TNSRE-2013-00115
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Synthesis of Walking Sounds for Alleviating
Gait Disturbances in Parkinson’s Disease
Matthew W. M. Rodger, William R. Young, and Cathy M. Craig

Abstract—Managing gait disturbances in people with
Parkinson’s disease is a pressing challenge, as symptoms can
contribute to injury and morbidity through an increased risk of
falls. While drug-based interventions have limited efficacy in
alleviating gait impairments, certain non-pharmacological
methods, such as cueing, can also induce transient improvements
to gait. The approach adopted here is to use computationallygenerated sounds to help guide and improve walking actions. The
first method described uses recordings of force data taken from
the steps of a healthy adult which in turn were used to synthesize
realistic gravel-footstep sounds that represented different spatiotemporal parameters of gait, such as step duration and step
length. The second method described involves a novel method of
sonifying, in real time, the swing phase of gait using real-time
motion-capture data to control a sound synthesis engine. Both
approaches explore how simple but rich auditory representations
of action based events can be used by people with Parkinson’s to
guide and improve the quality of their walking, reducing the risk
of falls and injury. Studies with Parkinson’s disease patients are
reported which show positive results for both techniques in
reducing step length variability. Potential future directions for
how these sound approaches can be used to manage gait
disturbances in Parkinson’s are also discussed.
Index Terms—Biomedical acoustics, Audio user interfaces,
Sensory aids, Patient rehabilitation, Noninvasive treatment
I. INTRODUCTION
Gait disturbances are a major symptom in Parkinson’s
disease (PD), restricting mobility and quality of life [3], and
contributing to disability and morbidity through an increased
risk of falling [14]. Some of the different types of impairments
experienced by patients include freezing-of-gait (FOG) [24],
reduced step length [25], increased spatio-temporal variability
[8] and difficulties with gait initiation [19]. While there are a
number of pharmacological solutions to try and help manage
the symptoms of Parkinson’s disease, such as levodopa [16],
some gait-related symptoms appear resistant to such
treatments [17], and may therefore benefit from
alternative/complementary forms of gait management. It is our
This paper was submitted for review on 29/05/2013. This research was
supported by the TEMPUS-G project funded by the European Research
Council (210007 StIG).
M. W. M. Rodger is with Queen’s University Belfast, Belfast, BT9 5BN,
UK (e-mail: m.rodger@qub.ac.uk)
W. R. Young was with Queen’s University Belfast. He is now with the
Brunel University London, London, UK (e-mail: will.young@brunel.ac.uk).
C. M. Craig is with Queen’s University Belfast, Belfast, BT9 5BN, UK (email: cathy.craig@qub.ac.uk).
interest to explore non-pharmacological, psychologicallymotivated interventions that could help improve activities of
daily living in people suffering from PD, either by
complimenting the effects of drug-based treatments or by
recruiting non-dopaminergic neural pathways to achieve
improvements in motor performance [1]. The approach
described here involves the use of movement-based sounds
that are generated using computational synthesis and are
presented to help improve the walking actions of PD patients.
Existing research would suggest that management of gait
impairments in PD should focus on the spatio-temporal
consistency of walking actions. Increased variability in strideto-stride timing is associated with higher risk of falls [24],
while FOG is often preceded by progressive reductions in
stride length [5]. Furthermore, step consistency is important
for stable gait initiation [19]. In response to these findings, we
are looking to develop auditory guides that specify spatiotemporal information to get PD patients walking, keep them
walking, and keep them walking steadily.
Previous attempts at cueing gait in PD can be categorized
according to whether they specify temporal information
relating to stepping frequency (mostly acoustic cues), and
spatial information relating to step length (mostly visual cues).
However, recent work has suggested that there is an
attentional cost of using a combination of cues to convey both
temporal and spatial information separately [2]. Hence, there
is a clear need to develop novel sensory cueing strategies that
specify both temporal and spatial information within a single
cue modality. Previous work has shown that gait performance
is compromised when PD patients walk to music [4]. The
assumption that walking whilst concurrently listening to music
will place increased cognitive demands on the walker is likely
to have discouraged attempts to develop acoustic guides for
enhancing gait in PD patients. However, this approach
represents a significant oversight. When patients select their
favorite musical track to walk to [4], the information presented
in the acoustic information has no relevance to the required
locomotor actions (aside from the possible translation between
the musical beat and cadence). This problem can be addressed
by presenting acoustic information that is both temporally and
spatially relevant to the required action, which can be
achieved using a number of methods involving computational
synthesis and presentation of sounds to guide gait in PD
patients.
A key principle for the present research is the phenomenon
of paradoxical kinesia; a term describing a suppression,
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although often brief, of Parkinsonian motor symptoms. For
example, patients who typically show bradykinesia or
slowness of movement when asked to reach as fast as possible
to grasp a ball placed on a table in front of them can suddenly
move more quickly and stably when intercepting a ball in the
same spot that has been rolled down a ramp [18]. Moreover,
startle sounds (high intensity auditory stimuli) can cause PD
patients to generate arm forces with equivalent magnitude and
speed compared to healthy adults, which otherwise would be
weaker and slower [1]. These results have been interpreted as
showing that motor symptoms of PD may be caused by
degeneration of neurons in the basal ganglia that are
responsible for the generation of self-guided actions, and that
when patients are presented with action-relevant external
sensory information, or cues, such affected neural areas may
be bypassed [22]. The current research aims to utilize this
phenomenon in the context of developing auditory guides that
assist walking in people with Parkinson’s.
Previous efforts to use sound to manage gait impairments in
PD patients, have typically involved rhythmic auditory
stimulation [26] [7] [13]. This type of intervention generally
provides PD patients with an auditory metronome, or
markedly rhythmic music, and asks them to match consecutive
footfalls with the onset of each beat. In spite of some success
with this approach in reducing gait variability, other research
suggests that the technique of using metronomic stimuli is
limited in its effect [29]. Moreover, rhythmic sounds only
specify step duration of gait, with no information relating to
spatio-temporal properties of walking actions. While the
sounds used in the present research are rhythmic in the sense
that they repeat regularly (to specify healthy walking actions),
they are more complex than traditional rhythmic auditory
stimuli, as they sonify intra-stride information relating to
additional gait parameters on top of step duration.
II. SYNTHESIZING ACTION SOUNDS
The sounds created by real-world events can specify
properties of the event itself, such as the physical properties of
sounding objects, and interactions between such objects [12].
This is particularly salient for action sounds, that is, sounds
generated through human interaction with the world. The
perceptual resonance of action sounds offers a potentially rich
source of cueing information to assist motor control in
individuals with Parkinson’s disease, as the action-specifying
information in the sounds may guide movements that patients
would normally find difficult to initiate or maintain. For the
present purposes, the affected action that we are interested in
representing sonically is gait.
Walking sounds are generated by the force interaction
between the foot and the ground surface, due to the impulse
imparted onto the surface throughout the stance phase. On
unconsolidated or compliant surfaces, such as gravel or dry
foliage, sounds are also generated by displacement of surface
elements under the sole of the foot as pressure is dynamically
imparted across the cycle of the stance phase from heel
contact through to toe off. Hence, walking sounds may contain
kinetic information about the force profile of the stance phase
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of gait and its associated spatio-temporal parameters. There is
some evidence to suggest that people are sensitive to this
information. In addition to walking speed, in some instances
people can pick up characteristics of the walker, such as
gender or emotional state, from the sounds of footsteps [27].
Moreover, complex walking sounds, such as footsteps on
gravel, may convey both temporal (step duration) and spatial
(step length) properties of gait. Evidence has shown that both
young healthy adults [30] and people with PD [31] can
appropriately adjust step duration and step length to the
recorded sounds of different gravel footsteps.
We were interested in using computational audio
synthesizing techniques to create artificial gravel walking
sounds, which would have the same dynamic properties as
recorded sounds, and theoretically the same perceptual cueing
effects for PD patients. This project is based on the principle,
adopted by Foley artists, that if one can recreate the relevant
invariant properties of the type of interaction that causes a
real-world sound event, then the artificial sound will be heard
as though it were the real one. To achieve this, an existing
gravel sound synthesis program [11] was adapted to use an
approximation of the ground-reaction force (GRF) between
the foot and the gravel surface as input control data for the
sound synthesis. The advantages of using this sound synthesis
technique over recordings are: greater control over the spatiotemporal parameters of the presented gait, as the input vector
parameters can theoretically be adjusted to any step length or
duration; the memory required to create the synthesized sound
is less than that required to store and playback recorded audio
files [10], making the synthesis approach easier to implement
on different devices; the timbre of the sounds can also be
altered to create perception of different walking surfaces,
which may help maintain patients’ interest, without the need
to create new recordings.
To create synthesized gravel sounds based on real action
dynamics, standard gait ground reaction forces (GRFs) were
recorded. One healthy male adult (age = 21years) walked in
time with a metronome across a 6m length walkway, in which
an 8-channel AccuGait force-plate was embedded in such a
way that the upper surface of the force plate was level with the
surface of the walkway. Force data from the plate sensors
were converted from analog to digital using an analog data
acquisition unit (National Instruments Inc., Austin, TX)
connected to a Dell (Precision T1650 Workstation) PC
running Qualisys Track Manager (QTM) software (Qualisys,
Sweden). Force data were sampled at 1000 Hz.
Step length was specified to the walker using paper strips
attached to the walkway. GRF vectors were recorded at 3
stride lengths (50cm, 70cm and 90cm) and 2 step durations
(600ms and 700ms), producing kinetic data for 6 different
spatio-temporal gait patterns. Each raw GRF profile was
curve-fitted to a 9th-order polynomial function using
MATLAB (Mathworks, Natick, MA):
f ( x)  a9 x9  a8 x8  ...  a1 x  a 0 ; x  0,1...T ,
(1)
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where T is the stance duration (time in which the foot is in
contact with the ground) of the in samples.
The coefficients of the polynomials for each gait parameter
combination were used to generate input vectors of the gravelsound synthesis patch, created in the PureData environment
(http://puredata.info/). This patch, which was adapted from
[11], takes as input the 10 coefficients from the 9th-order
polynomial function, the duration (T) of the stance phase of
the step, and the inter-step interval (ISI), corresponding to the
cadence of the synthesized gravel walking sounds. The
polynomial curve of duration, T, controls the intensity level
and band-pass filter central frequency of a randomly generated
noise impulse train. The impulse train is generated by dividing
a 300Hz low-passed noise signal by a 2000Hz low-passed
noise signal and clipping the output to lie between -0.9 and 0.9
of peak output level. The band-pass filter frequency is
generated by a random value generator (0-30000) weighted by
the control signal and clipped between 500Hz and 10000Hz.
An intensity envelope, derived from the polynomial function
control vector, was applied to the filtered impulse train. Audio
data was only processed for the stance phase duration, T, of
each gravel footstep sound. Syntheses of consecutive footstep
sounds were separated by the ISI duration, and consecutive
footstep sounds were alternately panned between left and right
stereo output channels.
Audio output was processed in the PC’s Realtek HD
soundcard. Sounds were presented over Sennheiser HDR180
wireless stereo headphones. This process is shown in Fig. 1.
3
A pilot study was conducted on 10 idiopathic PD patients
(on medication) (males = 4; mean age = 65 ± 5 years; mean
disease duration = 3.4 ± 1.5 years; mean disease duration = 3.4
± 1.5 years; motor impairment was assessed using the Unified
Parkinson’s Disease Rating Scale where participant’s mean
score was 28.4 ± 11.6 (ranging between 15 and 46), to test the
efficacy of using synthesized gravel footstep sounds to
improve step length and reduce variability. Two conditions
were used: control walk (no instructions or guides);
synthesized gravel sounds (70 cm). In the gravel sound
condition, the participants were asked to try to imitate the
sound they heard by walking in a way that would reproduce
this sound if they were walking on a gravel path. The step
duration of gravel footstep sounds was set at 600ms, which
was taken from averaging control walk step durations to the
nearest 100ms. The participants made two complete walks
(up and down) a 12 m path in each condition. Threedimensional gait kinematics were recorded using reflective
markers attached to the toe and heel of each foot, recorded by
16 Qualisys Oqus-3 cameras connected to a Dell PC running
QTM software, sampling at 200 Hz.
The main measure taken was the relative change in Coefficient of variation (CoV) of the participants’ step length and
step duration between the control walks and the walks in the
synthesized gravel footstep condition. Baseline CoV measures
of step length and step duration were 9.20% (s.d. = 2.96%)
and 5.28% (s.d. = 2.73%), respectively. When listening to
synthesized gravel footsteps while walking step length CoV
A. Testing synthesized gravel sounds
An initial study with young healthy adults showed that they
Fig. 1. Synthesis of gravel footstep sounds using GRF recordings. a) Ground
reaction data was recorded for steps made with specific step lengths and step
durations. b) GRF data from steps for each gait parameter were curve-fitted to a
9th-order polynomial function. t0 indicates heel contact with surface and T is
the duration of the stance phase between heel-contact and toe-off from surface.
c) The coefficients of these functions were used to control the input parameters
(sound intensity, I; central filter frequency, F) of a gravel step synthesis
program, generating sounds that correspond to particular step lengths and
durations. These sounds are played back to users who are asked to try to match
the sounds heard with their own steps.
were able to adjust the spatial and temporal parameters of their
gait in accordance with the parameters prescribed by the
synthesized gravel sounds, and that performance in this task
was comparable to using real recorded gravel step sounds
[30].
Fig. 2. Percentage changes in coefficient of variation (CoV) of both step length
and step duration when walking with synthesized gravel footstep sounds,
compared with a control walk. Error bars denote 95% confidence intervals.
was reduced to 7.46% (s.d. = 2.64%) and step duration CoV
was slightly higher at 5.32% (s.d. = 2.68%). Statistical
analysis showed that the participants’ mean percentage change
in CoV of step length was significantly lower than zero (t(9) =
-3.56, p = .007, Cohen’s d = 2.52), although change in CoV of
step duration did not significantly differ from zero (t(9) = 1.10, p = .305, Cohen’s d = 0.77), as seen in Fig. 2. This
indicates that variability in the spatial component of gait was
reduced for patients while walking with synthesized gravel
sounds compared to no sounds.
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This pilot study shows promising results that walking to
synthesised gravel footsteps sounds can improve gait stability
for individuals with PD.
III. REAL-TIME SONFICIATION OF GAIT
While action-tailored sound synthesis offers one promising
non-pharmacological means to manage gait disturbances in
PD, there are also a number of potential avenues in using realtime movement sonification to provide patients direct
augmented feedback of their actions with the goal of assisting
walking. Real-time sonification of gait movements creates an
additional perceptual channel of information about walking, in
addition to proprioception, kinesthetic, and optic flow [9].
This may aid gait by focusing perceptual attention on
movement of the lower limbs [21], and may also be used in
the context of providing target or guide sounds for patients to
attempt to recreate through their own movements.
There exist a number of projects that use sonification to
enhance movement co-ordination, either as a training tool for
sports, or in a rehabilitation context [15]. As stated above, our
present purpose was to develop an interactive sonification
framework and technique specifically to enhance gait
performance in PD. This is based on the following
propositions:
• Sonification of the ‘silent’ part of a movement - Sonifying
the swing-phase of gait provides feedback about the
component of gait that would not otherwise generate a
specific auditory profile. This is different from the
previous approach highlighted in this paper which utilizes
naturally occurring auditory feedback associated with
walking. Furthermore, sonifying the swing-phase when
walking is the only means to specify step length through
sound using kinematic, rather than kinetic, parameters.
• Provision of spatio-temporal sensory information Sonifying the swing-phase can provide information about
the dynamics of the action (changing-pitch), as well as the
spatial extent of the action (end-pitch), thus providing both
additional online sensory feedback, and goal-relevant
information.
• Guidance, not restriction – in existing sonification projects,
auditory feedback is often used to indicate deviation from
a ‘typical’ movement range [6] [28]. It is our intention not
to restrict movements made by patients by indicating
‘errors’. PD patients gait movement may not necessarily be
able to match a healthy template, but we can provide a goal
stride length and cadence and tailor this to a patient’s own
action capabilities.
Initial developments undertaken in the Movement
Innovation Lab at Queen’s University Belfast have
implemented these principles in a real-time gait swing-phase
sonification interface.
An initial phase involved setting up real-time motion
capture of gait. L-shaped structures were built so they could be
attached to the shoe worn on each foot, using rigid lightweight foam and plastic mounts to place reflective markers.
Five reflective markers were attached to each structure in a
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unique configuration for real-time automatic identification of
the markers by the QTM system. A motion capture volume
was set up and calibrated in the Movement Innovation Lab at
Queen’s University Belfast, comprising 18 Qualisys Oqus-3
cameras covering a 12m x 3m x 2m volume, connected to a
Dell (Precision T1650 Workstation) PC running QTM.
Multiple motion capture recordings were taken of a healthy
adult walking within the limits of the motion-capture volume
whilst wearing the L-shaped structures, from which Automatic
Identification of Markers (AIM) models were generated
(Qualisys, Sweden). These allowed the position of the marker
adjacent to the heel on each structure to be tracked in realtime. This positional data was used to control the synthesis of
the swing sounds.
Positional data (X and Y axes) from both L-structures were
streamed at 200 Hz from QTM to a PureData synthesis patch
using the Open Sound Control (http://opensoundcontrol.org/)
protocol. The first block of this patch resolves the X and Y
displacement changes into a single vector, v, for each heel
marker. The absolute difference in displacement between
sample, vn, and sample, vn-2, was calculated as an approximate
measure of instantaneous absolute velocity. The swing-phase
of the step was taken as initiated when this value exceeded a
threshold of 0.5ms-1, which triggered the sonification
synthesis. This sounded a 4-partial sine tone with fundamental
frequency MIDI-number 60 (261.63Hz). Each subsequent tone
was sounded as the displacement from stride initiation
increased in 0.1m intervals, with pitch increasing by 1 MIDI
number each interval, resulting in a chromatic glissando for
each stride. Digital data was converted to sound through the
PC’s Realtek HD soundcard, and presented to users through
Fig. 3. Real-time gait swing-phase sonification setup. Automatic detection of
foot marker structures is used to generate positional data for each foot, which is
then streamed to an audio synthesis program. This program triggers increasing
pitch tones over displacement intervals once a velocity threshold is exceeded.
These sounds are played directly back to users, creating a closed-loop, online
gait sonification interface.
Sennheiser HDR180 wireless stereo headphones, with sounds
generated by left and right foot markers panned to left and
right ears, respectively. This setup is illustrated in Fig. 3.
A. Testing Real-time Sonification of Swing-phase of Gait
A PD patient pilot study using real-time gait sonification
was conducted with 9 participants with idiopathic PD. These
were the same patients as described in Section II above, except
for one participant who was unable to complete this study due
to scheduling problems. The control condition was identical to
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that described above. In addition, gait kinematics from another
condition were recorded. In this condition, participants
listened to real-time sonification (as described above) of their
gait while walking 2 walks (up and down) the 12 m walkway
in each condition. Movements were recorded in the same
manner as described above.
As with the study using synthesized footstep sounds, the
independent variable calculated was the relative change in Coefficient of variation (CoV) of step length and step duration
between the control walks and the walks while listening to
real-time sonified step.
Baseline measures of step length and step duration CoVs in
this study were 9.65% (s.d. = 2.75%) and 5.66% (s.d. =
2.62%), respectively. When the present real-time sonification
technique was used during walking, patients’ step length CoV
reduced to 7.08% (s.d. = 1.57%), while step length CoV
reduced very slightly to 5.32% (s.d. = 2.53%). Using this
technique, mean percentage change in step length CoV was
significantly lower than zero (t(8) = -2.78, p = .019, Cohen’s d
= 1.76), although as in the previous study, the mean
percentage change in step duration CoV was not found to
significantly differ from zero (t(8) = -0.43, p = .675, Cohen’s
d = 0.27). Mean percentage changes in step length CoV and
step duration CoV are shown in Fig. 4. These results suggest
that for PD patients the real-time sonification of their steps can
lead to a significant reduction in the variability of spatial
factors of their gait.
IV. CONCLUSIONS AND FUTURE STEPS
The approaches described here use computationally
generated sounds to provide flexible, spatio-temporal
information, and offer a promising non-pharmacological
solution to the management of gait disturbances in Parkinson’s
disease. The first approach used ground-reaction force
recordings to synthesize the sounds of footsteps on gravel with
specific step lengths and durations, which can be picked up by
users to adjust the spatio-temporal parameters of their gait.
Fig. 4. Percentage changes in coefficient of variation (CoV) of both step length
and step duration when walking with real-time sonfication, compared with a
control walk. Error bars denote 95% confidence intervals.
The second approach involved real-time sonifiaction of the
swing-phase of gait, using motion-capture and audio
5
processing software. Preliminary patient pilot studies
conducted in our laboratory (Movement Innovation Lab)
suggest that both methods can be effective in reducing gait
variability in PD patients. These effects, if validated by testing
larger patient samples, provide the basis for novel, nonpharmacological tools to manage gait disturbances in PD.
It is relevant to note that both types of auditory gait cueing
techniques described here led to improvements in step length
variability, but not step duration variability, for these
participants. This suggests that the effects of auditory action
cues act only to improve consistency of spatial characteristics
of gait and not temporal parameters. Moreover, the baseline
variability of step length of participants in both studies was
greater than that of step duration, which might suggest that
there is less potential to improve consistency in step duration
compared with step length. However, it has previously been
shown that rhythmic auditory stimulation of gait in PD
patients is only effective in reducing step duration variability
if the sound is set to be faster than the patients’ preferred
walking speed [13]. As these studies described here used
sounds that fell within patients’ normal step duration range,
this may explain why reductions in step duration variability
were not obtained. Future research should examine any
additional benefits from the present techniques when temporal
parameters are set to cue gait faster than control walking
speeds.
Future work on this project will involve tailoring sounds to
suit patients. This can involve exploring different types of
sounds and timbres within types, as patients may have
different preferences and find some examples more
aesthetically pleasing than others. Moreover, in conveying
spatio-temporal information, some sound types may be more
efficacious than others. Another step will involve matching
sound playback to patients’ preferred cadence. Healthy elderly
adults find it easier to synchronize with auditory rhythms
when the tempo is close to their preferred gait step duration
[20], and this may also be important for PD patients in
recruiting intact neural sensorimotor timing circuits for
stabilizing gait performance. Finally, technological
developments will be pursued to implement the sound
approaches described here into portable devices for use in
everyday life by patients. A major difficulty of the present
technology is the use of force plates and 3D optical motion
capture techniques which are expensive and immobile. Future
work should research the possibilities of using light-weight
accelerometers to sense ground-reaction force and motion data
for controlling synthesis parameters in a mobile device. Such
developments would allow the current techniques and findings
to have real-world clinical benefits for patients in everyday
life.
ACKNOWLEDGMENT
The authors wish to thank Nick Gillian and Sile
O’Modhrain for their advice during early stages of the audio
synthesis and experiment design processes.
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