Measuring Upper-Extremity Use with One IMU

Hang Wang

1,2

, Mohamed Irfan Mohamed Refai

and Bert-Jan F. van Beijnum

University of Science and Technology of China, P. R. China

Biomedical Signals and Systems, University of Twente, Enschede, The Netherlands

Keywords:

Arm Use, IMUs, Upper Limb Movement, Movement Quality.

Abstract:

Discharge home from hospital can be a critical stage in the rehabilitation of patients with central neurological

disorders such as stroke. The new skills and early recovery achieved in the hospital may be difﬁcult to transfer

to the home environment. This work addresses the monitoring of arm usage and proposed a new metric called

Weighted Activity Counts (WAC) based on a sensing system that consists of only one inertial measurement unit

(IMU). The proposed metric combines activity counts and the smoothness of the movement. This work deﬁnes

Normalized Gross Energy Expenditure (NGEC) as the reference metric. WAC shows good performance under

the validation protocol we designed (correlation coefﬁcient r > 0.90). The optimal placement for the single

sensor which can sufﬁciently and reliably describe arm usage is also explored in this work.

1 INTRODUCTION

The main goal during stroke and central neurologi-

cal disorder rehabilitation is to achieve optimal motor

performance enabling patients to live independently.

Researchers use standardized clinical tests and func-

tional motion tasks to assess the capacity of stroke pa-

tients, for example, Fugl-Meyer Assessment (Sanford

et al., 1993) and the Action Research arm test (ARAT)

(Lyle, 1980). In the home environment, it’s hard for

the physicians to access necessary information about

the intensity and quality of a patient’s daily-life activ-

ities (Klaassen et al., 2016). Therefore, it is of interest

to build an unobtrusive and modular system for objec-

tively monitoring the patient’s upper or lower extrem-

ity motor function in daily-life activities. Since the

upper extremity function is a key Activities of Daily

Living factor and seen as a high research priority in

rehabilitation (Klaassen et al., 2016), the main focus

of this study is on upper extremity movement.

The use of an IMU is a potential method for the

minimal assessment of body movements in a daily

life setting (Van Meulen et al., 2015), (Xu et al.,

2016), (van Meulen et al., 2017). IMUs combine

accelerometers, gyroscopes, magnetometers and also

do not require an external physical reference system

to estimate movement which makes the use of IMUs

suitable for measurements in a daily life setting (van

Meulen et al., 2017).

Several IMUs based metrics have been used to de-

scribe upper extremity movements. In the arm us-

age coach (AUC) system (Klaassen, 2015), (Klaassen

et al., 2016), researchers put forward the difference

acceleration vector (DAV) which calculates the 3D

norm value of the vector difference between the

movement acceleration and the gravity vector in a

predeﬁned resting position. Another commonly used

metric is the integral of the absolute value of accel-

eration (IAA/IMA). This method takes the integral of

the absolute values of the acceleration measured by

the accelerometer (Bouten et al., 1994). Another most

widely used method has been put forward by Hale et

al., who use the mean acceleration (in m/s

) for each

of the three axes across set 1-second or 1-minute in-

tervals called as activity counts (AC) to measure the

amount of the arm usage (Hale et al., 2008). How-

ever, Leuenberger et al. (Leuenberger et al., 2017)

suggested that AC provides quantitative rather than

qualitative information. This holds for the other met-

rics as well.

Smoothness is a characteristic of coordinated hu-

man movements. According to Rohrer et al., patients’

movements seem to become smoother with recov-

ery (Rohrer et al., 2002). During rehabilitation, mo-

tion quality especially the smoothness can be different

which consequently requires the changes of treatment

programs. Thus, smoothness is an important indicator

of the quality of the movement. This work proposes

a new metric called Weighted Activity Counts (WAC)

that fuses the smoothness of upper extremity move-

Wang, H., Refai, M. and van Beijnum, B.

Measuring Upper-Extremity Use with One IMU.

DOI: 10.5220/0007253400930100

In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019), pages 93-100

ISBN: 978-989-758-353-7

Figure 1: Protocol Phase One. (a) is the top-down view of

the different position on the table with a participant seated

on a stool. (b) is the overview of the different heights above

the table with the subject standing in front of the table.

ments with the conventional AC. To investigate the

new metric, an experiment with healthy subjects has

been conducted.

This work deﬁnes a reference metric Normalized

Gross Energy Consumption(NGEC) based on the law

of conservation of mechanical energy: the mechanical

energy is deﬁned by the sum of the potential and ki-

netic energy. NGEC is also based on one sensor, and

able to evaluate the gross arm usage. In this study,

we validate the proposed metric as a measure of arm

usage and compare it with other four state-of-the-art

metrics.

The remainder of the paper is organized as fol-

lows: Section 2 describes the experiment protocol

that was used to evaluate the metrics, the rationale

of the WAC metric and the reference metric we pro-

posed, and Section 3 provides the results of compar-

ison among different metrics, Section 4 presents the

discussion based on the experiments, and ﬁnally, con-

clusions are given in Section 5.

2 MATERIALS AND METHODS

This section comprises of the measurement conﬁgu-

ration, two-phase experimental protocol and the pro-

cessing procedure (including pre-processing, the cal-

culation of the metrics and thereference metric).

Figure 2: Protocol Phase Two. (a) is the coordinate frame

of all the experiments, (b) is the top view of horizontal task,

the subject was in stable sitting position, and (c) is the top

view of front & back task(z-axis), the subject was in stable

sitting position. The black, yellow, green line in the ﬁgures

represent the normal, light tremble and heavy tremble mo-

tion traces separately. Finally, (d) is the side view of verti-

cal task, the subject was in stable standing position to avoid

bending their upper body.

2.1 Measurement Conﬁgurations

The device we used in our experiment is the Xsens

MVN suit (Roetenberg et al., 2009). IMUs are placed

over the entire body on different body segments. First,

the body length, shoulder width, arm span and foot

size of the subject are measured. Sensors are placed

on the hands, wrists, upper arms, forearms, shoulders,

sternum, chest, pelvis and head. Each sensor consists

of a 3D accelerometer, gyroscope and magnetome-

ter. Those data are collected and bundled with the

use of MVN Studio at a frequency of 60 Hz. The

Xsens Awinda protocol ensures real-time sending and

receiving of data and handles data packet loss.

2.2 Experimental Protocol

A total of 12 healthy subjects (24 ± 4 years old) vol-

unteered to participate in the study. The data was col-

lected in two phases as part of two separate studies.

The ﬁrst seven subjects were given the protocol Phase

One and the other ﬁve were asked to perform Phase

Two. The proposed protocol in our research has been

approved by the ethical committee in University of

Twente. All subjects ﬁlled in an informed consent

before doing the experiments. The participants are

asked to perform the following movements as seen in

ﬁgure 1 (a):

BIOSIGNALS 2019 - 12th International Conference on Bio-inspired Systems and Signal Processing

Figure 3: Complex task. Subjects were asked to move the

object from A to B, C, D separately with three different mo-

tion types (normal, light tremble, heavy tremble).

• A−B−A−C−A−D−A;

• B−C−D−B−D−C−B;

• pick up the object from ground and place it at A

and then pick up object from A and move it to ear,

ﬁnally put it back to A.

Then based on ﬁgure 1 (b), the motion sequences can

be grouped into the following three sections:

• Lift the object to height B’ and lower it back to

height A’– Move object from height A’ to height

B’ (place it at height B’) – Move object from

height B’ to height A’;

• at height A’, move object A−B−A−C−A;

• Move object from A to D at height B’− Move ob-

ject back to A− Use the dust cloth to clean the

table by going forth and back once.

The second phase of our protocol is as shown in

ﬁgure 2. This part consists of a horizontal task, ver-

tical task, front& back task and complex task. In

the ﬁrst three tasks, subjects were asked to move a

small ball from point A to point B along different

routes (black, orange, green line in ﬁgure 2) in order

to mimic different smoothness degree of movements

(motion types). In the fourth task, subjects were asked

to move the ball along the diagonal of all three planes,

as seen in ﬁgure 3 and also with three motion types

(normal, light tremble and heavy tremble). The routes

in the fourth task were selected by the subjects. All

tasks were done three times. Before each task, there

was a short break before starting.

2.3 Weighted Activity Counts

Decomposition of the complicated motion makes it

possible to use euler angles to estimate the position

during the arm movement. Based on IMUs system,

acceleration and angular rate from sensors were used

to estimate the forearm orientation relative to the earth

referential frame. For this purpose, the gradient de-

scent orientation ﬁlter proposed by Madgwick et al.

(Madgwick et al., 2011) was selected. The algorithm

fuses sensor measurements of angular rate and grav-

ity into an optimal orientation estimate. It also as-

sures convergence from initial conditions and com-

pensates for drift in a vertical plane. In this algo-

rithm, the weighting of the accelerometer measure-

ments in the error correction β according to the def-

inition in (Madgwick et al., 2011). We set β to 0.03

as proposed by Madgwick (Madgwick et al., 2011).

After that, the algorithm calculates the orientation

value by numerically integrating orientation change

rate. Then, the estimated orientation change rate is

computed as the rate of change of orientation mea-

sured by the gyroscope, and the magnitude of the gy-

roscope measurement error β, which is removed in

the direction based on accelerometer and magnetome-

ter measurement (Madgwick et al., 2011). The ﬁl-

ter outputs orientation in a quaternion representation

q = [q

]. The euler angles φ, θ , and ψ can

then be computed from these quarternions. Variance

reﬂects the average distance from each point to the

average value in the whole motion procedure. In that

case, we use all the three angles’ variances to describe

the smoothness of the movement. Here,

N = f

· E poch (1)

where f

is sampling frequency in Hz; Epoch is

duration of each movement in second, and the prepa-

ration time of the movement should not be counted in

Epoch. Then, we deﬁne the Smoothness Degree (SD)

of the data points in the observation period as:

SD =

variance(φ) + variance(θ) + variance(ψ)

(2)

According to equation 2, when the movement

shows large variance in φ, θ , and ψ then, the SD

value will also be large, which shows that the move-

ment contains certain degrees of tremble on one or

more directions. The estimated SD is combined with

the weight of conventional Activity Counts (AC). AC

for epochs are calculated by equation 3 adapted from

(Janz, 1994):

AC =

∑

n=1

x,n

+ a

y,n

+ a

z,n

(3)

Here a

i,n

is the acceleration at time ‘n’ for the i-th

axis, and ‘N’ is the total number of samples. Using

the above, the Weighted Activity Counts(WAC) is de-

ﬁned as:

WAC = SD · AC (4)

Measuring Upper-Extremity Use with One IMU

In this equation, WAC combines the movement’s

quality SD and the intensity AC together to capture

the smoothness degree of the movement.

2.4 Difference Acceleration Vector

Difference Acceleration Vector (DAV) is used to de-

tect movement of the arm by using 3D accelerome-

ters. The length of the DAV is calculated by subtract-

ing a reference gravitational acceleration vector g(n)

from the current acceleration vector a(n) and taking

the norm of the resulting vector. DAV is deﬁned as:

∑

n=1

x,n

− g

)

+ (a

y,n

− g

)

+ (a

z,n

− g

)

(5)

DAV takes the difference of the acceleration vec-

tor compared to a reference position which already

reduces the inﬂuences of gravitational acceleration

and possibly noise. In that case, no ﬁlter is applied

to the acceleration data when calculating the DAV

(Klaassen et al., 2016).

2.5 Integral of Absolute Value of

Acceleration

The Integral of Absolute value of Acceleration (IAA)

was ﬁrstly described by Bouten et al (Bouten et al.,

1994). Another known abbreviation of this method is

IMA, the integral of the modulus of the acceleration.

This method takes the integral of the absolute values

of the acceleration measured by accelerometer, as the

formula:

IAA =

dt +

dt (6)

The IAA metric is estimated by ﬁltering the ac-

celeration with a fourth order Butterworth zero phase

low-pass ﬁlter with a cut-off frequency of 20Hz to

attenuate the effect of frequencies that don’t arise

from voluntary movement as proposed by Bouten et

al (Bouten et al., 1994).

2.6 Root Mean Square

Schasfoort et al., describes the usage of the upper-

limb activity monitor (ULAM) (Schasfoort et al.,

2002), combined with the calculation of the root mean

square (RMS) (Bussmann et al., 2001). The RMS of

the signal was calculated after the band-pass ﬁlter-

ing (FIR, 0.3−16 Hz). Bussmann et al. (Bussmann

et al., 2001), proposed this metric to measure upper-

limb use from accelerometer data of the upper limb

and intensity.

2.7 Reference Metric (NGEC)

Estimate of energy of the movement using IMUs was

proposed by Aleshinsky et al. (Aleshinsky, 1986) and

Zaman et al. (Zaman et al., 2014). In order to sim-

plify the system and make it possible to be used in

obtrusive monitoring, we proposed Normalized Gross

Energy Consumption (NGEC) as the reference met-

ric. NGEC is based on the work done by Aleshinsky

et al. (Aleshinsky, 1986) which calculates the kinetic

energy consumption by using:

kin

· m ·

∑

n=1



− v



(7)

Where the absolute value means the energy con-

sumption should always be positive during the move-

ment and v

and v

represent the ﬁnal and start ve-

locity respectively. In order to evaluate the gross en-

ergy consumption, we derive E

tot

as E

kin

+ E

pot

, giv-

ing

tot

∑

n=1

(

· m ·



− v



+m · g · (h

− h

))

(8)

where h

and h

represents the ﬁnal and start

sensor position in global frame at adjacent points dur-

ing the movement.

In the calculation of both of these energies, the

mass is needed. This mass includes the mass of the

participant’s arm and the object that is being moved.

Since this mass is unknown and differs per subject,

it is also possible to calculate the speciﬁc energy in

J/kg, giving NGEC = E

tot

/m.

Before estimating the NGEC, the acceleration

data from the sensors was high-pass-ﬁltered at 0.3Hz

in order to reduce the inﬂuence of gravity. Please

noted that the rotational kinetic energy during the

movements is not counted. In the following section,

we compare the WAC with the other four metrics

(IAA, RMS, AC, DAV).

In this section, data was processed and analyzed

using MATLAB (MathWorks Inc., Natick, MA).

3 RESULTS

3.1 Metrics Comparison

Using equation 3 and 4, combined with the accel-

eration data, we can get the value of AC and ulti-

mately, WAC. Figure 4 presents the different WAC

values among different motion types in each task.

BIOSIGNALS 2019 - 12th International Conference on Bio-inspired Systems and Signal Processing

Figure 4: WAC value from hand worn sensor for different

motions: left panel showing sitting and right panel showing

the standing task.

Table 1: Comparing correlations among different metrics,

based on NGEC for the hand worn sensor in Phase One,

r > 0.7 is boldened.

Seq1 #1 #2 & #3 #4 #5 #6

WAC 0.82 0.90 0.90 0.90 0.95

AC 0.44 0.39 0.46 0.51 0.47

DAV 0.10 0.57 0.56 0.19 0.21

IAA 0.93 0.73 0.83 0.93 0.92

RMS 0.15 0.07 0.13 0.06 0.04

Two phases of the protocol are validated separately.

In Phase One, we compare the proposed metric WAC

with the other four state-of-art metrics, including AC,

DAV, IAA, RMS, as shown in Table 1. Based on the

difﬁculty of the motions, six sequences are made. Sit-

ting position: (#1) short distance, simple; (#2) a lit-

tle bit difﬁcult, long distance and (#3) up and down,

more difﬁcult; Standing position: (#4) up and down;

(#5) horizontal motion, short distance and (#6) forth

and back motion, short distance.

For the Phase Two of the protocol, the correlation

value when doing different tasks are compared in Ta-

ble 2 with high correlation coefﬁcient value (r > 0.7)

marked in bold. The performance of the other four

conventional metrics is presented in Table 2. Finally,

we compare the correlation value (WAC and NGEC)

among different motion types (normal, light tremble,

heavy tremble) in Table 3. And Table 4 shows the SD

value of the three measurements. The trend between

WAC and motion type is seen in Figure 5.

Figure 5: WAC value from hand worn sensor for different

motions: left panel showing simple and right panel showing

the complex tasks.

3.2 Optimal Sensor Placement

To explore the optimal position of the sensor, the same

processing procedure was done by using the data from

forearm and upper arm respectively. The results are

provided in Table 5.

4 DISCUSSIONS

4.1 Summary of Assessment Results

This paper focuses on the assessment of arm usage in

remote rehabilitation system by using wearable sen-

sors. Since the multi-sensor systems maybe unsuit-

able for daily life (Burke et al., 2009), WAC is devel-

oped based on a single sensor. This metric combines

the activity count metric for upper extremities with a

smoothness metric. Based on the results of our exper-

imental protocol, the following three main discussion

points are focused in this paper:

• The performance of the proposed metric WAC un-

der different tasks.

• The comparison among state-of-the-art metrics.

• The optimal placement for the sensors to assess

the movements.

A two-phase protocol are designed to investigate

the assessment capability of WAC. In the ﬁrst phase

of the protocol, subjects were asked to move an object

along the ﬁxed path to mimic the simple movements

of patients. While in the second phase of the protocol,

Measuring Upper-Extremity Use with One IMU

Table 2: Comparing correlations among different metrics, using NGEC as reference for the hand worn sensor, for Phase Two.

r > 0.7 is boldened.

– Horizontal Vertical Front & Back XOY YOZ XOZ

WAC 0.72 0.87 0.82 0.99 0.96 0.93

AC 0.01 0.25 0.38 0.05 0.10 0.14

DAV 0.53 0.68 0.13 0.26 0.16 0.18

IAA 0.84 0.87 0.98 0.94 0.94 0.92

RMS 0.01 0.50 0.02 0.18 0.21 0.06

Table 3: Comparing correlation among tasks and motion

types. r > 0.9 is boldened.

– Normal Light Heavy Total

Horizontal 0.97 0.73 0.33 0.72

Vertical 0.88 0.45 0.53 0.87

Front & Back 0.98 0.86 0.61 0.82

XOY 0.96 0.92 0.76 0.99

YOZ 0.93 0.96 0.96 0.96

XOZ 0.95 0.87 0.94 0.93

Table 4: Variances of Euler angles for the horizontal test.

Motion Type φ θ φ Average SD

Normal 4.10 6.36 0.05 3.50

Light 1.23 28.32 0.26 9.93

Heavy 8.65 35.47 0.65 14.92

a 3D task was assigned to mimic the movements of

stroke patients in their daily life.

4.2 Task Assessment

In Table 1 and 2, we compared WAC with the other

existing arm usage metrics under the two phases sep-

arately. The results of the ﬁrst experiment show that

WAC and IAA have the highest correlation with se-

lected reference NGEC for all movements (Table 1).

For the Phase Two, the comparison among different

metrics is shown in Table 2, in which the WAC is

compared with IAA, DAV and AC. Both the IAA and

WAC have better performance (correlation value is

higher). Moreover, when taking a close look at the

value, we can ﬁnd IAA is better when assessing sim-

ple movements. For example, in Table 2, during the

XOY, YOZ or XOZ task, WAC shows higher correla-

tion than IAA, whereas during the Horizontal, Verti-

cal or Front & back tasks, IAA is better. Meantime,

the RMS metric show the lowest correlation for all

experiments. For DAV, the highest value (0.68) ap-

pears when doing Vertical tasks in phase two and the

poorest value appears when doing sequence 1 (#1)

tasks. Based on the principle of WAC, the smooth-

ness (or variance from it) of the motion adds weights

on AC and improves the inﬂuence of motion qual-

ity on the metric. Hence, from Table 2, conventional

AC and WAC show large differences in correlations

with NGEC. The average correlation value of AC is

0.16, which is almost six times less than WAC. Also,

in Table 1, when doing simple tasks, the difference

between the two is down 1.96 times. Overall, WAC

shows advantages when assessing arm usage in the

complicated tasks.

The same conclusion can be summarized from Ta-

ble 3. We divided the Phase Two tasks into differ-

ent groups (Horizontal, Vertical, Front & back, XOY,

YOZ, XOZ). The result shows that when doing more

complicated tasks (XOY, XOZ, YOZ), the correla-

tion value between the metric and NGEC is higher

than the other tasks. Especially, the highest value

(0.99) appears at XOY tasks and the least value 0.71

is seen for Horizontal tasks. Also, here we considered

three types of motion (Normal, Light, Heavy trem-

ble). When doing normal tremble movements, all the

tasks except Vertical tasks are able to get great results

(correlation value more than 0.98). The lowest value

of 0.33 appears when the subjects do the heavy trem-

ble during Horizontal tasks to mimic the patients, in

which only the starting and ending points of the whole

motion trace are ﬁxed. While for the multi-type mo-

tion (the mixture of Normal, Light, Heavy tremble),

the result shows higher correlation (> 0.7).

Next, we focus on the metric WAC and our refer-

ence metric NGEC. Firstly, to see the relationship be-

tween WAC and motion types more clearly, we gen-

erate the line graph for both the two phases. In ﬁg-

ure 4 (Phase One), we group the six sequences by

the degree of difﬁculty of the motions. As the mo-

tion becomes more complicated, the WAC value in-

creases. Also, in ﬁgure 5 (Phase Two), the motion

type changes from normal to heavy tremble, the WAC

value increases. In both simple tasks (Horizontal, Ver-

tical, Front & back) and complex tasks (XOY, XOZ,

YOZ) cases, WAC is available to show the difference

when the motion type is changing.

The difference between NGEC and WAC should

also be noted. WAC combines the typical feature of

AC and motion smoothness which addresses the ques-

tion of arm quality during the rehabilitation. The al-

BIOSIGNALS 2019 - 12th International Conference on Bio-inspired Systems and Signal Processing

Table 5: Comparing correlations between sensor placement and task, for all ﬁve subjects, and all kinds of motion types.

r > 0.9 is boldened.

Placement Horizontal Vertical Front & Back XOY YOZ XOZ

Hand 0.72 0.87 0.82 0.99 0.96 0.93

Fore Arm 0.90 0.96 0.99 0.98 0.86 0.91

Upper Arm 0.83 0.78 0.99 0.98 0.79 0.99

gorithms used on the WAC do not require a full body

biomechanical constraint, such as that needed for the

NGEC. The NGEC requires transformation of accel-

erations measured in the sensor frame to body frame,

and estimation of positions, which is complex and re-

quires more than one IMU. Therefore, NGEC, though

easy to interpret, is more computationally intensive

than WAC.

4.3 Optimal Sensor Placement

In Table 5 we compared the inﬂuence of sensor lo-

cation on the correlation with the reference metric.

Hand-worn sensor has better correlations when doing

the complex task, while the forearm-worn sensor is

better when doing the simple task (r > 0.9). This sug-

gests that the hand worn sensor is a preferable option

in daily life. However, from the perspective of user

friendliness, the forearm or wrist could be the pre-

ferred location.

4.4 Future Work

Following from the discussion above, recommenda-

tion for the future research can be done. No feasibility

study on stroke patients has been done in our work.

We collected the data from healthy subjects and as-

signed typical tasks to mimic the stroke patients. Fur-

ther validation with patients is required. Besides, both

the two phases experiments require the subject to do

the tasks without any ambulatory activities, either in

standing or sitting position. The hand-worn sensor

is sensitive to any kind of movements, e.g. the arm

swinging during the ambulatory. These movements

are inevitable for the patients during their daily life.

Further exploration on this topic should is also worth

to pay attention.

5 CONCLUSIONS

Daily-life monitoring for stroke patients is essential

for the rehabilitation therapy. Efﬁcient and conve-

nient remote rehabilitation system is necessary for

the patients in the home environment. One of the

challenges in the analysis of patients’ daily-life per-

formance, compared to the assessment through stan-

dardized clinical tests in the hospital environment, is

the development of metrics for quantifying the move-

ments at home. In this work, we proposed a met-

ric that combines both the motion’s smoothness and

the quantity together to describe arm usage. In or-

der to validate the metric, we put forward normalized

gross energy consumption to evaluate the physical en-

ergy during the movement. WAC uses a single sensor

setup and is desirable as the convenience and usabil-

ity it provides to the stroke patients. The results of

both the simple 2D task and complex 3D task show

good performance (>0.9) when compared to NGEC.

WAC value also has relationship with motion types,

which provides possibility for detailed monitoring of

patients’ daily rehabilitation.

ACKNOWLEDGEMENTS

This work is part of the Perspectief programme Neu-

roCIMT with project number 14905 which is (partly)

ﬁnanced by the Netherlands Organisation for Sci-

entiﬁc Research (NWO). The authors would like to

thank Dr. Hans Bussmann (Erasmus MC University

Medical Centre Rotterdam, Department of Rehabili-

tation Medicine) for his advice on this work and Lian

Beenhakker (University of Twente) who collected the

data that was used in Phase One.

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