Sensors and Materials, Vol. 24, No. 6 (2012) 359–373
MYU Tokyo
S & M 0891

Development of a Wearable Gait Monitoring System
for Evaluating Efficacy
of Walking Training in Rehabilitation
Kosuke Motoi1,*,**, Sayaka Taniguchi1,2, Mina Baek1, Morikuni Wakugawa2,
Takumi Sonoda2, Tadahiko Yuji1,2, Yuji Higashi2, Toshiro Fujimoto2,
Mitsuhiro Ogawa1, Shinobu Tanaka1 and Ken-ichi Yamakoshi1
College of Science and Engineering, Kanazawa University,
Kakuma-machi, Kanazawa, Ishikawa 920-1192, Japan
2Fujimoto Hayasuzu Hospital,
17-1 Hayasuzu-machi, Miyakonojo, Miyazaki 885-0055, Japan
1(Received October 4, 2011; accepted February 16, 2012)
Key words:

wearable, posture change, walking speed, accelerometer, gyrosensor, rehabilitation

To evaluate the efficacy of walking training in rehabilitation, physical therapists
need to assess motion characteristics. However, they often evaluate these activities
subjectively by direct observation. In this study, we have developed a wearable system
for monitoring posture changes and walking speed in patients with hemiplegia. The
system can be used to measure the angle changes relative to the sagittal plane of thetrunk, thigh, and shank, and walking speed using the angle changes of the lower limb andleg length. In 12 healthy subjects (from 21 to 75 years) and 10 patients with hemiplegia (from50 to 87 years), we evaluated the measurement accuracy of the system by simultaneousrecordings using charge-coupled device (CCD) cameras. Also, in four patients withhemiplegia, the gait motions were monitored during the rehabilitation to evaluate its
applicability to the quantitative determination of the efficacy of the walking training.
The results demonstrated that the system could be used to measure the angle changes ofthe body parts (r = 0.99) and walking speed (healthy subjects: r = 0.99, patients: r = 0.95)
with high accuracy. It is also clearly shown through practical monitoring that the system
can be a useful means of evaluating the motion characteristics during the rehabilitationof the patients, therefore, the efficacy of the rehabilitation can be quantitatively evaluatedusing the system.

Corresponding author: e-mail: motoi@cc.hirosaki-u.ac.jp
Present affiliation: Graduate School of Science and Technology, Hirosaki University

*

**

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1.

Sensors and Materials, Vol. 24, No. 6 (2012)

Introduction

The importance of activity monitoring is well recognized in the fields of rehabilitation,
gerontology, and healthcare. For example, in the field of gerontology, it is necessary forthe elderly to actively maintain their daily living to avoid being bedridden. Therefore,
the quantitative measurement of these activities is essential.(1)To evaluate the efficacy of a rehabilitation program, a physical therapist (PT) mustassess the walking speed and posture changes during walking, standing, standingup, sitting, and so on. However, the PT must usually assess subjectively by direct
observation. One method used is to make recordings using a motion capture system withcharge-coupled device (CCD) cameras and a force plate. However, the range over whichsuch recording is possible is usually limited and data analysis is complicated. Therefore,
this method is generally not practical in rehabilitation.
For quantitative assessment, some wearable systems for monitoring the activities
using an accelerometer, a gyrosensor, a pressure sensor, and so on, have been developed.(2–18)
For example, a wristwatch-type accelerometer was frequently used for the analysis ofactivity.(2,3) However, the information obtained using this system showed only whether
the subject was moving or resting. The systems discriminating whether the subject wasstanding, sitting, lying, walking, and so on, were also reported.(4–9) However, detailed
motion characteristics during dynamic posture such as walking could not be obtained bythese methods. Also, by using the methods in which the accelerometer was attached tothe subject’s trunk, only balance during standing or walking could be assessed.(10,11) A
device that assesses walking by measuring plantar pressure cannot continuously analyze
movement of the body segments.(12) These devices are available for the rough estimationof physical activity or gait pattern. However, more detailed information is usuallyrequired, particularly in the evaluation of the walking training during the rehabilitation.
Also, the methods of detecting angular changes in the lower limbs or walking speed
using gyrosensors were reported.(13–15) However, these systems could not be used toobtain the initial angle for integrating angular velocity from a gyrosensor and also forcompensating the data drift. To solve these problems, joint angle measurements of
the lower limb using an accelerometer together with a gyroscope were reported.(16–18)
However, they must require complex calculation and could not simultaneously measure
the angle changes of the trunk, thigh, shank, and knee joint, and the walking speed that
the PT needs to evaluate during walking training. Therefore, the applicability of thesemethods in the rehabilitation may not be suitable.
On the other hand, we reported the different concepts for monitoring activities.
The measurement principle was quite simple. If we can measure the angle of three
anatomical parts, such as the trunk, thigh, and shank, with respect to the gravitational
direction, we can discriminate almost all the human postures in the sagittal plane,
which are possible under normal daily life.(19) Thus, we developed a portable device
for measuring the trunk, thigh, and shank angles relative to the gravitational direction
to improve the quality of life of elderly persons. At the start of the development, weused miniature electromagnetic inclinometers to measure angles,(19) but had problems
with the limited measurement range and inertial artifacts of the weight built in theSensors and Materials, Vol. 24, No. 6 (2012)361

sensor. To solve these problems, we used accelerometers and gyrosensors for the angle
measurement and developed our system to measure not only the posture changes, butalso the walking speed using the thigh angle changes.(20,21)
Using our concept for the activity monitoring, the motion characteristics ofthe patients under the rehabilitation could be clearly determined through practicalmeasurement during rehabilitation. (22) However, in the previous system that wedeveloped, we used a compass model based on only angle changes in the thigh forwalking speed measurement; therefore, the measurement precision was insufficient toevaluate the gait of a stroke patient during the rehabilitation. The analytical method
and parameter for detecting the motion characteristics during walking training were notinvestigated. These drawbacks obstruct its practical use for the patient’s gait monitoringin the rehabilitation field.
From these viewpoints, the objective of this study is to develop a wearable gaitmonitoring system to evaluate the efficacy of walking training in rehabilitation by
improving the previous monitoring system mentioned above. To resolve these practicaldrawbacks mentioned earlier for the rehabilitation, we have newly designed a methodof monitoring walking speed and analyzing the motion characteristics of a stroke patient
during walking training. Then, by combining these new methods, we developed a
new wearable device for monitoring posture changes and walking speed for use in thewalking training during the rehabilitation. We have also investigated its applicability to
the rehabilitation of four patients with hemiplegia.

2.

Materials and Methods

Figure 1 shows the outline of the wearable gait monitoring system for use in theevaluation of motion during walking training in the rehabilitation room developed in thisstudy. The three sensor units (42×60×15 mm3, 40 g) are attached to the patient’s trunk,
thigh, and shank.
The accelerometer (H34C, Hitachi metals, 3 axes, ±2 G), gyrosensor (ENC-03R,
Murata, 1 axis, ±300 deg/s), amplifier, micro SD card, transmitter (ZigBee, 2.4 GHz),
battery (3.7 V, 500 mAh), and CPU (10 bit A/D converter, sampling frequency of 25 Hz)
are installed in the sensor unit. The sampling frequency of 25 Hz was determined on thebasis of the results of the practical monitoring of the patients in a previous study(22) andthus is suitable for the gait monitoring. With the sensor system and personal computer,the patient’s motion in the rehabilitation room can be monitored. The wireless systemenables continuous monitoring for 6 h. The data is transferred to the computer in realtime and also stored on an SD card as back-up data.
Firstly, the angles of the trunk, thigh, and shank relative to the gravitational direction
are calculated from the gravity variation component (DC~0.5 Hz) of the accelerometers.
By using these angle values, it discriminates among the postures such as walking,standing, sitting, and lying.(20,21) In the static posture such as standing, sitting andlying, the angles relative to the gravitational direction of body parts are obtained using
the signals of the accelerometers mentioned above. To calculate the angle changesof the body parts during dynamic posture i.e., walking, we provide angle changes in362

Sensors and Materials, Vol. 24, No. 6 (2012)

Fig. 1. Outline of wearable gait monitoring system for use in rehabilitation room (a). Thissystem can be used to measure posture changes together with walking speed. Picture of subject’s
sensor attachment is also shown in the lower part (b). Definitions of trunk, thigh, and shank angles
relative to the sagittal plane, and knee angle are also shown (c).

Sensors and Materials, Vol. 24, No. 6 (2012)363

high precision in which angular velocity outputs of the gyrosensors (0.1–10 Hz) areintegrated. The initial angle values for integration are obtained from the sensor signalsof the accelerometers in the static posture just before the walking.(20–22) By repeating thedetermination of the initial angle values for every walking, accurate angle changes canbe calculated without the effect of the sensor drift caused by the integration.
From these calculations, in this study, the angle changes of the trunk, thigh, andshank during walking, θtr, θth, θsh, respectively, as shown in Fig. 1(c), can be obtained
accurately, for analyzing the posture changes and walking speed for the evaluation of theefficacy of rehabilitation.
On the other hand, the sensor units are attached to the subject’s body using theshoulder-and knee-supporter-type sensor holders. The sensor holders (shoulder type:79 g, knee type: 48 g) can be adjusted on the basis of the user’s physical size and alsoenable close contact with the body. Also, when the patient is sitting on a chair at the startof measurement, the initial angle can be reset with θtr = 0 deg, θth = 90 deg, θsh = 0 deg,using software installed in a personal computer. Therefore, the system can accurately
determine angle changes of the body segment throughout monitoring .
In Fig. 2, the method of measuring the walking speed is shown. In the previous
system, the compass model using only angle changes of the thigh was used for thewalking speed measurement. To improve this method, by using the two-link gait model
based on the thigh and shank angle changes, the walking speed during the stance phaseof the leg attached with the sensor units was calculated. The calculation step is asfollows.
(1) The heel contact can be determined from the maximum value of the shank angle. In
this point, the thigh and shank angle changes, θth,c and θsh,c (deg), respectively, areobtained. The horizontal distance between the hip joint and heel, Dc (m) = Lth sin θth,c+ Lsh sin θsh,c, was calculated using the subject’s thigh and shank length, Lth and Lsh (m),respectively.
(2) The heel off can be determined from the minimum value of the thigh angle, forobtaining the thigh and shank angles, θth,o and θsh,o (deg), respectively. The horizontal
distance between the hip joint and heel, Do (m) = –Lth sin θth,o–Lsh sin θsh,o, iscalculated using the subject’s leg length, Lth and Lsh (m).
(3) By summing Dc and Do, the moving distance from heel contact to heel off, Dco (m), is
calculated.
(4) The period from the heel contact to heel off, Tco (s), is determined.
(5) The walking speed, Vco (m/s), is detected from Dco / Tco. The cadence, C (steps/min),
was also calculated from the number of cycles during walking. By repeating the
calculation of the values mentioned above for one walking cycle, the walking speed
can be obtained continuously. In addition to these calculations, the values mentioned
below are also obtained in order to evaluate the motion characteristics of the patient
during walking.
(6) Knee angle, θkn (deg) = θth–θsh, as shown in Fig. 1(c), and variation width, θkn,w (deg),
are determined.
(7) Trunk angles in the heel contact and heel off, θtr,c, θtr,o (deg), respectively, aredetermined.

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Heel contact

Heel offVco
Dco (m) = Dc + Do
θth,c

L th

= (L th sin θth,c) + (L sh sin θsh,c)

θth,o
L sh

θsh,c

Dc

(L th sin θth,o) (L sh sin θsh,o)θsh,oVco (m/s) = Dco / Tco

Do

Vco (m/s)

θsh (deg)θth (deg)

Heel contact
Heel off
40

θth,c

20
0

θth,o

-20
25

θsh,c

0

θsh,o

-25
-50
1
0.75
0.5
0.25
0

0

1
Tco

2

3

4

5

Time (s)

6

7

8

9

10

Fig. 2. Outline of analytical program for the patient’s gait to obtain angles in heel contact andheel off and walking speed using a two-link gait model.

(8) Standard deviations, S.D., of the angle changes, θtr,sd, θth,sd, θsh,sd, θkn,sd (deg), and thosein the heel contact and heel off, θtr,c,sd, θth,c,sd, θth,o,sd, θsh,c,sd, θsh,o,sd, accumulated every
walking cycle are determined.
In addition to these analyses, the posture changes of the patient can be visualized as a
stick picture, making the PT easily understand the posture of the patient.
Figure 3(a) shows an experimental setup for evaluating the accuracy of the system inmeasuring the angle change and walking speed. Using a CCD camera, the movementsof the markers attached to the subject’s shoulder joint, hip (trochanter major), knee, andheel were simultaneously recorded at 30 frames per second.

Sensors and Materials, Vol. 24, No. 6 (2012)365

Fig. 3. Experimental setup for evaluating measurement accuracy in the angle change and walking
speed (a). To investigate its applicability to rehabilitation, practical measurements during walking
training in the patients with hemiplegia were carried out in the rehabilitation room (b).

Firstly, in front of the video camera, 7 healthy subjects (from 21 to 75 years) and4 patients with hemiplegia (from 50 to 87 years) were asked to take several posturechanges, e.g., walking, standing-up, sitting-up, etc. The actual angles relative to the366

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gravitational direction were calculated using the position of the markers mentioned
above. The angles estimated using the system were compared with the actual angle
values to evaluate the accuracy of the angle change measurement.
On the other hand, 12 healthy subjects (from 21 to 75 years) and 10 patients (from50 to 87 years) were asked to walk at various speeds. The actual walking speeds were
calculated from the moving distance of the marker attached to the subject’s trochanter
major. The speeds estimated using the system were compared with the actual speeds toevaluate the accuracy of the walking speed measurement.
To investigate its applicability to the rehabilitation, we also carried out the practicalmonitoring of four patients with hemiplegia (from 50 to 69 years) during 10 m ofwalking training in the rehabilitation, as shown in Fig. 3(b). The signals from the sensors
were recorded continuously throughout the rehabilitation. The lower limb sensors wereattached on the paralyzed side, and the subjects used a cane (T-type or Q-type) during
standing-up and walking.
Before the measurement, we obtained permission from the Ethical Review Board of
the hospital and written informed consent was obtained from each of the patients.

3.

Results and Discussion

Figure 4 shows typical recordings of the posture changes and walking speed in (a)a 24-year-old healthy male subject and (b) a 63-year-old male patient with hemiplegia(rehabilitation term: 62 days). In this figure, the angle changes relative to the sagittal
plane of the trunk, thigh, and shank (θtr, θth, θsh, respectively) are shown. The walking
speeds from heel contact to heel off (Vco) are also plotted for every walking cycle. The
angle changes determined from the CCD camera recordings are also shown in the graphswith an interval of the 0.33 s. From these results, the angles estimated using the systemcoincided well with those determined from the CCD camera. Also, the system could be
used to measure the walking speed continuously. In both healthy subject and patient,cyclic angle changes and stable walking speeds are observed. However, in the patient’s monitoring, the angle changes and walking speeds were smaller than those obtainedin the healthy subject. Moreover, as shown in this figure, the system could measure thewalking speed, such as not only the fast values of the healthy subject, but also the slow
values of the patient with hemiplegia.
Figures 5(a)–5(c) show the scatter diagrams between the angles obtained from thesensor system and those from the CCD camera during posture changes in seven healthy
subjects (from 21 to 75 years) and four patients with hemiplegia (from 50 to 87 years).
From these results, fairly good linear relationships are observed in the trunk, thigh, andshank, demonstrating that the system could be used to measure the posture changes withhigh accuracy (r = 0.99).
Also, in Fig. 5(d), the accuracy of the walking speed measurement is shown in 12
healthy subjects (from 21 to 75 years) and 10 patients with hemiplegia (from 50 to87 years). The horizontal axis shows the walking speeds calculated from the moving
distance and periods using the CCD camera recordings. The vertical axis shows those
calculated with the sensor system using the two-link gait model. As shown in this figure,Sensors and Materials, Vol. 24, No. 6 (2012)

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Fig. 4. Results of the gait monitoring in (a) a 24-year-old healthy male subject and (b) 63-yearold male patient with hemiplegia (rehabilitation period: 62 days).

the values estimated using the sensor system coincided well with the actual values
obtained with the CCD camera and a good linear relationship was obtained within a wide
range of walking speeds. The results also demonstrated that the accuracy of this system (r= 0.99) is superior to that of the previous system (r = 0.96).(19–22) Moreover, a good linear
relationship was also obtained in the patients with hemiplegia (r = 0.95).
As mentioned above, the system could be used to measure the posture changes andwalking speed with high accuracy, and can be of practical use in the rehabilitation field.
In the angular measurement, the good accuracy was obtained in a wide range of angles
including walking motion. Thus, further investigations can be carried out, such as theanalysis of activities including standing-up, sitting, and lying, and also the detailed
motion characteristics of those postures.

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n = 815

40
0
-40
-80

-120
-120 -80 -40

040

80 120

Angle obtained from CCD camera (deg)(c)
Angle obtained from sensor system (deg)y = 0.99x - 0.147
r = 0.99

120
80

Shank angle; θshn = 815n = 815

80
40
0
-40
-80

-120
-120 -80 -40(d)y = 1.01x - 0.37
r = 0.99
040

80 120

Angle obtained from CCD camera (deg)Walking speed: Vco

1.6

0.8

0
-40

-120
-120 -80 -40

Thigh angle: θth

120

1.2

40

-80

(b)
Angle obtained from sensor system (deg)120

Trunk angle: θtr

Speed obtained from sensor
system (m/s)

Angle obtained from sensor system (deg)(a)

y = 1.01x - 0.12
r = 0.99
040

80 120

Angle obtained from CCD camera (deg)

0.4
0

0

0.4

0.8

1.2

1.6

Speed obtained from CCD camera (m/s)◆ Healthy subjects ( n =120)
y = 1.00x + 0.01, r = 0.99

◇ Patients with hemiplegia ( n = 80)
y = 1.02x - 0.06, r = 0.95

Fig. 5. Correlation between values obtained from sensor system and those obtained from CCD
cameras. Results in (a) trunk, (b) thigh, and (c) shank angles and (d) walking speed are shown.

Moreover, in the walking speed monitoring, good accuracy was obtained in a wide
range of speeds. However, a slight underestimation was observed in the patients. In
these cases, the patients were walking while swirling the leg, and thus, the angle changes
relative to the sagittal plane of the thigh and shank were small. Therefore, walking
speeds smaller than the actual values were detected. As future development, we will
adapt a three-axes accelerometer and gyrosensor to the system and improve the methodof calculating the walking speed.
Figure 6 shows practical recordings of the motion characteristics during walking
training in the rehabilitation room. In this figure, the angle changes of the body segments
and walking speed are shown. The right side of the angle changes shows averaged data

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Walking speed; Shank angle; Knee angle; Thigh angle; Trunk angle;
Vco (m/s)
θtr (deg)θsh (deg)θkn (deg)θth (deg)

(a)
20
10
0
-10
-20
40

θtr,sdθth,sd

20

20
10
0
-10
-20
40
0
-20
40
30
20
10
0
20

θkn,sdθsh,sdθkn,sdθsh,sd

-20

-40
0.6

0

0.4
0.2

100

Walking
cycle (%)

0

5

10

15

Time (s)

20

-40
0.6

0

0.4
0.2

0.29±0.03

0
mean
S.D.
(n = 10)

5

10

15

Time (s)

20

25
mean
S.D.
(n = 10)(After rehabilitation)

20
10
0
-10
-20
20
θth,sd
10
0
-10
-20
40
30
θkn,sd20
10
010
θsh,sd
0
-10
-20
-30
0
100
Walking 0.6

θtr,sdθtr,sdθth,sdθkn,sdθsh,sd0cycle (%) 0.4

0.4
0.2

0.33±0.03
0

5

10

15

Time (s)

20

25

0.2
0

100

Walking
cycle (%)

0.37±0.06

025(Before rehabilitation)

20
10
0
-10
-20
20
10
0
-10
-20
40
30
20
10
0
10
0
-10
-20
-30
0.6

0

θth,sd0-200θtr,sd

20

0

0

mean
S.D.
(n = 10)(Brace)

-20
40
30
20
10
0
20

(b)
Walking speed; Shank angle; Knee angle; Thigh angle; Trunk angle;
Vco (m/s)
θtr (deg)θsh (deg)θkn (deg)θth (deg)

mean
S.D.
(n = 10)(Non-brace)

100

Walking
cycle (%)

0.28±0.05
0

5

10

15

Time (s)20

25

Fig. 6. Recordings of motion characteristics during walking training in rehabilitation room. In
figure (a), motions in a male patient (69 years, rehabilitation: 880 days, hemiplegia: left side) withor without brace (MAFO) are shown. Also, figure (b) compares motion between before and after
rehabilitation program in a female patient (62 years, rehabilitation: 21 days, hemiplegia: right side)
attached with the brace.

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Sensors and Materials, Vol. 24, No. 6 (2012)(accumulated 10 walking cycles) of the angles, represented as one walking cycle. The
horizontal axis shows the walking cycle as a percentage from 0% (heel contact) to 100%
(next heel contact). The bold lines denote the average and the thin lines denote ±S.D.
The average and S.D. values of the walking speed are also shown in the graph.
In Fig. 6(a), the motions of the male patient (69 years, rehabilitation: 880 days,
hemiplegia: left side) with or without brace (MAFO) are shown. From these results, thesubject could walk using the brace with the speed of 0.37 m/s faster than those without
the brace (0.29 m/s). However, the decrease in the motion repeatability in the bodysegments was observed from the S.D. (maximum S.D. in θkn = ±4.22 deg, nonbrace; ±7.34
deg, brace), showing the need of more walking training. Therefore, using these data, thePT could assess the efficacy and drawbacks of the brace attached to the subjects during
the rehabilitation.
Also, Fig. 6(b) shows the comparison of the motion during walking between
before and after the rehabilitation (about 30 min) in the female patient (62 years,
rehabilitation: 21 days, hemiplegia: right side) with the brace. In the rehabilitation,
this patient had the stretch, massage, standing and walking training sessions, and soon. In the results, no fluctuations in the motion were seen before the rehabilitation,
showing the high repeatability in the S.D. (maximum S.D. in θkn = ±3.42 deg) of theangle changes. However, the range of the knee angle change was very small. After the
rehabilitation, the range of the knee angle change became larger than those obtained
before rehabilitation, while the repeatability of the angle changes decreases. These
results show that the subject could control the gait by flexing and extending the knee;
therefore, the effectiveness of the rehabilitation program could be observed. However, inthe thigh, knee, and shank angle changes, the unstable fluctuations were observed in the
periods of the extension phase of the hip and knee joints. Moreover, the decrease in therepeatability of the motion was also observed from the S.D. (maximum S.D. in θkn = ±7.21deg) of the angle changes.
From these results, using the present system, the PT can easily understand thepatient’s posture changes during walking using the angular changes of the trunk, thigh,and shank. It is demonstrated through practical use that the system could measure
the detailed changes in the motion characteristics with the walking training and theattachment of the brace, showing its usefulness as a quantitative assessment of theefficacy of the rehabilitation. Moreover, the motion characteristics that they cannot
determine only by direct observation can be obtained quantitatively, and thus, the PT can
feedback the motion drawbacks and improvement to the patients more accurately.
Figure 7 shows the long-term measurement of gait in (a) a 56-year-old male subject
with hemiplegia of the right side and (b) a 50-year-old male subject with hemiplegia ofthe left side. These recordings show the walking speed (Vco), cadence (C), range of thethigh angle change obtained using the difference between two angles in the heel contact
and heel off (θth,c, θth,o), and range of knee angle change during one walking cycle and S.D.
(accumulated 20 walking cycles) of the thigh angle in heel contact. Each plot denotes
the average value during 10 m of walking after rehabilitation.
From the results shown in Fig. 7(a), the walking speed Vco and cadence C became
faster from 42 to 57 days and then the Vco of about 0.6–0.7 m/s and C of about 110 steps/

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Range of knee
angle change (deg)Thigh angle (deg)Cadence (steps/min)

Walking speed (m/s)

Sensors and Materials, Vol. 24, No. 6 (2012)

(a)

0.9
0.8

0.8

0.7

0.7

0.6

0.6

0.5

0.5

0.4

Vco

0.3

120
110

100

100

90

90

80

80

70

70

C

60

30
20
10
0
-10
-20
θth,o -30
-40

θth,c

Heel contact

Heel off

45

40

40

35

35

30

30

25

25

20

θkn,w

θth,c,sd
40

45

50

55

60

C

60

45

14
12
10
8
6
4
2
0

Vco

0.3

110

15

SD of thigh angle
in heel contact (deg)

0.4

120

30
20
10
0
-10
-20
-30
-40

(b )

0.9

65

70

Rehabilitation length (day)

75

80

θth,c

Heel contact

Heel off

θth,o

20

θkn,w

15
14
12
10
8
6
4
2
0

θth,c,sd
15

20

25

30

35

40

45

50

55

60

Rehabilitation length (day)Fig. 7. Long-term measurement of gait in (a) a 56-year-old male subject with hemiplegia of theright side and (b) a 50-year-old male subject with hemiplegia of the left side.

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min were maintained. Therefore, performance and walking efficiency could be improvedby the rehabilitation. Moreover, the ranges of the thigh and knee angle changes became
larger; therefore, the joint motion was improved by the rehabilitation. On the other
hand, the S.D. of the thigh angle in the heel contact, θth,c,sd, i.e., the repeatability of thighmotion, became worse from 57 to 64 days while the walking speed, Vco, was maintained.
However, after then, the S.D. values became small with increasing walking speed until
75 days, showing the improvement of the motion repeatability.
Also, as shown in Fig. 7(b), the walking speed Vco, cadence C, and angle changes
became larger by 42 days. However, in 56 days, the walking speed Vco and cadence C
became smaller than those obtained in 42 days and the θth,c,sd became larger, showing thedecrease in the repeatability of the motion. Therefore, the patients would need further
rehabilitation by the PT.
Therefore, the present system can be useful for the quantitative evaluation of theeffectiveness of the long-term rehabilitation, setting up the final goal of the training,
giving the permission for the walking without assistance by a therapist during dailyliving, and so on, using analysis results such as that shown in Fig. 7. As a future
research, more measurements and analyses with a longer time and many subjects would
be needed to investigate a more quantitative method of evaluating the efficacy of therehabilitation. Also, the changes in the motion characteristics and recovery processes inmany symptoms should be analyzed.
The PT may have to determine the rotation of the body segments and, thus, theangular measurements using the three-axes accelerometer and gyrosensor signals would
also be needed. On the other hand, the sensor units of the lower limb were attached toonly the subject’s paralyzed side in this study. If the sensor units are attached to bothsides, i.e., the paralyzed and healthy sides, a more detailed analysis can be carried out for
the evaluation of the patient’s motion, considering these viewpoints and improvementsof the system as subjects for future investigation.

4.

Conclusions

In this study, we developed a new gait monitoring system for use in the walking
training in rehabilitation. The results demonstrated that the present system can be used
to measure the posture changes, i.e., the trunk, thigh, and shank angle changes andthe walking speed with good accuracy during walking. On the other hand, the system
successfully functioned during rehabilitation in the hospital. It is clearly shown throughpractical monitoring that this system could be used to measure the detailed motion
characteristics of the patients with hemiplegia as the changes in the trunk, thigh, andshank angles in the sagittal plane and walking speed for one walking cycle. We suggestthat the efficacy of the rehabilitation can be quantitatively evaluated using the system.
Further investigations will be needed, such as the availability of measurements for alonger period of time in more patients, three-dimensional analysis of the motion during
walking, and application for analysis of other motions including standing-up, sitting,lying, and so on.

Sensors and Materials, Vol. 24, No. 6 (2012)

373

Acknowledgement
The authors wish to thank the patients and therapists in the rehabilitation roomof Fujimoto Hayasuzu Hospital. This work was supported in part by the Japanese
Ministry of Internal Affairs and Communications under the Strategic Information andCommunications R&D Promotion Programme (SCOPE; No. 102305004, 2010 and2011), for which we wish to express our sincere appreciation.

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