Berkeley MEng Bioengineering capstone project final report

1. EVOLUTION DEVICES - HARDWARE CAPSTONE REPORT
Wearable gait tracking for clinical rehabilitation
Ross Schneider1, Hayley Boyd2 and Anisha Basu2
1 Mechanical Engineering, University of California, Berkeley
2 Bioengineering, University of California, Berkeley
Summary—In collaboration with Evolution Devices, we created a lower body motion tracking system to provide richer data for the
development of their assistive walking device, EvoWalk. Additionally, our system has the potential to enhance clinical gait analysis with
our detailed motion data.
Gait impairment is a widespread problem often caused by aging, stroke, and neurodegenerative conditions. These impairments can
lead to fall injuries, reduced mobility, and limited independence. Clinicians typically use qualitative observation methods to diagnose and
track gait impairment. Motion capture technology can enable quantitative analysis to enrich clinical diagnoses and supplement qualitative
assessments. Unfortunately, modern motion tracking systems have limited availability due to high cost barriers and requirements for a
dedicated lab space.
We designed GaitTrac, a wireless, wearable lower body motion tracking system. To reduce cost we used off-the-shelf parts for
communications, processing, and inertial measurements. GaitTrac calculates metrics such as circumduction, joint angles, and symmetry
of movement to construct an individual’s gait pattern; improving methods of diagnosis and rehabilitation at a greatly reduced cost. This
system shows promise to support the development of EvoWalk and to give quantitative gait analysis a broader reach in clinical settings.
INTRODUCTION
The Gait Problem
A third of people over the age of 60 suffer from impaired
gait, which can lead to loss of personal freedom, falls, and
injuries (Mahlknecht et al., 2019). Falls are the leading cause
of fatal injury and trauma-related hospital admission among
older adults, and costs around $50 billion each year (NCOA,
2020). In addition to age-related gait impairment, there are
many neurodegenerative disorders that can reduce mobility
such as Parkinson’s Disease, Multiple Sclerosis, and Ataxia
(Fig. 1) (Pirker and Katzenschlager, 2016).
Assistive Devices
Evolution Devices, a start-up in Berkeley, has developed
a system to help improve mobility for individuals with
restricted gait (Fig. 1). EvoWalk is the first Functional Elec-
trical Stimulation (FES) device (Fig. 2) that uses artificial
intelligence to reliably track personalized walking patterns
and precisely stimulate the right muscles at the right time to
treat foot drop and other symptoms of paralysis (Alderton,
2019). Early trials show that EvoWalk can prevent fatigue
and improve the walking form of multiple sclerosis patients,
ALS patients, and stroke victims (Evo, 2020).
Fig. 1: Examples of impaired gait: Hemiplegia - swing at hip and
knee with leg circumduction, pelvic tilt; Parkinson’s disease -
small shuffling steps, difficulty picking up feet; Cerebellar ataxia -
wide-based gait with clumsy staggering movements; Footdrop -
dropping of forefoot resulting in high stepping or swinging gait;
Sensory ataxia - loss of sensation resulting in unsteady stomping
gait (Medxclusive, 2020)
Our Objective
Evolution Devices currently uses a camera system to co-
llect motion data in order to create personalized stimulation
patterns based on an individual’s unique gait. While this sys-
tem is accurate, the camera system is not portable and requi-
res elaborate calibration set-up. We hope to help Evolution
Devices collect better motion data with our project, GaitTrac.
Our system is comprised of seven wearable motion
sensors that will collect lower limb motion data (Fig. 3)
and translate that data into quantitative gait metrics (i.e.
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Fig. 2: EvoWalk from Evolution Devices (Evo, 2020)
circumduction, symmetry, step length, joint flexion, etc.).
Modern software filtering techniques and real-time embed-
ded programming solutions facilitate a constant stream of
motion tracking data which gives rise to better algorithms.
These metrics will allow for more precise stimulation
patterns and the portability of the system will allow more
patients access to individualized gait information. In the near
future GaitTrac will also be released to clinics to provide
affordable, user-friendly, physician-requested gait metrics
for aiding in the diagnosis of unhealthy walking patterns.
THEORETICAL BACKGROUND
Simplified Kinematic Joint Alignment
Kinematic data is extracted from 7 inertial measurement
units (IMUs): one attached to the pelvis and each thigh,
shank, and foot. The rotation of these units relative to one
another allows extraction of joint angles without need for
measuring the lengths of these body sections or knowing
the precise locations of the units on the body. The ability
to extract these angles is firstly dependent on mathematical
alignment of the output data, which is given in quater-
nion form thanks to an on-board algorithm that fuses the
IMU’s accelerometer, gyrometer, and magnetometer data.
Quaternions are a four dimensional number system used
to represent rotations. Spatial alignment is accomplished
by employing the following two properties of quaternions:
1) multiplication of two quaternions yields the quaternion
describing the rotation between the two original quaternions,
and 2) multiplication of a quaternion by its inverse yields
a unit quaternion, defined here as [1, 0, 0, 0]. To invert a
quaternion, the non-real components are simply negated
(Dam and Lillholm, 1998).
The data from each unit can thus be aligned in the same
global frame by taking a alignment point when the units are
in a known configuration, such as a straight line. For each
unit, the quaternion data at this point is inverted and multi-
plied with subsequent readings from that unit. In this way,
all devices read as the unit quaternion at the alignment point,
and subsequent readings are given as rotation relative to that
alignment state. Then quaternions can be converted into ot-
her forms of angle readings, such as axis angles which allow
intuitive understanding of the joint angles.
Fig. 3: GaitTrac: Seven node motion tracking system located on
the pelvis, thighs, shins, and feet
Wireless Network Synchronization Protocol
Our wireless network consists of one router and 7 weara-
ble devices communicating on the 2.4 GHz spectrum using
the 802.15.4 protocol. The router is attached to the user’s
computer via USB and requests and receives data from the
wearable devices in real-time. Since we are fusing the data
from the multiple wearable devices, it is important that they
all remain synchronized to the same clock. Each wearable
device is independent of the others which means that there is
no shared clock between them, and since they are wireless,
sharing a clock poses bandwidth issues as the clock is much
faster than it is possible to communicate. From Kusy (2004),
a common method of clock synchronization on wireless
devices is called the flooding time synchronization protocol,
or FTSP. This protocol periodically sends messages with
real-time clock information so that each node in a wireless
network can calculate its own offset and correct the internal
timing.
We have implemented a modified version of FTSP in
which the wearable devices clocks are synchronized at 40
Hz. This is done by sending a request packets to each of the
wearable devices at the same time, known as a broadcast. We
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3. EVOLUTION DEVICES - HARDWARE CAPSTONE REPORT
assume that this broadcast reaches and is processed by the
wearable devices at the same time (due to the speed of light
and similar hardware). We base all of the real-time proces-
sing and transmission handling with the time of receipt as a
basis. This provides all of the wearable devices with the same
real time clock.
MATERIALS & METHODS
Low-Profile Motion Sensor Circuit
This platform works by sending data from 7 wearable
devices to a router collecting the data. The router then ports
the data directly to the PC. Each wearable device contains
a microcontroller (MCU), inertial measurement unit (IMU),
and an radio frequency (RF) module. The router consists
of an MCU and 4 RF modules. We used 4 RF modules on
the router so we can collect data over 4 different wireless
channels and increase overall throughput. By operating
on 4 different channels we are able to communicate with
4 wearable devices simultaneously since there will be no
overlap of air time on any of the radios. This increases trans-
mission speeds greatly since only 2 communication cycles
need to occur instead of 7 (4 devices per communication
cycle instead of 1). We optimize the channels for minimum
interference by testing the transmission speed before testing
in an area.
To develop this platform, we have utilized several rapid-
prototyping procedures to reduce the cost of and increase the
speed of development. At first we developed our prototype
wearable device using breakout boards of the Adafruit
nRF52832 feather (MCU), Adafruit BNO055 9-DoF IMU,
and a Digi xBee3 (RF module). We developed the router
using a Teensy 4 (MCU) and 4 Digi xBee3 modules. We
chose the Teensy 4 because of its radically high clock
speed of 600 MHz compared to the nRF52’s 60 MHz.
This increase in speed sacrifices low power consumption.
The Teensy 4 consumes 100 mA and the nRF52 consumes
20 mA. Additionally, the Teensy has 7 hardware UART
connections which allow for simultaneous communication
between the 4 xBees without the need of a multiplexer. Too
keep a low profile for the wearable device we used small,
105 mAh batteries so low power consumption was a critical
design decision.
We developed PCB motherboards for the hardware. On
the wearable an integrated switch allows for simple on/off
user interaction and the PCB allows for a unique low-profile
design. We used 3D printing to develop the enclosure for the
wearable device and router. They were printed on Ultimaker
3 printers. Figure 4 shows a wearable assembly, and figure 5
shows the router assembly.
The router also has 4 buttons for human interaction such
as starting/stopping the stream of data, resetting global frame
alignment, requesting device IDs, restarting the system, etc.
The buttons can also be programmed for future needs.
Our first router and wearable prototypes did not perform
as well as their breadboard counterparts as they had less
Fig. 4: Assembled wearable device prototype v1
Fig. 5: Assembled router prototype v1
than 50% successful transmissions after moving more
than 0.5 meters away from the router. After re-reading the
manufacturer literature for the xBee modules, we learned
about the concept of keepout areas for PCB antennas. The
keepout area of the xBee module is shown in figure 6 (Digi,
2020).
Fig. 6: Manufacturer recommended keepout area.
Fig. 7: Assembled router prototype v2.
We promptly redesigned the router and wearable device
PCBs to allow for keepout areas which are compliant with
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Fig. 8: Assembled wearable device prototype v2.
the manufacturer’s recommendations (Fig. 7 and Fig. 8, res-
pectively). After constructing the new devices, the improve-
ments resulted in a 97% overall successful transmissions)
which is within our initial target of <5% of packet loss.
Device Securing Wearable Band
To use this platform, a user will attach the wearable
devices to their lower limb segments and record the motion
capture data recorded by the IMUs. As stated in Cerveri and
Ferrigno (2005), rigid attachment to soft flesh of this nature
introduces motion artefacts which may impede accurate
results as we are assuming the devices are attached to the
skeleton. To mitigate this as much as possible, we designed
adjustable neoprene straps with a Velcro layer to attach to
the devices. Figure 9 shows the strap on a user’s shank and
foot.
The elastic properties of the neoprene allow for a constant,
compressive force on the surface of the wearable towards the
leg segment for internal stabilization for motion artefact re-
duction. Custom blue tabs were designed for a comfortable
and reliable grip when securing and removing the bands.
Software and Firmware
Embedded Firmware
The embedded software encompasses the programs
running on the router and wearable devices. They were
developed in the Arduino IDE and the modified version of
it, Teensyduino.
Upon booting, the wearable devices will wait for a signal
from the router, so the user boots all of the wearable devices
that will be used. Each device is associated with its intended
position on the body, such as the pelvis, thigh, shank, or
foot. Once powered, the user then powers the router which
is connected to the PC. The router sends a request over all
channels to find all possible wearable devices in the area.
Once the router has discovered all of the available devices, it
assigns them unique IDs and keeps track of the connection
number.
Fig. 9: Neoprene strap affixed to shank and foot, with blue tabs for
grip
Fig. 10: Oscilloscope reading showing receipt of request.
Once the user wishes to collect data, the button on the
router is pushed which will send a command to the wearable
devices at a user-specified frequency. Once the wearable
device receives the request for a data point, it will access
the IMU, and store an 8 byte array of values which can be
unpacked to represent a quaternion. The synchronization
of two wearable devices using this method was shown
to synchronize at 0.5 microseconds (Fig. 10). The rise in
voltage corresponds to the interrupt when the wearable
device receives a request for data from the router. We have
the devices synchronized within 4 microseconds.
This is then stored into a packet along with the timestamp
and unique device ID and sent to the router. If two devices
exist on the same channel, one of them will wait until the
first device is done transmitting before sending its packet.
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5. EVOLUTION DEVICES - HARDWARE CAPSTONE REPORT
Once the router has received all of its expected packets (1
per device), it will send the data to the PC. It will then wait to
request another packet to match the user-specified frequency.
If this frequency exceeds the maximum throughput then the
router will dynamically adjust the request throughput to be
at a maximum.
The router also keeps track of the time of its request as
well as the time each byte from each packet comes in. This is
to handle possible data losses. if one packet is dropped or one
of the wearable devices does not receive a data request, the
router must time out to continue streaming data. If there is
no handler for dropped packets or missed requests, then the
streaming will stop while the router waits for the data to co-
me through. This is the limitation of wireless, asynchronous
communication. The duration of timeouts were empirically
determined to be 25 ms for a global timeout (request time to
the time all bytes are received) and 2 ms for a local timeout
(time bytes on one channel start coming in to the completion
of that packet). During testing these durations approached,
but never exceeded these values.
Real-time PC-Based Data Analysis
Angle extraction methods were originally developed using
MATLAB, with the benefit of quaternion-manipulation fun-
ctions from the MATLAB Aerospace Toolbox. All MATLAB
data processing was done post-recording. Following this
initial algorithm proof-of-concept phase, all angle extraction
code was rebuilt in Python3 to be more compatible with the
GaitTrac ecosystem. This program performs serial reading
of device output quaternions from the router to the PC,
where quaternion multiplication and conversion occurs in
real-time for immediate human-readable data. The Python
module "pyquaternion"was used for quaternion manipula-
tion, specifically quaternion multiplication and angle type
conversions.
During a recording session, the raw quaternion data is
received by a Python script in real-time serial fashion. This
script performs the quaternion alignment process described
earlier by retaining an inverted alignment point reading for
each node, against which the subsequent data is multiplied.
Joint angles are then calculated using quaternion multiplica-
tion between adjacent sensors. For example, calculating the
angle between the the pelvis and right thigh rotations yields
the right hip angle. Data for each joint is converted into axis
angle format and plotted live during the recording. Axis
angles were chosen for live plotting because of their intuitive
simplicity. This format reduces the data to a single angle
around a potentially moving axis, which reduces the amount
of information that is conveyed when presented without the
axis data. However, reducing the data to just one line per
joint angle (compared to three for Euler angles or rotation
angles) is most intuitive for real-time plotting.
Data formats including raw quaternions, alignment-
calibrated quaternions, axis angles, rotation angles, and Euler
angles are available as options for both real-time and post-
recording applications.
Fig. 11: Validation testing. "Janky Leg", the wooden angle testing
device.
Bench Testing for Data Verification
To support early validation and testing, a bench testing
device was created to simulate simplified motion of a joint
with one degree of freedom. The device consists of two
wooden arms: one in a fixed vertical position and the other
attached via a hinge allowing for variable angle between the
two arms ranging from 180◦ to about 45◦(Fig. 11). A 3D
printed protractor is attached to this joint to allow easy angle
visualization, and simple brackets were designed in CAD
and 3D printed to allow attachment of the breadboard-stage
wired devices to the arms.
In conjunction with the angle testing device, an open sour-
ce video analysis tool (Kinovea) was used to verify angle
readings. Video was taken while breadboard-stage units re-
corded measurements while attached to the arms. For a ty-
pical validation procedure, the upper unit remains stationary
while the lower unit is moved with the arm from the initial
position (180◦) to 135◦ or 90 ◦ multiple times. Angles we-
re extracted from the video using Kinovea at various points
in the recording and compared to the angle outputs from the
IMU devices.
Walk Test Protocol Product Validation
A 10-meter walk test is a common method for clinical gait
analysis. Typically, this test is used to determine walking
speed of an individual over short distance by having them
walk a 10-meter straight line path at their comfortable speed.
A stopwatch is started at the two-meter mark and stopped at
the 8-meter mark, to remove variability due to acceleration
and deceleration (AbilityLab, 2014). It was determined that
GaitTrac should be implemented on this same protocol to
optimize simplicity of use for clinicians.
For GaitTrac to be successful in the 10-meter walk test
protocol, the following needs were determined: 1) Conti-
nuous 40Hz readings with adequate accuracy must be taken
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Fig. 12: Output plot showing hip and knee angles over time,
corresponding to three exaggerated steps.
for the duration of the walk test. 2) Relevant walking metrics
must be able to be extracted from GaitTrac recordings follo-
wing this protocol. 3) Involvement of the wearables must not
significantly change a patient’s walking speed or other wal-
king parameters. Continuous and wireless 40Hz readings are
attained with hardware listed in the Methods section above.
Accuracy validation using a state-of-the-art camera system
was planned, however due to extenuating circumstances ac-
cess to this system has been delayed. The ability to generate
quaternion rotation data for each joint has been demonstra-
ted, which ensures there is adequate information to extract
relevant gait metrics. Finally, care was taken to minimize the
size and weight of the wireless nodes that are attached to
the body. Camera system validation tests have not yet been
conducted, however it is recommended that trials are run in
which walking metrics are gathered using the camera system
with and without the GaitTrac nodes attached to the body in
order to confirm that they do not impede the patient.
RESULTS & DISCUSSION
Gait Metrics Output
GaitTrac is a simple and practical means to generate rich
and quantitative gait data. This small, portable device can be
readily available to a physician or physical therapist in their
office or elsewhere. After simply powering up the system and
strapping it to the patient, a walk test can be performed. As
per a standard walk test, the patient is asked to walk at a
comfortable pace over 10 meters while the device is running.
The physician can watch the joint angle readings in real ti-
me. An example of joint angle outputs can be seen in Figure
12. Afterwards, a report is generated in which step and stri-
de length, joint angles, dorsiflexion, plantarflexion, and gait
symmetry are robustly characterized in an easy-to-read com-
bination of graphs and charts.
Clinical Impact of GaitTrac
Currently physicians diagnose gait impairments through
subjective measures, based on their best medical opinion and
experience. While this method of diagnosis is widely accep-
ted and more than sufficient, we believe that GaitTrac can en-
rich the diagnosis and treatment process. GaitTrac will provi-
de physicians with quantitative data and highlight the nuan-
ces of impaired gait. Physicians will be able to see how gait
metrics change over time with rehabilitation therapy and al-
ter the treatment plan accordingly for more targeted, perso-
nalized medicine. GaitTrac will change the field of clinical
gait analysis by providing motion tracking data without the
high cost, highly structured lab space, and trained personnel
required by currently available methods.
Research Implications
In addition to enriching clinical gait analysis, GaitTrac can
provide accurate real-time data for gait research. Since our
system does not require a special space or training, GaitTrac
will lower the overall cost of research and development. Our
specialized software and hardware generates key motion data
that can rapidly propel rehabilitation engineering research.
Specifically for Evolution Devices, GaitTrac can increase the
volume of gait data collected per patient and allow Evolution
Devices to fine tune EvoWalk protocols to each individual’s
personalized walking pattern.
Future Works
Moving forward GaitTrac will be further developed to add
functions such as a portable charging case, more nuanced
gait metrics, and improved device-securing wearable bands,
including a new design to secure the foot sensors more
comfortably.
Presently, we are able to calculate easily visible gait
metrics such as joint angles, step and stride length, and
gait symmetry. There are still many more subtle aspects of
walking movement that our system will be able to capture
but that we have not quantified. Physicians will be consulted
to determine what these gait metrics are and how they
present in impaired gait.
During data collection trials, we noted that the neoprene
wearable band tends to slip while walking. We plan to upgra-
de these bands by adding some non-slip features to combat
any slippage. Additionally, while our device-securing bands
are very slim, the foot strap was felt while walking. Even
though it does not cause discomfort, the noticeable feel of
stepping on the band could cause a user to alter their gait.
We are working to redesign the foot sensor securement
mechanism to ensure we capture a user’s natural gait pattern.
Validation testing that had been planned and delayed due
to labspace inaccessibility will need to be performed. This
includes walk tests performed along with a gait lab camera
system in order to compare outputs of both measurement
modalities and generate quantitative accuracy values for
our system. Camera measurements should also be used
to compare walking metrics of patients with and without
the GaitTrac wearables attached to their body, in order to
show that the devices do not affect gait to a significant extent.
While we have discussed the significant clinical and tech-
nological impacts of GaitTrac, more work remains to be done
to fully commercialize the final product. Once the system is
fully productized we hope to release GaitTrac for use in cli-
nical and research settings.
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7. EVOLUTION DEVICES - HARDWARE CAPSTONE REPORT
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