2019 2nd IEEE International Conference on Soft Robotics (RoboSoft)
COEX, Seoul, Korea, April 14-18, 2019

Sensory garments with vibrotactile feedback for monitoring and informing
seated posture
Vincent J. Barone1 , Michelle C. Yuen2,3 , Rebecca Kramer-Bottiglio2 , and Kathleen H. Sienko1
and vibrotactile cues to encourage users to adopt specificreference postures [6], [7]. Dunne, et al. employed an opticalfiber sensor to monitor seated posture while performing
computer workstation tasks and compared the results to clinical opinions of posture, though did not provide corrective
feedback [8]. Wang, et al. used two inertial measurement
units to monitor thoracic spinal angles and provided visual feedback during torso flexion-extension exercises via
a smartphone app [9]. Wong, et al. used a network of threeinertial measurement units to measure thoracic and lumbar
angles and provide auditory feedback to encourage a neutral
standing position [10].
Additionally, wearable posture trainers are also gaining
traction in commercial markets, with products such as the
UpRight Go Posture Trainer, Lumo Lift, Prana, Alex, andMevics posture monitors currently available. Most provide
feedback via vibrotactile cues, allow for historical posture
tracking through app-based visual graphics, and are attachedto the skin or clothing via adhesive, clips, magnets, or pins.
These devices generally rely on inertial measurement units
to determine posture from a single node, thereby limiting
postural reconstruction to a single vector relative to gravity
and are not capable of directly measuring relative joint
angles.
The emergence of soft, stretchable sensors offers a uniquesolution space for wearable feedback devices, whereby garments with integrated sensing and actuation can potentiallybe made non-obstructive or imperceptible to the user. Softsensors are typically composed of low-modulus materials
that transduce physical deformations to electrical signals.
Various types of these sensors developed for detecting humanmotion include silicone elastomers with embedded liquid
metal microchannels [11], [12], conductive polymer composites for resistive [13] and capacitive sensing [14], stretchable
optical fibers [15], and conductive textiles [16], [17].
Systems utilizing flexible strain sensors (FSS) have previously been successfully employed to reconstruct upper bodyposes and detect kinematic angles. Mattman, et al. developed
a posture monitoring system using a network of FSSs in
conjunction with a classification algorithm to detect exercise
movements and 27 discrete upper body poses, reporting
97% classification accuracy during pose recognition [16],[17]. Yamamoto, et al. investigated the use of FSSs adhered directly to the back for tracking tri-axial movementsof the lumbar spine, reporting errors of less than 3◦ inestimated lumbar angles for flexion-extension, side bending,and rotational movements [18]. Fujimori, et al. developed awearable motion capture suit that integrated flexible tactile

Abstract— Increases in the number of sedentary workers
and the rate of related musculoskeletal injuries have spurredinterest in posture monitoring and feedback devices. The
emergence of flexible sensors that can be integrated into
wearable garments provides a unique solution to unobtrusive
monitoring of postural movements. Furthermore, a postural
guidance system can be realized with the addition of a feedback
display. In this work, a flexible strain sensor was integrated
with a Raspberry Pi processor and a vibrotactile feedback
display to form a sensory feedback garment that measured,
evaluated, and communicated real-time feedback of seatedlumbar posture. The sensor was evaluated for measurement
accuracy via comparison to a Vicon motion capture system, andthe garment was evaluated for efficacy of feedback guidance.
Across studies of five participants, we found that the sensory
garment reproduced the lumbar angle with low error (5.4%)
relative to a motion capture system, and that providing haptic
feedback resulted in participants maintaining lumbar posture
within customized target zones for substantially more time thanin the absence of haptic cues.

I. I NTRODUCTION
As of 2003, musculoskeletal complaints were the second
most common cause of short term and most common causeof long term medical absences from the workplace (1020% of primary care consultations) and were the mostexpensive disease category in a cost of illness study [1].
Computer workstation posture, especially when static, hasbeen shown to be a significant contributor to the developmentof musculoskeletal ailments [2]. While there is no agreed
upon proper posture during sitting, there are many posturesthat are deemed to be poor and should be avoided [3]. Seatedpostural training programs have proven effective, however
they require participants to self-monitor and lead to decreases
in productivity [4]. Furthermore, posture monitoring systems
used in clinics or labs require large or expensive equipment
and are not suitable for everyday use [5].
Typical biofeedback systems employed for postural orbalance guidance and correction employ a sensor (or networkof sensors) to capture body movement, and a display to relay
motion tracking information and corrective cues. Recently,
several portable or wearable systems have been developed
to monitor posture and, in some cases, provide corrective
feedback. Zheng, et al. employed force sensors embedded
in office chairs to detect seated posture and used visual
1 Dept. of Mechanical Engineering, University of Michigan, Ann Arbor,
MI, USA. email: {vbarone, sienko}@umich.edu
2 School of Engineering & Applied Science, Yale University,
New
Haven,
CT,USA.
email: {michelle.yuen,

rebecca.kramer}@yale.edu
3 School of Mechanical Engineering, Purdue University, West Lafayette,
IN, USA.

978-1-5386-9260-8/19/$31.00 ©2019 IEEE391

Fig. 1. (a) A photo of the capacitive sensor. The inset shows the end of thesensor where it is interfaced with the signal conditioning electronics board
via copper strips. (b) Schematic of the cross-section of the sensors. The redlayer indicates the active electrode, the black layers indicate the groundelectrodes. These conductive layers are separated by dielectric material,
indicated in blue. Copper wire is used to electrically connect the upperand lower ground electrodes, and copper strips are affixed to the surfaceof the active and one of the ground electrodes to interface with the signal
conditioning boards. (c) Photo of the sensors integrated into an upper-bodysensory garment. This study focused on the efficacy of the sensor situatedon the lower back, highlighted in the red box.

Fig. 2. (a) Tactor assembly showing coin-style vibration motor mounted
inside plastic housing (top) and fully assembled (bottom). (b) Flexible strain
sensor, reflective markers, and tactor mounted on a participant. (c) Rear viewof participant seated in backless chair at computer workstation. (d) Seatedposition of participant at computer workstation.

structure of White, et al. was modified with the addition ofanother ground and dielectric layer, resulting in a five-layer
capacitor, as discussed in [37] (Figure 1). The additional
layers result in the active layer being fully surrounded byground planes, which increases the signal to noise ratio by
reducing the influence of electrostatic noise on the sensor
output via shielding and by multiplying the capacitance ofthe sensor by a factor of four relative to a three-layer sensor
of the same size. The center electrode layer was composed ofa silicone elastomer (DragonSkin 10 Slow, Smooth-On, Inc.)
blended with expanded graphite (Expandable graphite, Sigma
Aldrich), and separated two dielectric layers composed of theunmodified silicone elastomer. The ground planes comprising the top and bottom outermost layers of the sensor weremade of the same conductive composite used for the centerelectrode.
The conductive composite material was made by mixing silicone with expanded graphite particles suspended in
cyclohexane (a solvent for silicone) for a final loading of
10wt% graphite in the cured silicone, after the cyclohexane fully vaporizes (more details are discussed in [36]).
The ground electrode, dielectric, and active electrode layers
were rod-coated using a threaded rod to create large films(≈ 25 cm × 40 cm), building up the capacitive structure
layer-by-layer. After rod-coating the active electrode, the film
was folded onto itself to create the 5-layer structure. The
sensors were then laser cut from the film, cleaned with soap
and water, and then interfaced with the signal conditioning
electronics. A custom PCB attached to the end of the sensor
measured the change in capacitance between the electrodelayers as strain was applied (Figure 1a). Output from the
PCB was transmitted to a microcontroller (Raspberry Pi 3
Model B) for inline data processing using custom softwaredeveloped in Python. Sensor signals were collected at 25 Hz
and low-pass filtered at 2 Hz to remove high frequency noise
not associated with body motion.
A tactor, consisting of a coin-style vibration motor seatedin a custom plastic housing (Precision Microdrives, 310-101
vibration motor encased in plastic housing [38]), was also
connected to the microcontroller (Figure 2a). The vibration
motor had a frequency of 200 Hz at an operating voltageof 3.0 V and had a spin-up time of 90 ms. The motor

sensors with an inertial measurement unit that was able
to classify several poses [19]. Though these findings arelimited, it is evident that soft sensors have the potential
to monitor postural pose and determine angular relations
between segments of the spine.
To provide cues to the user, feedback displays can be
designed for visual, auditory, or tactile interactions. However,
it is imperative to consider the impact of display modality
on intended use and ease of interaction with the system.
Unlike visual and auditory displays, providing feedback
via vibrotactile cues avoids interference with vision andhearing. Vibrotactile displays have been implemented toprevent collisions and provide navigation information whiledriving [20]–[22], to provide altitude information, warning
signals, and replace or reinforce visual and auditory cues
while flying [23], [24], and to enhance virtual reality environments [25], [26]. For biofeedback purposes, vibrotactile
displays allow for fast encoding of directional cues andcan be co-located with measurement devices to provide
more intuitive operation [27], [28]. Furthermore, vibrotactile
displays have been successfully implemented in arm motion
training, balance training, and gait rehabilitation programs
to provide corrective cues in real time [29]–[35]. Therefore,
these types of displays are well-suited for integration into awearable biofeedback system for indicating postural deviation.
II. M ATERIALS AND M ETHODS
A. Sensory Feedback Garment
The sensory feedback garment consisted of a flexible strain
sensor (FSS), custom PCB, Raspberry Pi processor, andtactor. The FSSs employed in this work are an extension of
the conductive composite-based capacitive sensors presented
originally by White, et al., in which the sensors were demonstrated to be reliable for thousands of cycles and withstandstrains up to 275% with no change in functionality [36]. In
this work, the previous three-layer parallel plate capacitor392was installed in a 3D printed housing measuring 27 mm indiameter by 8 mm thick which served to increase skin contact
and amplify the feedback sensation. The tactor was set tovibrate when certain strain levels were exceeded by the FSS.
Strains thresholds were customized for each participant basedon lumbar range of motion. Tactor activation was logged by
the FSS system.

cues only provided alerts that the participant had deviatedfrom the desired position; they did not indicate directional
errors. Participants were instructed to make corrections until
vibrations from the tactor ceased. Participants were notallowed to practice with feedback prior to testing but were
instructed to sit tall and attempt to return to their own proper
posture to stop a tactor cue. During testing, participants were
tasked with retyping a document displayed on a portion of theworkstation screen. Instructions were to transcribe as quickly
and accurately as possible, while responding to any tactor
cues with a posture correction. Participants were instructed
to look only at the monitor, and to refrain from leaning on
the desk.
Participants completed two trials, each lasting 10 minutes.
During the first trial, tactor activation was disabled for the
first five minutes (Block 1, feedback (FB) Off) and enabledduring the second five minutes (Block 2, FB On). During
the second trial, tactor activation was enabled during thefirst five minutes (Block 3, FB On) and disabled for thesecond five minutes (Block 4, FB Off). Participants were only
asked to respond to cues and were not aware of when tactors
were enabled or disabled. There was an approximately twominute break between trials, and the text to be transcribed
was changed for the second trial.
Following completion of testing, participants responded
to eleven prompts on a comparative Likert scale survey(strongly disagree (1), disagree (2), neutral (3), agree (4),or strongly agree (5)) covering the use and instructions
for use of the posture feedback system. When responding
to prompts, participants were instructed to focus on theirinteractions with the FSS and tactor.

B. Motion capture setup
A Vicon motion capture system (Giganet MX with T-20
Series cameras (10), Vicon) was used to measure reflective
marker positions for calculation of lumbar spine angles. FSS
and Vicon data collection was synced electronically at thestart of each trial. Vicon motion capture data was collected
at 50 Hz. 3-D marker positions were extracted using Nexus
1.7.8 (Vicon) and imported to MATLAB for post processing.
All angular measures were calculated during post-processing.
C. Subjects
Five young (26.2 ± 1.3 yrs) healthy individuals (3 female,
2 male) recruited from the University of Michigan student
body participated in this study. The University of Michigan
Institutional Review Board approved the experimental protocol, and researchers obtained written informed consent fromeach participant prior to the start of the experiment.
D. User study test procedure
All participants completed a single testing session. After
providing informed consent, participants donned a polyester
exercise shirt. A FSS was secured to the lumbar region of
the back using Velcro straps. The ends of the sensors were
fastened dorsal to the T-10 and S-2 vertebrae. Three reflective
markers were affixed alongside the sensor, such that one
marker corresponded to the transverse body plane at the
T-10, L-3, and S-2 vertebrae. The markers were positioned
as close to the spine as possible with cloth tape. A single
tactor motor was placed at the T-10 end of the sensor andwas held in place with Velcro strapping. An electrode pad
was affixed to the left elbow and connected to the groundpin of the microcontroller. The experimental setup is shownin Figure 2b-d.
Participants sat in front of a computer workstation in abackless office chair. The height of the chair was adjusted
such that the thigh and shank were approximately perpendicular when seated. The height of the computer monitor was
adjusted such that participant line-of-sight was parallel to or
angled slightly below horizontal. Desk height was fixed.
At the start of each trial, participants were asked to sittall with what they believed to be proper posture, and to
slouch the lower back, not the shoulders. Measurements weretaken in both positions and were used to characterize thelumbar range of motion. A feedback threshold was then set
to 90% of the range of motion as measured by the FSS(where 100% represented the participants proper posture). If
a participant slouched to a point below the 90% threshold,the tactor was activated to provide an out-of-posture cue until
posture was adjusted back to the 90%-100% range. Tactor

E. Data Analysis Methodologies
Three-dimensional positions of the T-10, L-3, and S-2
markers were used to calculate the lumbar angle as measured
by the Vicon cameras. The motion capture data was thenresampled at the FSS time points using the interp1 functionin MATLAB to account for collection rate differences. A
linear regression was fit to determine the root-mean-squareerror for each trial between the FSS output and the calculated
lumbar angle. Percent time in the target posture zone (PIZ,
90-100% of ROM) was calculated for each five-minute blockwithin a trial (FB enabled, FB disabled). Paired t-tests wereused to evaluate differences in PIZ for the feedback on
and feedback off conditions, and to evaluate learning effects
between the first feedback off and the last feedback off
condition. Responses to the survey were evaluated on a
five-point scale and averaged across participants to gauge
sentiments towards device usability and identify any potential
bias introduced by instructional deficiencies.
III. R ESULTS
A. Flexible Strain Sensor Performance
Representative linear regressions between FSS and lumbar
angles for the two trials of all participants can be seen in
Figure 3, while time series representations for the same trials,
393

#
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
P11

Statement
Satisfied with the sensory garment
Satisfied with the ease of use of the garment
Able to complete tasks quickly with the garment
Felt comfortable using the garment
Easy to learn to use the garment
The information conveyed by the tactor was clear
Could recover easily when a mistake was madeCould recover quickly when a mistake was madeThe instruction provided by the tactor was clear
The instruction provided by the tactor was effective
in completing the given task of maintaining a certain
lumbar angle
The instruction provided with the garment was easy
to understand

Score
4.8
4.6
4.4
4.4
4.8
4.6
4.6
4.6
4.8
4.2
4.6

TABLE IE VALUATION OF USER SATISFACTION WITH THE GARMENT. T HE MEAN
SCORE OUT OF 5 CORRESPONDING TO THE L IKERT SCALE IS LISTED .

with sensor outputs converted from voltage lumbar angle via
the regressions, are shown in Figure 4.
The average lumbar angle as measured by the motion
capture system was 32.8◦ ± 4.8◦ , while the average trial
minimum lumbar angle was 28.7◦ ± 5.2◦ and the average
maximum was 37.3◦ ± 4.9◦ . The average range of motion
minimum was 19.5◦ ± 5.9◦ and the maximum was 35.0◦± 4.8◦ . The average feedback threshold, below which thetactor would provide a cue, was 33.5◦ ± 4.7◦ . The mean
FSS root mean square (RMS) deviation from motion capture
measurements across all participants and trials was 0.85◦ ±0.26◦ , and the average R2 among regressions was 0.83 ±0.14. The average regression slope was 73.4 ± 25.1 degrees
per volt. There was significant variability observed in the
regression slopes between trials ranging from 35.7 to 109.4
degrees per volt. The largest within-participant difference
between trials was 43.2 degrees per volt, though all otherparticipants had slope differences of less than 14 degrees per
volt. We hypothesize that this is due to shifting of the sensor
on the body and changes in the integrity of the interface
between the sensor body and the signal electronics.

Fig. 3. In situ sensor calibration with respect to lumbar angle. Linear
regression plots showing comparison of flexible sensor output versus thelumbar angle as measured by the Vicon motion capture system. Regressions
for all trials and participants are shown.

B. Postural Feedback Efficacy
The average percent time in the target zone (PIZ) for eachfive-minute testing block was 0.9 ± 1.9%, 94.4 ± 6.3%,
99.1 ± 0.4%, and 35.5 ± 34.8% (Block 1 FB Off, Block 2
FB On, Block 3 FB On, Block 4 FB Off, respectively; see
Figure 5). Paired t-tests indicated no significant differencesin PIZ between feedback enabled blocks (Blocks 1 and 4,
p>0.05) or between feedback disabled blocks (Blocks 2
and 3, p>0.05). There were significant differences in PIZ
between feedback on and feedback off blocks within a trial(Blocks 1 and 2, p<0.0001; Blocks 3 and 4, p<0.0001).
C. Subjective Evaluation of Sensory Feedback Garment
Results of the Likert survey are shown in Table I andindicate an overall positive reaction to the sensory feedback
garment. Average responses indicated agree to strongly agree
sentiments for all prompts. We concluded therefore that the
stiffness of the FSSs was sufficiently low and the size of theFig. 4. Flexible strain sensor and Vicon lumbar measurement outputs
for all trials. Sensor outputs are displayed as lumbar angle in degrees after
transformation via linear regression. Feedback thresholds and block dividers
are shown via dashed horizontal and dotted vertical lines, respectively.

394

Fig. 5. Percent time in the postural target zone (PIZ) in the feedback on (blocks 2 and 3) and feedback off (blocks 1 and 4) conditions. The results fromeach particplant are shown; the mean PIZ values are shown in the leftmost chart. A marked improvement in PIZ is observed when vibrotactile feedback
is provided.

the 2-5◦ error that McGinley, et al. and Cuesta-Varagas, etal. recommend for clinical interpretation [40], [41]. However,minimum R2 values for linear regressions were smaller thanother researchers have reported, with the minimum R2 for asingle trial being 0.57. Only five trials (out of 10) had R2
values above 0.85, and regression slopes were variable acrossparticipants and trials. Variability of regression slopes may
be attributable to differences in system capacitance, whichis slightly different for each user based on sensor-to-bodycontact and the differences in electrical activity of the body.

tactor small enough so that the garment was perceived tobe comfortable and sufficiently imperceptible to the wearerof the garment. The results of this subjective evaluation
thus indicate that materials and form factor of the sensor
garment are a suitable basis to build upon from a comfort
and compatibility perspective, and point to areas in which
the garment can be improved.
IV. D ISCUSSION
This study aimed to characterize the efficacy of a flexible
capacitive strain sensor for measurement of seated lumbar
angle, and to evaluate the utility of a postural feedback
system during seated workstation tasks. A motion capturesystem was used as a measure of true lumbar excursion tovalidate FSS measurements. Lumbar angles calculated frommotion capture data agreed with those measured in previous
work, indicating that seated postures during the typing tasks
were within normal ranges [39].
Linear regressions within trials yielded a mean RMS
error of 0.85◦ over a 15.5◦ range of motion, or a 5.4%
average error. In comparison, Yamamoto, et al. reported
RMS errors of 2.35◦ for flexion/extension of the lumbar
spine using a network of flexible sensors [18], and Wong,et al. reported RMS errors of less than 3.1◦ using a systemcomprising several inertial measurements units [10]. In thecase of Yamamoto, et al., flexion angle was determined as
the average output of two flexible sensors located 10 cm
laterally from the spine whereas the single sensor usedin this study was positioned directly dorsal to the spine,which may have contributed to the smaller observed RMS
error. Wong, et al. employed inertial measurement units
which were subject to drift errors that were algorithmicallycorrected, while the strain sensors in this study were lesssubject to drift. With regard to efficacy, 0.84◦ is well within

Percent time in target zone results indicate that participants
were able to successfully use vibrotactile feedback about
their lumbar angle to prevent flattening or rounding of thelower back while completing a typing task. During Block 1of the first trial, when feedback was disabled, participants
spent nearly no time in the target zone as they slouchedalmost immediately following calibration. When feedback
was activated in Block 2, participants were able to respondquickly and spent most of the block in the target zone.
Blocks 3 and 4 (Trial 2) were included to identify if any
learning effect was present after the short training period, andany learning effect would be evidenced by an increase in PIZ
between Blocks 1 and 4. During Block 3 participants again
remained in the target zone for over 99% of the block, withtime in zone dropping off after feedback was again disabled
in Block 4. While there was no statistically significant difference between Blocks 1 and 4, there was a trend towards andincreased residence in the target zone, with the average PIZ
increasing from 0.9% to 35.5%. Furthermore, all participants
experienced increased residence time in the target zone from
Block 1 to Block 4. This trend indicates that implementationof a training regimen may improve overall posture over time.
Research in motor learning has shown that the amount and395

timing of feedback presented are important to consider whendeveloping a training program, and these principles should
be considered when designing postural feedback training
programs [42]–[44].
The survey results indicate that most participants felt thatthe device was easy to use and communicated deviations
from the target lumbar angles well, even with a single
tactor feedback display (Table I). Furthermore, the simple
instruction provided prior to use of the system was sufficient
to elicit an understanding of how the device functioned
and proper responses to vibrotactile cues, even without theimplementation of a practice session. Anecdotally, verbal
comments made to the research team indicated that participants did not notice the FSS but did find the Velcro strappingused to position the sensor to be a source of some discomfort.
The use of Velcro fasteners was employed to allow foradjustability of the system to ensure proper placement of
the sensors relative to the spine and specific vertebral levels.
Overall, the results of this study indicate that a single
flexible strain sensor is an effective tool for monitoring seatedlumbar angle and can be used in tandem with vibrotactile
feedback as both an aid and training tool to guide seatedposture. To our knowledge, this is the first study to employ
an elongation sensor with vibrotactile cuing capabilities tomonitor and guide seated posture. Further development of
this technology has enormous implications for workplace
health and safety, as soft sensors of this nature can be incorporated directly into clothing, allowing eventual widespreadmonitoring of daily posture that could elucidate trends inthe development and treatment of musculoskeletal disorders common to sedentary workplace settings. This would
allow for evidence-based development of postural training
programs that employ vibrotactile feedback to encourage
beneficial postural habits.
This study was not without limitations. The small population size and age demographic are useful for proving theefficacy in a pilot study but limits the statistical power ofthe results. The participant population must be expanded tobetter understand sensor and feedback system behavior andreception across the age and body-type spectrum. The sensor
and feedback system were also tested in an ideal setting
whereby motion was mainly limited to torso flexion andextension in the sagittal plane. Therefore, the efficacy of thesensor is unknown in more commonly experienced situations
where there is also flexion in the coronal plane and torsion
along the spinal axis. Future studies should then focus on
improvement to measurement capabilities beyond a single
direction of motion and may require the development of a
network of sensors to improve posture reconstruction accuracy. Additionally, future work should aim to characterize theeffect of varying sensor placement on measurement errors,
as sensor location is likely to vary as sensors are embedded
directly into garments such as shirts.

subsequently evaluated the efficacy of the system for both
postural measurement and postural guidance efficacy. A
flexible strain sensor was paired with a vibrotactile display
and implemented to measure lumbar angle and provide
corresponding postural feedback. The results indicate reasonable measurement errors when compared with other posturemonitoring devices, and current acceptable errors observedin clinical settings. Furthermore, participants successfully
utilized postural feedback to maintain lumbar angles in target
zones. These findings indicate that the device effectively
provides postural aid and shows promise as a postural trainer.
Further research should focus on implementation of multisensor networks to potentially improve measurement quality
and the impact of training with postural feedback devices.
ACKNOWLEDGEMENTS
The authors would like to thank John C.S. McCaw for his assistance in sensor development, and Tian Bao, Catherine Kinnaird,
and Christina Mao for their help in experimental preparation andexecution. MCY was supported by a National Science Foundation
Graduate Research Fellowship (Grant DGE-1333468). This work
was funded by a grant from Intel Corporation.

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