or for domestic use was
considered. Specific details of tool use and the type of tools considered was sought. Finally,
respondents were asked if they smoked or were ex-smokers.
3. Results
The results of a series of pavement surface surveys and the self-reported symptoms questionnaire
survey results are provided in the following sections. The instrumented bicycle pavement surveys
Rev: 1st August 2018
were conducted in Summer 2017 and the symptom questionnaire survey was conducted in Spring
3.1. Instrumented bicycle survey
National cycle network routes were considered for vibration exposure assessment. In conjunction
with dedicated cycle path routes, adopted roads were also surveyed to provide a comparison with
dedicated off-road cycle path pavement surfaces. Data concerning cyclists’ exposure to vibration
associated with riding on the shoulder area (1.5m to 2.0m from kerb) on adopted roads was
considered. Bus lanes are constructed in Edinburgh as shared space with bicycle traffic.
Kocak and Noble (2010) identified the total length of all cycle paths in the City of Edinburgh as 224
km. They also provided an indication of the split between on-road and off-road cycle path
infrastructure as 82 km and 142 km respectively. The present study surveyed 13.682 km of off-road
and on-road pavement surfaces, representing 6.1% of the cycle paths identified in 2010. Table 1
provides a summary breakdown of pavement surfaces surveyed.
Table 1 Pavement material type and cycle path category surveyed.
Vibration exposure data is presented for road and off-road (no motorised vehicles) pavement
surfaces. Pavement surfaces surveyed included hot rolled asphalt (HRA), asphaltic concrete (AC),
cobble setts (CS), compacted fill (CF), concrete monoblock (M) and concrete pavers (CP). Table 2
shows r.m.s., VDV and A(8)t data for the off-road pavement survey.
Pavement material Road Off-road Total
(m) (m) (m)
Hot rolled asphalt (HRA) 3408 4290 7698
Asphaltic concrete (AC) 0 2392 2392
Cobble setts (CS) 1472 189 1661
Compacted fill material (C
0 1099 1099
Monoblock (M) 160 257 417
Concrete pavers (CP) 0 316 316
Concrete (C) 0 99 99
Total survey distance 5040 8642 13682
Total Edinburgh network 82000 142000 224000
% of network surveyed 6.15% 6.09% 6.11%
Rev: 1st August 2018
Table 2 Off-road pavement vibration exposure summary (n = 20).
Table 3 shows r.m.s., VDV and A(8)t data for the road pavement survey.
Table 3 Road pavement vibration exposure summary (n = 15).
3.2. Symptom questionnaire results
The survey was issued in April 2018 for a period of four weeks. In total, 555 responses were
received. The survey respondents mean age was 40 years (SD = 12.42, range 18-77) and the mean
number of years cycling was 19 years (SD = 15.41, range 0.6-70). Respondents were asked about
their occupation, hobbies and recreational pursuits which may contribute to vibration exposure. The
survey also sought information on medical procedures, lifestyle, vibrating tool use (work and
domestic) and medical conditions which may contribute to vascular and sensorineural hand-arm
Average Sample
speed time
(m) (kph) (s) (ms
NCR754 Union Canal CS 189 11.33 60.06 12.73 50.24 0.58
Donkey Lane CF 614 18.31 120.75 9.71 55.42 0.63
A720 Culvert CF 182 12.22 53.61 6.64 26.90 0.29
A720 Culvert to Railway Bridge CF 165 11.78 50.44 6.31 24.57 0.26
NCR754 Gilmore Place CF 138 14.22 34.95 5.35 20.34 0.19
NCR754 Union Canal AC 621 21.09 105.98 4.65 28.32 0.28
NCR754 Union Canal AC 610 17.73 123.84 4.13 25.73 0.27
Research Avenue North HRA 678 18.42 132.49 3.95 29.63 0.27
Station Park M 257 15.27 60.59 3.88 30.04 0.18
A720 Culvert CF 99 10.14 35.13 3.77 14.17 0.13
NCR754 Union Canal AC 517 23.05 80.74 3.72 16.53 0.20
Bankhead Drive HRA 408 17.38 84.51 3.68 23.15 0.20
NCR754 Union Canal AC 484 18.26 95.40 3.37 20.98 0.19
North Meadow Walk HRA 571 26.26 78.28 3.36 15.09 0.18
Middle Meadow Walk HRA 301 18.86 57.47 3.23 15.15 0.14
NCR754 Union Canal AC 160 14.93 38.58 3.21 11.48 0.12
Bankhead Drive HRA 780 19.46 144.27 3.14 17.74 0.22
Carrick Knowe HRA 1110 23.00 173.75 2.81 17.68 0.22
Station Park CP 316 17.20 66.16 2.50 13.14 0.12
Stenhouse Drive HRA 442 22.90 69.48 2.15 10.89 0.11
A(8)tLocation and surface category Distance R.M.S. VDV
Average Sample
spee d time
(m) (kph) (s) (ms-2)(ms
High Street CS 252 21.50 42.20 13.66 46.95 0.52
High Street (P.Square) CS 159 19.73 29.02 12.85 44.25 0.41
Merchiston Mews CS 120 13.13 32.90 11.92 39.98 0.40
Napier Road HRA 283 20.23 50.35 10.41 47.03 0.44
Lawnmarket CS 839 30.99 97.46 10.11 53.48 0.59
Lawnmarket CS 102 13.11 28.00 9.03 36.44 0.28
Blantyre Terrace HRA 282 18.38 55.23 8.45 47.62 0.37
Horne Terrace HRA 156 17.51 32.08 8.18 32.18 0.27
Merchiston Park HRA 394 26.03 54.50 7.37 32.99 0.32
Merchiston Avenue HRA 393 22.83 61.98 7.16 39.54 0.33
East Castle Road HRA 230 15.52 53.36 5.58 26.29 0.24
Dorset Plac e M 160 12.35 46.65 5.53 25.86 0.22
Forrest Road HRA 354 22.15 57.52 4.51 18.21 0.20
Muir Wood Road HRA 621 25.19 88.75 3.38 20.47 0.19
Research Avenue North HRA 695 23.33 107.23 2.77 13.07 0.17
Distance R.M.S. VDV A(8)tLocation and surface category
Rev: 1st August 2018
vibration exposure symptoms. Of the total response (n = 555) only 24.5% of the respondents (ns =
136) had a medical history which was suitable for considering cycling to be exclusively responsible for
their hand-arm vibration symptoms.
Table 4 Prevalence of self-reported symptoms (ns=136, screened).
Table 4 shows the prevalence of the self-reported symptoms in participant’s hands, wrists, arms and
shoulders. A severity index was constructed to examine the prevalence of hand-arm vibration
symptoms. For each symptom category, a respondent would be awarded a point for each symptom
experienced in the hands, wrists, arms or shoulders, e.g. pain experienced in the hands and wrists
would be considered as two points on the severity scale. The authors note the rudimentary nature of
the scale and intend to develop the symptom severity measurement process as part of the ongoing
Figure 3 shows the screened respondents use of suspension and the relationship with self-reported
hand-arm vibration exposure symptoms. In response to, Does your bicycle have suspension?, 109
(80.14%) responded no and 19 (13.97%) responded yes with 8 (5.8%) not providing a response.
Figure 3 HAV symptoms vs cyclist’s use of suspension.
Figure 4 shows the screened respondents number of years cycling versus the self-reported HAV
symptom severity scale. Figure 5 shows the reported rider positions and prevalent combinations of
rider position reported against the self-reported HAV symptom severity scale.
Severity Blanching Cold
sensation Stiffness Swelling Pain Tingling NumbnessWeakness
088 68 62 117 73 65 70 105
64.7% 50.0% 45.6% 86.0% 53.7% 47.8% 51.5% 77.2%
145 47 48 18 37 53 57 22
33.1% 34.6% 35.3% 13.2% 27.2% 39.0% 41.9% 16.2%
1.5% 11.0% 14.7% 0.0% 14.7% 11.8% 5.9% 4.4%
0.7% 2.9% 4.4% 0.0% 4.4% 0.7% 0.7% 2.2%
0.0% 1.5% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Rev: 1st August 2018
Figure 4 HAV symptom severity vs number years cycling.
Figure 5 HAV symptom severity versus rider position.
4. Discussion
The data set provides insight into the variation of comfort associated with pavement surface materials
used in Edinburgh. Setts are providing considerable vibration exposure in conjunction with defective
asphalt and asphaltic concrete surface materials. These surface materials and conditions are
providing vibration exposure which may contribute to hand-arm vibration exposure symptoms. The
results of the self-reported hand-arm vibration symptoms questionnaire have demonstrated that riders
are experiencing symptoms associated with vascular and sensorineural hand arm vibration. The
results showed that 33.1 % of screened respondents (n =136) reported blanching, 39.0 % and 41.9%
reported tingling sensations in their hands. The use of suspension and the bicycle design type
contribute to an increase of self-reported symptom severity. However, the numbers of years cycling
appears to have an inverse relationship with reported symptoms. This could be associated with more
experienced riders taking measures to reduce vibration exposure, i.e. wearing gloves, adjusting tyre
Rev: 1st August 2018
pressure (or tyre type) or avoiding specific routes which require riding over defective pavement
Potential limitations of the findings include the accuracy of the information provided by the
respondents and that no clinical examination of respondents with high severity score rating was
undertaken. The instrumented bicycle data was collected with only one rider and one bicycle type.
Future work intends to contact survey participants for instrumented bicycle vibration studies on
selected commuter routes in Edinburgh.
5. Conclusions
There is a need to consider the damage caused to cyclists by pavement surface defects as we strive
to increase human-powered and electric (reduced emission) vehicles in our cities. Professional
cyclists should consider monitoring their vibration exposure and in particular those who cycle in urban
areas. Measures for reducing excessive vibration exposure should be sought. For example, gloves,
front (and/or rear) suspension, fit adjustments, anti fatigue handlebar grips , appropriate tyre selection
and pressure all contribute to improved rider comfort. However, the importance of the pavement
surface design and maintenance condition is paramount.
An instrumented bicycle could be used by local authorities when undertaking asset management
decisions associated with re-surfacing and maintenance. Future studies intend to examine the
relationships between bicycle dynamics, mechanical performance of the bicycle and the tyre
interaction with the pavement surface. The collection of objective data concerning cyclists’ vibration
exposure may contribute to improving pavement specification, asset management practice and a
reduced reliance upon direct visual inspection surveys. The results presented provide evidence of
self-reported hand-arm vibration exposure symptoms and objective data that quantifies vibration
exposure on common pavement surfaces in Edinburgh. It is essential that pavement surface quality
is monitored to ensure that there are no public health implications associated with defective
inappropriately specified pavement surfaces.
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