Manual physical balance assistance of therapists during gait training of stroke survivors: characteristics and predicting the timing
Haarman et al. Journal of NeuroEngineering and Rehabilitation
Manual physical balance assistance of therapists during gait training of stroke survivors: characteristics and predicting the timing
Juliet A. M. Haarman 0 1 2
Erik Maartens 0 1
Herman van der Kooij 0
Jaap H. Buurke 0 1
Jasper Reenalda 0 1
Johan S. Rietman 0 1
0 Department of Biomechanical Engineering, University of Twente , Drienerlolaan 5, 7522 NB Enschede , the Netherlands
1 Roessingh Research and Development , Roessinghsbleekweg 33b, 7522 AH Enschede , the Netherlands
2 Roessingh Research and Development , Roessinghsbleekweg 33b, PO Box 310, 7500 AH Enschede , the Netherlands
Background: During gait training, physical therapists continuously supervise stroke survivors and provide physical support to their pelvis when they judge that the patient is unable to keep his balance. This paper is the first in providing quantitative data about the corrective forces that therapists use during gait training. It is assumed that changes in the acceleration of a patient's COM are a good predictor for therapeutic balance assistance during the training sessions Therefore, this paper provides a method that predicts the timing of therapeutic balance assistance, based on acceleration data of the sacrum. Methods: Eight sub-acute stroke survivors and seven therapists were included in this study. Patients were asked to perform straight line walking as well as slalom walking in a conventional training setting. Acceleration of the sacrum was captured by an Inertial Magnetic Measurement Unit. Balance-assisting corrective forces applied by the therapist were collected from two force sensors positioned on both sides of the patient's hips. Measures to characterize the therapeutic balance assistance were the amount of force, duration, impulse and the anatomical plane in which the assistance took place. Based on the acceleration data of the sacrum, an algorithm was developed to predict therapeutic balance assistance. To validate the developed algorithm, the predicted events of balance assistance by the algorithm were compared with the actual provided therapeutic assistance. Results: The algorithm was able to predict the actual therapeutic assistance with a Positive Predictive Value of 87% and a True Positive Rate of 81%. Assistance mainly took place over the medio-lateral axis and corrective forces of about 2% of the patient's body weight (15.9 N (11), median (IQR)) were provided by therapists in this plane. Median duration of balance assistance was 1.1 s (0.6) (median (IQR)) and median impulse was 9.4Ns (8.2) (median (IQR)). Although therapists were specifically instructed to aim for the force sensors on the iliac crest, a different contact location was reported in 22% of the corrections. (Continued on next page)
(Continued from previous page)
Conclusions: This paper presents insights into the behavior of therapists regarding their manual physical assistance
during gait training. A quantitative dataset was presented, representing therapeutic balance-assisting force characteristics.
Furthermore, an algorithm was developed that predicts events at which therapeutic balance assistance was provided.
Prediction scores remain high when different therapists and patients were analyzed with the same algorithm settings.
Both the quantitative dataset and the developed algorithm can serve as technical input in the development of
(robotcontrolled) balance supportive devices.
Stroke survivors with a Functional Ambulation Category
(FAC) of 3 often experience reduced balance control and
difficulties with independent ambulation [
therapists focus on improving these aspects in
rehabilitation therapy, for instance by training tasks that
specifically relate to Activities of Daily Living (ADL’s) such as
overground walking in and around the house [
During these training sessions, therapists continuously need
to supervise patients when they walk. When patients
lose their balance, therapists provide manual physical
balance assistance to the body in the form of small
corrective forces. In any given situation, therapists consider
patient-specific examination findings to determine if and
when balance assistance is needed, such as a patient’s
specific muscle strength, isolated movement capacity,
reaction or movement time deficits, co-morbid sensory
loss, coordination deficits, as well as, fatigue status and
fall history. Providing balance assistance not only allows
patients to continue their training safely, it also lets
them experience the boundaries of their abilities without
actually falling. Such a process of experiencing
trial-anderror in (re)learning motor tasks is commonly referred
to as error-based training, a concept often applied in
stroke rehabilitation . The applicability of this concept
was confirmed in observations during training sessions by
the authors and by personal communication with
therapists, who state that an optimal tradeoff between safety of
the patient and physical manual balance assistance by the
therapist is critical. When therapists provide assistance
too soon, patients might not learn from their mistakes as
imbalance is already corrected by the therapist before it is
noticed by the patient. On the contrary, when therapists
provide assistance too late, more corrective force is
needed to stabilize the patient, thereby creating a possibly
dangerous situation in which a therapist is not able to
prevent the patient from falling. It has been shown that such
(sensory) feedback is important in the learning process of
], especially when the patient is able to link
this information to his/her body movements .
Even though gait training was found to be effective
for stroke survivors in terms of regaining functional
], the one-on-one contact with the
patient and the constant need for supervision makes
this type of therapy time consuming, labor intensive
and expensive in terms of healthcare costs. The
burden on health care is expected to increase even
further in the near future, limiting rehabilitation time
and potentially its effectiveness for patients. Patients
benefit from, among other aspects, a training
environment where sufficient training hours at a suitable
training intensity can be made . A solution that
might positively contribute to this aspect is the use of
(robotic) devices that support balance [
which patients can undertake additional training
hours in a self-administered training environment.
The intuitive and effective training method of
therapists, and their complex integration of knowledge of
the patient’s abilities might be of great importance in
the acceptance and effectiveness of such a device.
Therefore, a first step in designing such a particular
training device is to investigate this complex behavior
and the possibilities to convert this information into a
robotic device. Previous work by Galvez et al. was
performed with a similar goal in mind . The study
intended to quantify and analyze the interaction
forces between the physical therapist and the patient’s
hips and legs, while performing gait training that
required continuous support by more than one
therapist. However, no quantitative data is currently
available that describes how a single therapist
provides intermittent manual balance assistance to
patients that are able to walk for short distances, nor is
data available that describes when therapists
intuitively decide that patients need support.
Therapeutic balance-assisting forces could be provided
at many locations on the body. Since no data is available
on this, observations during training sessions by the
authors and by personal communication with therapists
have identified that the iliac crest is a preferred point of
contact. Possibly as this allows accurate control of the
center of mass (COM), an important parameter when it
comes to balance. Additionally, a large number of
existing fall detection systems [
] reflect that a relation
exists between acceleration signals measured at the
COM and the likelihood for falls. These fall detection
systems measure accelerations of the COM and process
this data by the use of specific algorithms in order to
determine if and when the subject fell. The algorithms
were validated by comparison of the detected falls from
the recorded data with the actual falls of the subjects
during the measurements. The studies all showed the
ability to distinguish true falling events from other
activities based on acceleration data, and a sensitivity >80%
and specificity of 100% was shown by Mathie et al. [
COM accelerations therefore seem to be highly related
to falls. Other fall detection systems exist that use
acceleration data of different body parts (i.e. trunk, thigh,
head), but Kangas et al. [
] found that measurements
from the waist and head were more useful for fall
detection compared to the other body parts.
Predicting the intuitive and complex behavior of
therapists in providing balance assistance logically
depends on many more aspects than is the case in the
predication of falls. Yet, given the relation between
COM acceleration and falls as described above and
the pelvis as the preferred place of therapists to
provide balance assistance, we assume that a change in
the acceleration of the patient’s COM plays an
important role in the decision of therapists to provide
balance assistance. Although it might not capture all
the moments in time at which balance assistance
takes place, it is a first step in the development of
the previously mentioned robotic training device. This
paper therefore aims to develop an algorithm that
predicts the timing of therapeutic balance assistance
during overground walking, based on the
accelerations of the patient’s sacrum. Note that, in contrast to
existing fall detection algorithms, the developed
algorithm aims at detecting events of therapeutic balance
assistance rather than to detect actual falls of the
subjects. By comparing the predicted events of
physical balance assistance with the actual therapeutic
events, a measure is provided that validates the
predictive abilities of the algorithm. Moreover, in order
to provide insight into how therapists support
patients during gait training, this paper will be the first
to provide a step towards quantifying therapeutic
balance-assisting forces. It will quantify balance
assistance in terms of the anatomical planes in which force
is provided, the duration of the force and the amount
of force necessary to regain or retain the balance of
the patient during overground walking. Aside from
the general insight that this information gives into
the behavior of therapist during gait training sessions,
both the algorithm as well as the quantitative data
can serve as a first step in setting up technical
requirements for robotic balance gait training devices.
Eight male stroke survivors (age = 58 ± 6 years, height =
1.82 ± 0.06 m, weight = 84 ± 7.2 kg) were recruited for
the overground walking task. Subjects were both
recruited from Roessingh Rehabilitation Hospital in
Enschede, The Netherlands, and physiotherapy practice
PMI Rembrandt in Veenendaal, The Netherlands.
Subjects were included if they met the following inclusion
criteria: (1) Stroke survivor (either (sub-)acute or
chronic); (2) Able to walk for a short distance (10 m)
without a walking aid, but with physical supervision of a
therapist; (3) Able to understand and execute
instructions of the walking tasks.
This study was approved by the local Medical Ethical
Institutional Review Board and methods conformed to
the Declaration of Helsinki. All subjects gave written
informed consent prior to participation.
Subjects were instrumented with two Force/Torque
sensors and one inertial measurement unit (see Fig. 1).
To capture the balance-assisting force characteristics of
therapists, the two wired 6 Degree of Freedom Force/
Torque sensors (Mini45 F/T sensor, ATI Automation
Industrial, Apex, NC, USA) were fixated on the left and
right side of a belt that was worn around the pelvis of the
patient (Fig. 1). Specifically, the sensors were positioned at
the location of the iliac crest. Prior to the start of
measurement trials, interviews were conducted with the patient’s
physical therapist and entire physical therapy treatment
sessions were observed during which gait training sessions
occurred. These sessions have revealed that the iliac crest
is the preferred location to provide balance assistance to
the patient. The belt with the sensors were oriented such
that walking direction of the patient equaled the x axis of
the sensor for each subject. The force sensors were
connected to a power supply that was connected to a Data
Acquisition (DAQ) card (National Instruments, Austin,
TX, USA) in order to acquire the signals in Simulink
(MathWorks, Natick, MA, USA).
A wireless inertial magnetic measurement unit (IMU)
(MTw sensor, Xsens Technologies B.V., Enschede, the
Netherlands), consisting of an accelerometer, gyroscope
and magnetometer, was positioned at the sacrum to
capture sacral motion throughout the measurement. The
accelerometer signal (60 Hz) was specifically measured
at the sacrum as this location closely resembles COM
]. The IMU was mounted with adhesive
skin tape to the lower back of the subject during
overground walking training. Data recording was performed
through MT Manager software (Xsens Technolgies B.V.,
Enschede, the Netherlands).
To synchronize both measurement systems, a switch
was manually pressed on one of the force sensors
(leading to a peak in this data-signal), at the same time
triggering the IMU software to start recording. Force data
was cut afterwards in MATLAB (MathWorks, Natick,
MA, USA), based on this synchronization peak.
Measurements took place during a regular gait training
session, and lasted no longer than 30 min for each
patient. Patients were asked to arrive 15 min prior to the
start of the measurement in order to mount the sensors
correctly onto the body. Subjects walked at a
selfselected walking speed and a physical therapist walked
behind the patient to supervise and physically support
the patient when necessary. Therapists were instructed
to correct the balance of the patients at the location of
the force sensors (at the iliac crest). Therapists did not
have their hands on the patients at times other than
when corrective forces were being applied.
Similar to conventional gait training sessions, subjects
were asked to slalom (zigzag movement) around cones
and walk in a straight line during the measurements, in
order to represent training of ADL tasks. Patients that
only needed few therapeutic balance-assisting force
corrections were given an additional cognitive task (e.g.
count down in increments of three) in order to provoke
more postural instabilities during the measurements.
Tasks were repeated in a random order until the end of
the training time was reached, or until the patient was
tired. Training took place on a level ground walkway of
approximately 10 m with a chair on both sides so that
patients could rest in between the measurements (see
Fig. 2). As the force sensors were connected with wires,
a trolley was used to guide the wires and transport the
laptop alongside the patient.
When therapists provided corrective forces at a
location other than the force sensors (e.g. at the shoulder or
the trunk of the patient), no force characteristics could
be captured by the force sensors. However, the moment
in time of these balance-assisting events was still
captured, as a timestamp was manually created in Simulink
by one of the researchers that logged the time and the
location of these events.
In order to obtain patient characteristics, Berg Balance
Scale (BBS), Functional Ambulation Category (FAC), ten
meter walking test (10MWT), Motricity Index (MI) and
Dynamic Gait Index (DGI) were performed within one
week after the measurement by the treating physical
therapist of the patient.
Balance-assisting force characteristics based on the force
Force data was filtered over all three axes with a 2nd
order Butterworth, low-pass filter with a cutoff
frequency of 5 Hz. Baseline offset was removed during a
static condition prior to the start of each measurement
and the resultant force between the sensor that was
mounted on the left and right side of the body was
calculated over all axes separately. For all measurement
trials, the start and stop time of each individual
Fig. 2 Schematic representation of the measurement set-up. Legend:
Chairs were positioned 10 m from each other. In between the chairs
were five cones, such that patients had to walk around them. Therapist
walked behind the subject and only provided assistance when
the patients was unstable
therapeutic balance-assisting event was manually
selected. All measurements were checked and
synchronized in correspondence with video recordings that
were taken during the measurements. Subsequently,
the maximal force and the duration of the therapeutic
corrective forces were calculated for each event
individually. Maximal force was presented as the absolute
value, irrespective of the sign (positive or negative) of
the correction applied.
Predicting the timing of therapeutic balance assistance
based on acceleration data of the sacrum
Accelerometer data of the sacral IMU was processed in
MATLAB, for each subject and measurement trial
individually. The resultant acceleration signal of the X, Y
and Z axes of the sensor was used for analysis, making
the data independent of sensor orientation on the
sacrum. The mean resultant value was subtracted from
the data, such that gravitational acceleration was
removed and free acceleration data remained. The periods
in time where a patient was not moving before the start
and after the stop of each measurement were cut off,
leaving only the actual movement data to be analyzed.
The algorithm that was built is based on the
assumption that deviations in the sacral signal (for instance:
relatively long periods in time where high accelerations
in the signal are visible) represent the moments in time
were therapeutic balance assistance takes place. Outlier
detection was used to identify these time points.
Interquartile ranges (IQR) and individual quartiles (e.g. Q1
Q4) were calculated on the remaining dataset and were
used to define the threshold for outlier detection of each
subject and measurement trial individually [
Additionally, a moving average window (‘δ’, seconds) was
applied to calculate the average value of the data within a
specified window width. A visual representation of the
method used is presented in Fig. 3. A threshold was set
at Q3 + α*IQR according to Tukey’s method [
‘α’ was a factor that determined the height of the
threshold. Each time an averaged value was above the defined
threshold the data was marked as an outlier. The factor
and the width of the moving average window were
varied in order to retrospectively determine the most
optimal set of parameters that lead to the best algorithm for
outlier detection: ‘α’ was varied between the constants
0.5 and 3.0 in steps of 0.1 and ‘δ’ was varied between 0.5
and 3 s in steps of 0.01 s. All possible combinations
between both parameters were used.
The time points that were identified as actual events
of therapeutic balance assistance (captured by both the
force sensors as well as other locations on the body)
were loaded into the acceleration dataset and compared
with the outliers that were detected by the algorithm.
Each outlier was classified as a True Positive (TP; both
therapist and algorithm classify point in time as an event
of balance assistance), False Negative (FN; therapist
provides assistance, but algorithm does not find outlier) or
False Positive (FP; therapist does not provide assistance,
but algorithm finds outlier). Additionally, the Positive
Predictive Value (PPV = TP/(TP + FP)*100) and True
Positive Rate (TPR = TP/(TP + FN)*100 were calculated
for each patient individually and on a group level,
indicating the ability of the algorithm to identify the events
of balance assistance correctly.
Optimal settings were defined as the best
combination of ‘α’ and ‘δ’, leading to the highest score of
PPV and TPR at the same time. Optimal settings
were determined by using the data of four, randomly
selected subjects. The data of the remaining four
subjects were used to validate these settings, and to test
whether these settings still resulted in high PPV and
TPR values for other subjects.
Eight measurement sets were performed with the
included patients. None of the subjects fell during the
measurements. A total of seven different therapists
provided physical balance assistance during the training
sessions. Patient characteristics as well as scores on clinical
measures are presented in Table 1.
Balance-assisting force characteristics based on the force sensor data
Characteristics of the provided therapeutic correction
forces are presented in Table 2. Median number of
balance-assisting events during the total measurement
set of each patient was 3 times (0.6) (median (IQR)).
Median distance walked during this time span was 35 m
(21) (11.5 m (18.3) walked per event of balance
assistance, median (IQR)).
Corrective forces were mainly provided over the
medio-lateral (ML) axis (frontal plane): Roughly 80% of
the total force that was used is provided in this plane,
representing 2% of the average body weight of the
subjects. Around 10% (±0.2% of the patient’s body weight)
of the total force was provided over both the
anteriorposterior (AP) and superior-inferior (SI) axes.
Seventyeight percent of the total amount of events of balance
assistance was located at the side of the pelvis (either on
one or on both force sensors on the iliac crest). Other
locations that were used, were the trunk and shoulder of
the patient. The median duration of therapeutic
assistance was typically 1.1 s (0.6) and the median impulse
per event over the ML-axis was typically 9.4Ns (8.2).
Typical examples of therapeutic balance-assisting force
profiles are shown in Fig. 4. Here, two hands have been
used for the application of corrective force. The left
hand exerts the largest amount of force on the body.
The right hand of the therapist exerts a smaller amount
of force on the body. It is used to provide stability to the
pelvis of the subject and it helps the therapist in
controlling the application and release of the corrective forces.
Predicting the timing of therapeutic balance assistance based on acceleration data of the sacrum
Based on four randomly selected subjects (patient ID = 1,
4, 5 and 7), there were several combinations of window
width ‘δ’ and factor ‘α’ that lead to similar PPV and TPR
score. In all cases, ‘δ’ was 2.5 s and ‘α’ could be chosen as a
constant value between 0.8 and 1.3, all leading to the same
PPV and TPR values. With these algorithm settings, a
PPV of 73% and a TPR of 80% was obtained on a group
level. Individual PPV and TPR scores ranged between
67% and 100%.
The algorithm was validated by applying these settings
(with ‘α’ chosen as a constant of 1.0) to the remaining
ML = medio-lateral, SI = superior-interior, AP = anterior-posterior. Missing data is indicated by an ‘-‘: therapists did not provide assistance at the location of the
force sensors in these cases. Characteristics of these events could therefore not be calculated
Resultant force profile between
left and right hand
four subjects (patient ID = 2, 3, 6, 8). PPV and TPR
values increased in this case to 87% and 81%
respectively, on a group level. PPV and TPR scores of the
individual subjects remained between 67% and 100%.
Both the scores on an individual level as well as on a
group level are presented in Table 3 and Table 4, for
Two typical examples of an acceleration signal over
time are shown in which all balance-assisting events
were correctly classified (Fig. 5, left) or in which one of
two events was not detected (Fig. 5, right). Note that the
events that were not detected, the therapeutic assistance
force was applied at the shoulder.
This paper is the first to present insights into the
behavior of therapists regarding manual physical balance
assistance to stroke survivors during gait training in terms
Scores are presented as the summed total of all measurements trials within
of force characteristics and prediction of the timing. We
aimed to provide a quantitative dataset on the
balanceassisting force characteristics and provide insight in the
timing of these events, as such information is of
importance in the technical development of (robotic) devices
that support balance. As therapist integrate knowledge
on many aspects of the patient, the results of this paper
should be interpreted as a first step towards the analysis
of this complex behavior. Regarding the timing of the
balance-assisting events, we developed an algorithm that
is able to predict therapeutic events, solely based on the
acceleration signal of the patient’s sacrum during a gait
training session. Prediction scores remain high, even
when different therapists and patients were analyzed
with the same algorithm settings. Additionally, the
results that were shown in the quantitative dataset imply
that the therapists in this study in general use similar
balance-assisting strategies while physically supporting
the patients. For instance, correction forces were
relatively low in all cases, corrective forces were mainly
provided over the ML axis (80%) and the spread in duration
was rather small. The quantitative results in this paper
might best be interpreted as guidelines, rather than
being fixed numbers, especially as this dataset is based on
only eight subjects.
The complex clinical decision-making process of
therapist in determining whether or not to apply a
balanceassisting force, might explain PPV and TPR scores that
are lower than 100%, indicating that the algorithm was
not able to identify all therapeutic events correctly.
Therapists integrate information such as the patient’s
muscle strength, reaction times, fatigue state and fall
history in order to determine whether or not to provide
balance assistance. Although the developed algorithm in
our study already shows high prediction scores, a more
elaborated or altered measurement set-up and algorithm
might be needed to better approach the complex clinical
decision-making process of therapists. Studies that focus
on the prediction of falls, such as in the study by Li et
] or Sim et al. [
] report PPV rates of 80% to
90%, and show that by adding more accelerometers or
additional gyroscope sensors to the body (e.g. head or
waist) the performance of the algorithm could be further
increased. It was shown by, among others, Li et al. [
and Bourke et al. [
] that performance of fall detection
systems can be even further increased when adding
gyroscope data to the algorithm, supplementary to
acceleration data. This might specifically be beneficial when a
large variety of ADL tasks need to be classified during
gait training [
] and an adequate distinction
between the body movements is needed.
Although therapists aim to treat patients with the best
possible care, there could have been situations in which
the therapist was too careful and therapeutic balance
assistance was provided at times when this was not
actually needed. These situations could have led to FN
scores at the side of the algorithm as no deviations
would then be visible in the measured signal. This
example indicates the complexity in interpreting FN and
FPscores. For (robotic) devices, both FN as well as FP
values should be as low as possible: missing a critical
balance-assisting event at the side of the patient could
lead to an actual fall when no safety harness is present,
whereas the system might not measure up to the
principles in error-based training when assistance is provided
too soon, thereby letting the patient not experience the
boundaries of his abilities. By changing the algorithm
settings (‘α’ and ‘δ’) that are presented in this study, one
can adapt the error-based training component: allow
more or less movement freedom, depending on the
abilities of the patient. It should be emphasized that the
present research and the developed algorithm serves as
a first step towards this process. No (robotic) devices
that focus on balance assistance are known that use a
control mechanism that tries to mimic the complex
behavior of therapist, whereas this might be of great
importance in the acceptance and effectiveness of such a
device. As a first step in this development, the developed
algorithm and the used measurement set-up were kept
as simple and intuitive as possible.
Even though the algorithm proves to have a good
ability to predict therapeutic balance-assisting events
correctly, it does not directly validate that sacral
acceleration is wholly responsible for the therapist
balanceassisting responses. In fact, a different contact location
was reported in 22% of the corrections, even though
therapists were specifically instructed to aim for the
force sensors on the iliac crest. Beside the argument that
this could be just common practice of therapists, the
typical example of Fig. 5 demonstrates that a different
trigger (arm sway of the patient in order to restore
balance) other than sacral accelerations, causes the
therapist to apply corrective forces to the body. Although with
the use of the present measurement setup it was
attempted to capture a conventional gait training session
as realistically as possible, restrictions in the used
measurement set-up have limited the recording of the events
that were not located on the force sensors.
Consequently, these events were not taken into account in
calculating median values. Yet, the used measurement
set-up did not affect the number of balance-assisting
events in any way, nor did it affect the timing of the
events. Even though the use of force sensors logically
deviates from a conventional clinical setting, none of the
therapists indicated that they were forced to balance the
patient at a location at which it was inconvenient for
them. It was therefore expected that regarding the
specific location of providing balance assistance, the
measurement set-up did not significantly affect the behavior
of the therapists. Logically, other methods could have
been chosen to capture the corrective forces by
therapists. For instance the use of force gloves that would
allow the capturing of all therapeutic forces, regardless
of the contact location on the body. However, most
known force gloves [
] are still in the development
stage and/or have a limited amount of sensors on the
glove, thereby introducing the risk that not all contact
force between therapist and patient would be measured
by the glove. Additionally, using these gloves would
introduce the risk of having only one measurement from
a specific body location, thereby restricting the ability to
generalize the results. As the main goal of this
manuscript was to provide first insights into the behavior
of therapists, we have chosen to focus specifically on
one location on the body at this stage of the research.
Additionally, thickness of the force sensor prevents
any contact between the therapist’s hand and the
patient’s body: thereby ensuring that all force is
captured by the force sensor.
Furthermore, in 38% of all balance-assisting events
located at the iliac crest, the hands of the therapist hit only
one sensor, even though two hands were used for the
balance-assisting event in these cases. This affected the
calculation of the resultant force, as this was calculated
as the difference between both hands. It caused an
undershoot of the resultant force in some cases and
overshoot in others. Yet, it is believed that the
presented median corrective force is still a good
representation of the average force profile that is used
during a gait training session.
Although this study was conducted with care, a couple
of limitations can be identified. First of all, it should be
noted that a certain inaccuracy exists in the time
synchronization between both measurement systems.
Both measurement systems were synchronized by
pressing a switch (triggering the IMU software to start
recording) against one of the force sensors (leading to a
peak value in this data signal). Force data was cut
afterwards in MATLAB, based on this synchronization peak.
Even though it was intended by the researchers to
shorten the duration of the peak value as much as
possible, a small inaccuracy remained in the time
synchronization between both systems. Secondly, a delay
might exist between the instability of the subject and the
timing of the balance-assisting event of the therapist, i.e.
between a peak in the recorded acceleration data and a
peak in the recorded force data. Both peaks do not occur
exactly at the same time, mainly due to the reaction time
of the therapist. Yet, it was assumed that the total time
inaccuracy between both systems was no more than
±0.5 s: When the actual balance-assisting event and the
detected outlier were a maximum of ±0.5 s apart, the
outlier was classified as a TP.
Furthermore, the algorithm is based on data of four
patients and four therapists, and is validated with data of
the other four patients and three therapists. Positive
Predictive Values and True Positive Rates remain high in
the validation process, even though measurements of
different subjects and patients were then included. This
positively suggests that the algorithm is valid for a
random group of therapists and/or patients, rather than
being restricted to the measured group of subjects.
However, it must be kept in mind that the optimal
parameter settings (‘α’ and ‘δ’) that determined PPV and
TPR scores, were in both cases obtained by using a
group of (four) subjects with rather homogeneous
patient characteristics. These setting might not be the most
optimal settings when focusing on individual subjects, or
even a single measurement trial. This is confirmed by
the finding that for individual patients, PPV and TPR
scores vary between 67% and 100%. Therefore, the
present research only functions as a first step in the
process of capturing the complex behavior of therapists.
The algorithm that was developed and the results of the
balance-assisting force characteristics provide a good
first impression of the requirements that can be set to
(robotic) devices that support balance. Future work
should first of all increase the number of subjects to
further specify these requirements. A larger and more
heterogeneous group of FAC 3 patients should be included
for these measurements and the patients should perform
additional tasks during the measurements: step over
objects, 180 degree turns, variable step length, etcetera.
Furthermore, increasing the number of subjects,
therapists and measurements would allow to specify
intertherapist variability. Although the quantitative results in
this study imply that therapists in general use similar
balance-assisting strategies, specific details on this
matter might be of interest while developing robotic training
devices, for instance in relation to specific walking tasks
]. Although it must be kept in mind that force
characteristics might differ for each individual subject,
training session and even for each intervention, increasing
the number of subjects might enable the classification of
balance-assisting events, thereby provide more insight
into the aspect of inter-therapeutic variability.
In order to capture additional aspects of the complex
behavior of therapists, it might be of interest to focus on
verbal and sensory cues of therapists, to obtain
characteristics of other body locations (such as shoulder or
trunk), and to specify the directions of the balance
assistance in relation to the gait cycle. For instance, it has
now been observed from video analysis, in
correspondence with the recorded data, that patients mainly fall
sideways or to the back, and the therapist consequently
pushes the patient upwards, to the front and over the
medio-lateral axis. It might be of interest to study the
ML balance assistance in relation to the gait cycle at the
moment of intervening, and in relation to the affected
side of the subject.
The goal of this study was to present a quantitative
dataset on therapeutic balance assistance and predict the
timing of these events. A threshold-based algorithm was
developed that was able to predict timing of therapeutic
balance assistance events with a Positive Predictive Value
of 87% and a True Positive Rate of 81%.
Quantitative data showed that patients on average
received a balance-assisting corrective force every 11.5 m
and that instability mainly occurred in the frontal plane.
They received force corrections by the therapists of
around 2% of their total body weight, with a median
duration of 1.1 s and a median impulse of 9.4 Ns. This
study represents the first step in the analysis of complex
behavior of physical therapists during gait training and
this information can serve as input for the development
of (robotic) devices that support balance.
10MWT: 10 meter walking test; AP: Anterior-Posterior; BBS: Berg balance
scale; COM: Center of mass; DGI: Dynamic Gait Index; FAC: Functional
ambulation category; FN: False negative; FP: False positive; IMU: Inertial
measurement unit; IQR: Interquartile range; MI: Motricity index; ML:
Mediolateral; PPV: Positive predictive value; Q1-Q4: Quartile 1 – Quartile 4;
SI: Superior-inferior; TP: True positive; TPR: True positive rate
We would like to thank T. de Vries for her help during the execution of the
experiments. Additionally, we would like to thank the physical therapists
involved from Roessingh Rehabilitation Hospital, Enschede, The Netherlands,
and physiotherapy practice PMI Rembrandt, Veenendaal, The Netherlands for
their help in running the experiments.
This study was supported by a research grant from ZonMw (grant number:
Availability of data and materials
The datasets used and/or analysed during the current study are available
from the corresponding author on reasonable request.
JH acquired the data, analyzed and interpreted the data and was the primary
author in writing the paper. EM was involved in the design of the study, in the
analysis and interpretation of the results and in writing the paper. HvdK, JB, JR
and JSR were involved in the design of the study, in the interpretation of the
results and in writing the paper. All authors read and approved the final
Ethics approval and consent to participate
This study was approved by the local Medical Ethical Institutional Review
Board (Medisch Etische ToetsingsCommissie Twente (METC), Enschede, the
Netherlands) and methods conformed to the Declaration of Helsinki. All
subjects gave written informed consent prior to participation.
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published
maps and institutional affiliations.
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