Gait analysis is a rapidly expanding and evolving research area with application to biomechanics and rehabilitation. Current wearable technology that can be used to collect gait information is becoming more accessible in terms of cost and usability compared to lab counterparts which can be expensive and require training to setup and use. This paper provides an exploratory analysis of knee angle versus angular velocity of the lower leg for six healthy participants for at four walking speeds (1.8 km/h, 2.7 km/h, 4.5 km/h, 5.4 km/h) on a treadmill and walking over the ground at 3.9 km, using the Xsens Inertial Measurement Unit. These phase portraits provide a rich data source for qualitative comparison. In future objective gait analysis based on suggested gait parameters can aid with rehabilitation of dysfunctional gait.
Gait Analysis (GA) is an area of research that is continually expanding and evolving
across a wide range of domains such as, healthcare, sport science and surveillance.
There are a plethora of medical gait applications such as the evaluation of prosthetics,
assessment of surgical procedures [
During the past four decades the measurement and assessment of gait has evolved
rapidly; tools and technology now provide an objective, quantitative evidence-based
approach. Current clinical practice for motor assessment of the lower limb in stroke
survivors is based upon a battery of tests, such as the two-minute walking test,
timedup and go, berg balance scale, fugl-meyer assessment, motor assessment scale,
rivermead motor assessment of movement, motricity index and stroke rehabilitation
assessment of movement. All of the aforementioned motor assessment scales predate the year
1997 and have an average age of 31 years. Although they provide a quantitative score
they are based upon human clinical observation and are subject to inter- and intra-rater
variability. Additionally, the majority of these assessment approaches are not capable
of detecting subtle changes in motor function particularly at the top end of assessment
scales as a ceiling effect often occurs [
Advances in technology used to measure gait have been instrumental in the evolution
of GA. Biomechanical movement of the human body is complex and therefore effective
GA requires information such as kinematics, ground reaction forces and influence of
muscle activity. Motion Capture (MC) strives to measure kinematic data in an accurate,
valid and unobtrusive manner. There are two competing MC technologies: optical
capture and the use of force plates to measure plantar pressure. Each offers advantages and
disadvantages depending on the context of the application being considered [
Optical Motion Capture (OMC) systems use cameras based upon active or passive
markers to accurately detect the position of body worn markers within a 3-dimensional
space. There are a number of commercial OMC systems available such as Vicon,
Qualysis and Codamotion. These systems tend to be very accurate at sensing marker position
to within the sub-millimeter range, however this accuracy heavily relies on the ability
of a researcher to place markers accurately and repeatably. Various protocols exist to
help locate joint centers but these differing conventions can produce a varied set of
results [
Traditionally force plates were designed to record single steps with high accuracy
and resolution. Pressure sensing technology has evolved through the incorporation of
this technology into instrumented walkways such as the GAITRite mat facilitating GA
of a sequence of steps [
Over the last 5 years, there have been significant advances in technologies for inertial
motion capture (IMC) systems; in particular insole pressure sensor recording and the
measurement and wireless transmission of the electromyogram (EMG). Insole pressure
sensing technology has benefited from [
Inertial Motion Capture (IMC) systems offer benefits over OMC systems due to
their portability, wearability and decreasing costs. IMC sensor units provide the
opportunity for more practical, untethered data capture free from the constraints of an
indoor observation area. Deploying outside a gait laboratory environment can facilitate
diverse spatial settings such as stairs, open space, more natural terrain or other indoor
areas. The setup time is shorter repeatability for measurement of joint angles during
walking [
The fundamental assumption of an underlying rigid body such as the human
skeleton can be violated by the movement of overlying soft tissue known as skin artefact.
This is particularly evident when there is an excess of soft tissue or during highly
dynamic movements [
In this study we conducted exploratory GA on healthy participants (n=6) using data
gathered from a wearable IMC system. The GA focuses on a subset of parameters to
assess feasibility, validity and diversity of gait amongst a healthy cohort. Baseline data
were collected to establish a normal set of gait parameters. Factors that may contribute
to variations in the data relate to but are not limited to gender, age, height and weight.
Walking therapies are part of the rehabilitation pathway as defined by the NICE
guidelines [
In a data driven approach the quality and volume of the data being collected plays a vital role in the capability of a computational model to provide accurate, objective and repeatable assessment of gait. Therefore, it is import to control the recording of activities by applying a consistent clinical protocol. Although the number of participants is low (n=6) the size of the dataset can still be adequate to provide sufficient quality of data as the number of steps can reach 2,400. In addition each step can provide further information angle, velocity for 7 locations on the lower body.
Participants wore an IMC system and a pair of smart insoles to collect kinematic and ground reaction forces during walking activities. Participants were required to complete a short anonymous questionnaire that provided information on their age, gender and weight.
Inertial Motion Capture Configuration. The IMC system was configured to investigate lower limb which involved donning 7 Inertial Measurement Units (IMUs) using a velcro based strapping system as shown in Fig. 1. The IMUs were attached to the pelvis (sacrum), left/right upper leg (thigh), left/right lower leg (shank) and left/right foot. A number of anatomical measurements (body height, shoulder width, arm span, hip height, hip width, knee height, ankle height, foot size, shoe sole height) were taken from the participant to help complete the calibration. The IMC was re-calibrated before each recording session. Participants. A group of 6 healthy adults participated in the study. A summary of demographic information is provided in Table 1.
Initial feasibility testing was conducted to evaluate at an observational level that the wearable systems were fit for purpose in terms of robustness, reliability, usability, comfort, repeatability and set-up time. Walking activities have been designed to include walking at differing speeds, walking on a treadmill versus over the ground. The study also included turns as this is an important constituent of GA.
Given that the potential use case scenario for this research will be typically elderly post-stroke survivors some slower walking speeds were included. Participants performed two walking activities. The first involved walking indoors over the ground on a flat smooth surface within a gait laboratory environment for a distance of 10m and turning. This activity was repeated for 2.5 mins at a comfortable walking speed, selfselected by the participant. The second activity required participants to walk for 2.5 minutes at 4 different speeds on a treadmill with zero incline for a total walking time of 10 minutes. The four speeds using the treadmill were: very slow (0.5m/s), slow (0.75m/s), medium (a comfortable speed self-selected by the participant, either 1m/s or 1.25m/s) and fast (1.5m/s). The fastest walking speed test is similar to the 2-minute walking test which is a clinical assessment. 3
The Xsens system has already been well validated against OMC systems and has been
reported to have a coefficient of multiple correlation > 0.96 for all joints during
flexion/extension for level walking activities [
The two phase portraits shown in Fig.2 are highly correlated and present knee angle against angular velocity of the lower limb for participant 1. These phase portraits show the dynamic nature of the knee during walking on a treadmill at a 5.4 m/s for 2 minutes. A single gait cycle is represented by one phase which can be seen as a closed loop. The phases are plotted on top of each other as each gait cycle is repeated, it shows high levels of correlation but with some dynamic and chaotic variations. The variation between gait cycles in the first test can be seen in red while the blue lines show the variation of gait cycles in the second test. Since both tests were recorded within 30 minutes and under the same conditions the variation which is expected to be minimal between walks can be observed by comparing red and blue lines.
To provide a statistical measure of correlation an average gait cycle was computed for both walks and a correlation coefficient calculated by comparing both average gait cycles. The average phase portraits can be seen in Fig. 3, these are highly correlated as expected (r=0.9993), this is the Pearson correlation coefficient as reported by MATLABs 2-dimensional correlation function.
The next stage of GA was to compare all of the walking activities for participants across a number of different walking speeds; this involved 2 minute treadmill walks at 1.8 km/h, 2.7 km/h, 3.6 km/h or 4.5 km/h and 5.4 km/h and a final 2 minute walk over the ground for 10 metres with a turn. The phase portraits of these walking activities can be seen in Fig. 4 for participant 1. Initial observations show correlation between walking speed and the area enclosed within the phase portraits. A greater range of motion and increased angular velocity should result in an increased area within the curves. Additionally, as the speed increases the variability in the phase portraits reduces to produce a more rhythmic and stable gait cycle, this is particularly for true for walking activities on the treadmill. It seems that this effect is caused by a combination of the treadmill and higher walking speeds. To quantify the relationship between the area of the phase portraits and speed an average phase portrait was computed for each walk. These were then used as a basis to calculate an average area of each phase portrait and to plot these against speed to quantify any relationship and how it may change across the cohort. Observation of Fig. 5 shows a common pattern of increased area equating to increased speed. Participants 4 and 5 were both in their early seventies and yet it is interesting that they were able to maintain walking speeds of 1.8 km/h, 2.7km/h and 3.3km/h with a reduced area, this may imply a greater sense of control by reducing the stride length. Comparing treadmill walking activities against over the ground walking in Fig. 4 shows that the variation in knee angle is more apparent while walking over ground. As the walking over the ground activity included turning 180 degrees every 10 metres there are a significant amount of turns (n~=16) within a 2 minute period. Therefore it makes it more difficult to attribute the increased knee angle variation as a direct result of walking over the ground. The second noticeable feature of the phase portraits for walking over the ground is that there is a mirroring effect which results in two prominent distinct phases. These are a direct result of walking in two opposite directions and may be combined into a single phase if it is possible to adjust the data to accommodate walking direction. This would be a useful analysis feature as it would allow all walking activities to be analyzed and compared irrespective of walking direction. 4
This paper provided a demonstration that repeatable and interpretable gait analysis is possible using wearable IMC technologies. Phase cycles and repeatability were assessed by observation and quantitative measurement. Further work will be conducted with more healthy participants and incorporate between-day and inter-rater reliability. There is a significant opportunity to quantify GA through the measurement of spatial, temporal, spatiotemporal and other phasic parameters, as listed in Table 2. Additional features that can be used in assessing the repeatability of normal gait can be derived from the normative dataset. Using these measurements and features will facilitate gait modelling for a healthy population. A gait model for a healthy population can be used to provide a reference to any new gait information that should be compared from a disease specific cohort such as stroke survivors.
There are a number of use cases where gait analysis can be beneficial for both the clinician and patient such as Parkinson’s disease, cerebral palsy, lower-limb osteoarthritis, post-stroke and diabetic neuropathy. The authors propose to develop a computational gait model based on intelligent data analysis using non-linear techniques to provide an objective and quantifiable assessment of pathological gait for post-stroke that is accurate, robust and repeatable.
Temporal Cadence (steps/min) Step Time (s) Stride Time (s) Stance Time (s) Swing Time (s) Single Support Time (s) Double Support Time (s)
Spatiotemporal Gait Speed (m/s) Stride Speed (m/s) Stride variability Smoothness Centre of Pressure
Phasic Stance Time (%GC) Swing Time (%GC) SST (%GC) DST (%GC) Gait analysis of the normal population and of different pathologies is an area of research that is expanding rapidly. There are a number of competing technologies that can provide gait information, two such competing sets are research gait lab technology and wearable technology. The former tends to be more expensive, less flexible and with longer setup times often requiring specialised training. With recent advances, wearable technology can offer a cheaper, more accessible, less restrictive and easier to use option without comprising on the accuracy or quality of the information. This is particularly true of recent advances of IMC systems.
The Xsens IMC system captured kinematic data from walking. Due to the
exploratory nature of this study only the dynamic nature of knee angle during walking was
considered; the gait variation across a number of walking speeds on a treadmill and
walking over the ground and gait variation across the population were assessed. Future
research aims to build a computational model that can be used to assess a user’s gait
during ambulation. A large set of features will be generated to serve as input to the
model and as such can be configured in multiple ways via feature selection to ascertain
the optimal model and as a result what are the optimal features, technologies and
sensors. There is a significant body of research to suggest that spatial temporal gait
parameters provide such a feature set [
The authors would like to acknowledge the School of Computing at Ulster University and SENDoc (www. Sendocnpa.com) project in the ongoing support of this doctoral research.