Short Stick Exercise Tracking System for Elderly
Rehabilitation using IMU Sensor
Kazuki Oi1 , Yugo Nakamura2,4 , Yuki Matsuda1,4 , Manato Fujimoto3 , and Keiichi Yasumoto1
1

Nara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan
Email: {oi.kazuki.of8, yukimat, yasumoto}@is.naist.jp
2
Kyushu University, Fukuoka, Fukuoka 819-0395, Japan
Email: y-nakamura@ait.kyushu-u.ac.jp
3
Osaka City University, Osaka, Osaka 558-8585, Japan
E-mail: manato@osaka-cu.ac.jp
4
JST PRESTO, Tokyo 102-0076, Japan

Abstract—Stick exercises, which have been attracting attention
for improving the health of the elderly, are usually performed
in nursing homes under the guidance of nursing staff. However,in the current pandemic in which the elderly are advised torefrain from going out unnecessarily, it is desirable for eachindividual to be able to perform the stick exercises alone. In
this study, we aim to develop a stick exercise support systemthat can automatically record the number of times an elderly
person performs each type of stick exercise and provide feedback
to improve the movement for each exercise. As a first step
toward the realization of this stick exercise support system,
we investigated a method for recognizing exercise movements
using inertial measurement unit (IMU) sensors. In the evaluation
experiment, 21 subjects performed 3 sets (10 times per set) ofeight basic stick exercises. The exercise movements were classified
based on the linear acceleration and quaternion data obtained
from the IMU. As a result, 90 % of F-measure was achieved
when using LightGBM as the learning algorithm.
Index Terms—Activity recognition; Stick exercises; Healthpromotion; Machine Learning

I. I NTRODUCTION
Falls in the elderly often result in significant trauma, suchas bone fractures, that require hospitalization and are notuncommonly followed by the victim becoming bedridden.
Therefore, it is important to prevent such falls through daily
moderate exercise. In recent years, stick exercises, which areeasy to perform, have been attracting attention as a means toprevent falls and improve the health of the elderly [1]. Stick
exercises are usually performed in nursing homes under theguidance of nursing staff. However, in the current pandemic
situation in which it is advisable to refrain from going out
unnecessarily, it is desirable for each individual to be able toperform the stick exercises by himself or herself. A system that
can automatically record the number of times he or she has
performed each stick exercise at home is therefore required.
Shen et al. developed MiLift, which can track workouts
such as aerobic exercises and weightlifting with high accuracy
using a smartwatch [2]. Takada et al. investigated the recognition accuracy of 10 different exercises for each wearable
sensor position with a focus on body weight training without
equipment [3]. 93.5% recognition accuracy was achieved

when the sensors were placed at both the wrist and waist.
In both of these studies, inertial measurement unit (IMU)
sensors were attached to the body to realize highly accurate
motion recognition, but the application of this method to stick
exercises, which forms the focus of this study, has not yet been
investigated.
In this study, we investigate an approach for identifying
the stick exercises performed by a user in which an IMU
sensor is attached to the stick used for the stick exercises.
To evaluate the proposed method, we constructed a dataset
consisting of sensor data collected from 21 experimental
subjects performing three sets (10 times per set) of eight basic
stick exercises (boutaisou). In the evaluation experiment, weevaluated the performance of the proposed method using theabove dataset and confirmed that the proposed method canidentify the stick exercise with approximately 90% accuracy
in the leave-one-person-out scenario.
The remainder of this paper is organized as follows. In Sect.
2, we review the existing research related to the proposedsystem. In Sect. 3, we describe the proposed method for
tracking stick exercises using an IMU sensor. In Sect. 4, we
describe our evaluation experiments and the results. Finally,in Sect. 5, we conclude this paper and discuss future work.
II. RELATED RESEARCH
In this section, we describe the existing work on the related
fields of health support for the elderly and exercise support.
A. Research on health support for the elderly
Dobre et al. developed a system that enhances the deliveryof professional health care to the elderly through the provisionof non-intrusive monitoring and support [4].
Richard et al. proposed a health management system that
displays the temperature and heart rate data on a LCD andsends automatic notifications to caregivers and doctors [5].
Susnea et al. proposed a method to monitor the behavior ofelderly people living alone and detect deviations from past
behavior patterns [6]. However, these studies did not propose
systems to encourage exercise in the elderly.

B. Research on exercise support
Voicu et al. proposed a human physical activity recognition
system based on data collected from smartphone sensors [7].
Relevant features from the six activities of walking, running,sitting, standing, climbing, and descending are extracted by thesystem. An evaluation of the collected data shows that mostof the activities could be recognized correctly, and an average
accuracy of 93% was achieved in four of them. The challenge
is to expand the scope of activities recognized to include otheractivities such as riding a bicycle.
Kurban et al. proposed a daily activity recognition system
based on a 3-axis accelerometer that can be used in various
body positions [8]. In this study, data were collected fromthe subjects as they performed walking, sitting, standing,jumping, and falling motions. The proposed method achieved
an accuracy of up to 100% with an average accuracy of
96.54%.
Shen et al. developed MiLift, which can track workouts
such as aerobic exercise and weightlifting with high accuracy
using a smartwatch [2]. This system achieved more than
90% accuracy and repeatability in tracking both aerobic andweightlifting exercises.
Takada et al. investigated the recognition accuracy for 10
types of body weight training exercises without any equipment
for each position of a wearable sensor [3].
Turmo et al. developed a system that supports the understanding, execution, and modification of a variety of exercises
for a wide range of subjects [9]. The system uses BodyLights,
which are 3D printed wearable optomechanical devices thatcan be placed at critical body parts and inside equipment.
These devices project laser crosses based on the wearer’s
movements to help correct 18 targeted exercises.
Torigoe et al. focused on kendo, a representative martial arts
in Japan, and proposed a method for detecting and recognizing
striking motions through the use of IMUs to realize a support
system for improving the user’s kendo technique. To confirm
the effectiveness of the proposed method, they collected inertial sensor data for the striking motions of subjects, who
included both experienced and inexperienced kendoka, using
four IMUs attached to the right wrist, waist, shinai tsuba,and shinai tip leather and detected five striking motions basedon the dynamic time warping (DTW) distance obtained fromthe acceleration time series data. The hitting motion could be
detected with 89.9% of F-measure [10]. The systems proposedin these studies can be used to recognize the activities andmotor movements of elderly people. However, a system that
evaluates and improves the stick exercise movements in theelderly by providing feedback has not yet been developed.
Therefore, we decided to develop such a system.
III. STICK EXERCISE TRACKING SYSTEM
A. System overview
The goal of this study is to realize a stick exercise support
system that can provide feedback to elderly users during exercises to improve their exercise movements using IMU sensors.

step1

step2
What kind of exercises?

step3
♪

+
IMU sensor

Sensor data

Stick exercise

Motion recognition in stick exercises

IMU sensor attached to stickto detect exercise movements.

Automatic classification of exercise
type based on sensor data(acceleration and quaternion)

Intervention with the subject
Evaluate exercise exercise andprovide appropriate feedback

Fig. 1. System overview

Figure1 shows an overview of the proposed system. An IMU
sensor is attached to a stick to detect the exercise movement.
The system automatically classifies the type of exercise fromthe linear acceleration and quaternion sensor data, evaluates
the exercise movements, and provides appropriate feedback. In
this study, we aim to recognize the exercise events performedby the user as the first step toward the realization of the stick
exercise support system.
B. Exercises for recognition
Eight basic exercises were chosen as recognition targets
from the stick exercises introduced in [1] which are shownin Figure 2 and explained below.
Exercise A: Stick straddling exercise
The exerciser holds the stick with both hands andstraddles the stick without bending it. Next, theexerciser lifts his/ her hips off the chair and raises the
stick to the back of the waist. Finally, the exerciser
returns to his/her original position in reverse order.
The effect of this exercise is to increase the flexibilityof the legs and maintain the range of motion.
Exercise B: Stick lifting exercise
The exerciser holds the stick with both hands anddoes the Banzai (holding up two hands) with a backstraight. He/she breathes and raises his/her shoulder
during the exercise. This exercise stretches the backand helps prevent falling to the side.
Exercise C: Body twisting exercise
The exerciser stretches back and rotates the body to
the left and right while holding the stick with both
hands. Rotating the body helps improve the mobilityof the spinal column and thorax. Body rotation is
also a necessary element of getting back to one’s
feet after losing balance.
Exercise D: Sideways body tilting exercise
The exerciser holds the stick with both hands,
stretches back, and bends the body to the left andright. This exercise helps increase the flexibility of
the rib cage.
Exercise E: Falling forward exercise
The exerciser holds the stick with both hands, leansforward, and places the bar on the floor. By doing thisexercise, the exerciser experiences weight loading on
the sole of the foot and the forward leaning posturenecessary for standing up.

a. Stick straddling exercise

b. Stick lifting exercise

c. Body twisting exercise

d. Sideways body tilting exercise

e. Falling forward exercise

f. Shoulder twisting exercise

g. Receiving the stick behindthe back exercise

h. Stick turning with hands exercise

Fig. 2. 8 types of stick exercises

Fig. 4. Smart stick used in the experiment.

C. Sensor devices used and mounting position
Fig. 3. IMU sensor used in the experiment

Exercise F: Shoulder twisting exercise
The exerciser holds a stick with both hands andtwist shoulders as if he/she were turning the stickin front of body. Twisting the shoulders can increase
the mobility of the shoulders.
Exercise G: Receiving the stick behind the back exercise
The exerciser passes the stick behind his/her backand receives it with the opposite hand. Manipulating the stick in an unseen location enhances body
movement imagery and increases shoulder mobility.
Exercise H: Stick turning with hands exercise
The exerciser holds the stick with both hands androtates it by moving the wrists up and down alternately. This exercise can increase the mobility andflexibility of the wrists.

In this study, we used the MetaMotionR1 shown in Figure 3
as the IMU sensor device to classify the exercise movements.
The IMU sensor can perform linear acceleration and quaternion measurements and has a recording rate of up to 100 Hz.
It can also collect the linear acceleration and quaternion data
wirelessly. The sensor is embedded in a hole in the centerof the stick, as shown in Figure 4. The exerciser performs theexercise by holding both ends of the stick. Subjects performed
the exercises by holding both ends of a stick. The way andangle of holding the sticks were adjusted so that all the subjects
had the same angle.
IV. EXPERIMENT
A. Experiment summary
To evaluate the effectiveness of the proposed method, we
conducted a data collection experiment with 21 male andfemale subjects in their 20s. The linear acceleration and quaternion measurement data from the IMU sensor were collected
as the subjects performed three sets of each of the eight target
1 https://mbientlab.com/metamotionr/

exercises (10 times per set) using the smart stick shown in
Figure 4.
B. Sensor waveforms
The measurement results for a single run are shown in
Figure 5. The figure shows the synthetic acceleration, whichis the sum of the accelerations in the x, y, and z axes
during each exercise, and the quaternion measurements. The
horizontal axis shows the time, and the vertical axis shows
the magnitude of the synthetic acceleration and quaternion
measurements. From the results of this run, it can be seenthat the synthetic acceleration of the straddling exercise (firstcolumn from the left, first row from the top in Figure 5) does
not change significantly from the beginning to the end of theexercise because the straddling exercise does not involve any
fast movements of the stick. The quaternion (second column
from the left, first row from the top in Figure 5) also showsno significant changes in the w, x, y, and z components
possibly because there are no large movements of the stick.
The synthetic acceleration for the stick lifting exercise (thirdcolumn from the left, first row from the top in Figure 5) has
two periods of large acceleration, which we assume correspondto the raising and lowering of the arms. The magnitudes of
the w and y components of the quaternion (fourth column
from the left, first row from the top in Figure 5) decrease
while the stick is being lifted. The synthetic acceleration
during the body twisting exercise (first column from the left,second row from the top in Figure 5) shows a large overallchange. We believe that the large change is due to the large
and fast gymnastic movements involved in raising the stickto chest height, twisting the body to the left and right, andreturning to the original position. The z component of thequaternion (second column from the left, second row fromthe top in Figure 5) is sometimes large and sometimes small
because of the twisting to the left and right. The synthetic
acceleration during the sideways body tilting exercise (thirdcolumn from the left, second row from the top in Figure5) does not change significantly in general. This is because
although large movements are involved in this exercise, it isdifficult to move the body quickly. In the quaternion (fourthcolumn from the left, second row from the top in Figure 5),
there are times at which the x component decreases when thez component increases and times at which the x component
increases when the z component decreases. This is thought tobe due to the larger changes in the z component when the
body is tilted to the left or right. The synthetic acceleration
during the falling forward exercise (first column from the left,
third row from the top of Figure 5) is generally small. Thisis because this exerciser is only required to bring the stickfrom knee height to foot height in a sitting position, so thestick does not move very quickly. The y component in thequaternion (second column from the left, third row from the
top in Figure 5) increases during the exercise after the arm islowered and returns to its original value when the arm returns
to its original position. The synthetic acceleration during theshoulder-twisting exercise (third column from the left, third

row from the top in Figure 5) shows a large change fromthe beginning to the end of the exercise. This is because
the exercise involves rapid movements in bringing the stickfrom knee height to chest height at the beginning, twisting to
the left and right, and then returning the stick to the original
knee height. In the quaternion (fourth column from the left,
third row from the top in Figure 5), there are times at which
the x component decreases when the z component increases
and times at which the x component increases when the z
component decreases. This is thought to be due to the factthat the change in the z component becomes larger when
the shoulder is twisted to the left or right. The synthetic
acceleration in the receiving the stick behind the back exercise( first column from the left, fourth row from the top in
Figure 5) shows little overall change. This may be due tothe fact that because the exerciser cannot see the stick, theexercise becomes difficult if the movements are too fast. The
y component of the quaternion (second column from the left,
fourth row from the top in Figure 5) rises at the beginning of
the exercise and returns to its original value at the completionof the exercise because the stick is lowered with the left hand,received with the right hand, and then returned to its original
position. The synthetic acceleration of the stick turning withhands exercise (third row from the left, fourth column fromthe top in Figure 5) has two sections with larger magnitudes
because the stick is rotated in two separate movements. The y
component in the quaternion (fourth column from the left in
Figure 5, fourth row from the top) is larger at some times andsmaller at others because it increases when the stick is firstrolled in on the left hand and decreases when it is rolled inon the right hand.
C. Feature extraction
The feature values shown in Table 1 were calculated fromthe linear acceleration and quaternion data acquired at asampling rate of 100 Hz from the IMU to build the machinelearning model. These features are namely, the mean, standard deviation, median absolute deviation, maximum value,
minimum value, sum of squares, entropy, quartile range,
fourth-order Burg autoregression coefficient, range between
minimum and maximum values, and the root mean square of
the time-domain signal, and the skewness, kurtosis, maximum
component, weighted average, spectral energy, frequency band
power spectrum spectral energy, and power spectral density of
the frequency-domain signals. These features were selected
because they have been shown to be effective in previous
studies on context estimation based mainly on inertial data[10]–[12].
D. Performance evaluation
The performance of nine typical machine learning algorithms, namely, the SVM artificial neural network (ANN),
random forest (RF), decision tree (DT), LightGBM logistic
regression (LR), k-nearest neighbor (KNN), naive Bayes (NB),
and extra-trees (ET), was evaluated using leave-one-person-out
cross-validation. Figure 6 shows a bar graph of the F-measure

1

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Fig. 5. Sensor waveform for each exercise
TABLE IF EATURE L IST FOR S HORT S TICK E XERCISE R ECOGNITION
Function

Description

means

Arithmetic mean

stds
mads
maxs
mins
energysentropys
iqrs
autorregresions
rangesStandard deviation
Median absolute deviation
Largest values in array
Smallest value in array
Average sum of the squares
Signal entropy
Interquartile range
4th order Burg autoregression coefficients
Range between smallest and Largest values

rmss

Root square means

skewnesss
kurtosissmaxFreqInds
meanFreqs
energyBandsab
psds

Frequency signal skewness
Frequency signal kurtosis
Largest frequency component
Frequency signal weighted average
Spectral energy of a frequency band [a, b]Power spectral density

Formula1 PN
s̄ = N
si
q i=1
1 PN
2
σ=
i=1 (si − s̄)Nmediani (| si − medianj (sj ) |)maxi (si )mini (si )1 PN
2
i=1 si
N
P
PN
N
(c
log(c
i )), ci = si /i=1 i
j=1 sjQ3(s) − Q1(s)a = arburg(s, 4), a ∈ R4max
q i (si ) − mixi (si )1(s21 + s22 + · · · + s2N )N
s−s̄ 3E[( σ ) ]E[(s − s̄)4 ]/E[(s − s̄)2 ]2argmaxi (si )PNPN
i=1 (isi )/j=1 sjPb1
2
i=a sia−b+1
PN 2
1
i=1 si
F req

Type
TF
TF
TF
TF
TF
TF
TF
TF
T
T
T
F
F
F
F
F
F

NSignal vector lengthQQuartileTTime domain features, FFrequency domain features.

achieved by the machine learning algorithms for each subject.
Using LightGBM resulted in the highest F-measure of 90.0%.
In comparison, Using NB resulted in the lowest F-measure of
52.4%. LightGBM achieved the highest F-measure because it
combines the decision tree algorithm with gradient boosting.
We can therefore conclude that LightGBM is an effective
machine learning algorithm for the proposed method. Figure
7 shows the confusion matrix obtained from the evaluationof LightGBM, which achieved the highest F-measure. As
can be seen from the figure, the F-measure is, in general,high. The F-measure of the stick straddling, stick lifting, body
twisting, sideways body tilting, and receiving the stick behindthe back exercises are about 90%, while those of the falling
forward and stick turning with hands exercises are about 80%,
which is a little lower than those of the other exercises. It
can therefore be concluded that the stick exercises performedby the subject were recognized accurately most of the time.
However, there were a few cases in which the exercise was
not identified correctly, which may have resulted from theindividual differences between the exercise movements of therecognize the eight stick exercises with an F-value that exceeds
90%. In the future, we will improve the proposed method byincreasing the number of subjects. In addition, we plan todesign feedback mechanisms to motivate habitual exercises
and improve their exercise skills based on the IoT data-driven
nudging concept [13].

1
0.9
0.8
0.7

F value

0.6
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0.4

ACKNOWLEDGMENT

0.3

This research was supported by JST PRESTO (JPMJPR21P7) and Grant-in-Aid for Scientific Research (B)(No.20H04177).

0.2
0.1
0
LightGBM

SVM

LR

ANN

RF

KNN

DT

ET

NB

R EFERENCES
Fig. 6. F-value results for each machine learning algorithm

Fig. 7. Performance evaluation of LightGBM

21 subjects asked to perform the stick exercises. We would
like to improve the proposed method in the future so that theexercise movements can be recognized more accurately.
V. CONCLUSION
In this paper, we investigated a method for recognizing exercise movements in the elderly and focused on stick exercises,
which have attracted attention for fall prevention and healthpromotion in the elderly. To demonstrate the effectiveness ofthe proposed method, we asked 21 subjects to perform threesets of eight stick exercises (10 times per set). We classified theexercise movements using linear acceleration and quaternion
data measured using an IMU sensor attached to the centerof the stick. The target exercise movements are eight basic
stick exercises comprising the stick straddling, stick lifting,body twisting, sideways body tilting, falling forward, shoulder
twisting, receiving the stick behind the back, and stick turningwith hands exercises. The measurement data obtained from thesubjects were compared with laboratory data. The F-measureof nine typical machine learning algorithms in classifying
the exercises based on the measurement data obtained fromthe subjects was evaluated. We found that LightGBM can[1] Katsushi Yokoi, Kouichi Yoshimasu, Shigeki Takemura, Jin Fukumoto,
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