The fiscal burden of increasing social security ex- penses for the elderly is a major issue in Japan. The elderly lose physical fitness as they age. In particular, degradation in walking ability leads to falling accidents which pose a risk of serious wounds such as fractures that may resign one to a life in bed. In this study, we propose a system that is capable of measuring and analyzing an elderly person’s walking ability. The proposed system, using Kinect motion sensor and written in the programming language Scratch, can measure an elderly person’s gait accurately and be operated simply without specialist knowledge. Through real experiments with 16 subjects, some potential problems in walking were easily found by analyzing the measured data.

1. A Physical Strength Measurement
and Analysis System for Elderly
People Using Motion Sensors
Akira Kawai1
Shinichi Taniguchi2
Huili Li2
Shizue Izumi1
1, Data Science Department, Shiga University
2, Graduate School of Economics, Shiga University
1

2. The Key of Super-aged Society Problem
2
Super
Aged
Society
Population above
65 years old
27.3%(2016)
No solution for
population
composition
Social
security cost
Nursing-
needed
elderlies
Elderly living
alone
Government
financial
burden
Family
nursing
burden
Workers
Community
Solution of Ease
the burden
Prolonging
healthy life

3. Prolong Healthy Life
⇒ Maintain Walking Ability
• “Falling” is very dangerous for elderly
•1/3 elderly suffer more than 1 fall a year
• Falling is no.2 cause of death for elderly in accident.
• The major cause of proximal femoral fractures (78%)
• Most of the patients have to require nursing care
3
We propose a method to easily
measure walking ability
of elderly
to prolong health life of elderly by
cooperative with medical institution

4. Related Researches
• A method to analyze motion quantitatively and
visually by using Kinect
• Operation of woodworking plane
• Observe arm joint sites
• Effective in learning and improving skills
• Too difficult to implement/operate for medical staff
• A model for exercise capacity and body balance
evaluation
• Head center of gravity fluctuation and body joint angle ⇒
Body balance evaluation
• The cost of optical motion capture is very high, and
there are many space restrictions
4

5. Our proposal: method for elderly
walking ability measurement
• Joint detection by Kinect
• Screen display and Control program by Scratch
• Easy way to storage data, polish, and analyze
5
Elderly
Kinect
Motion Sensor
System Operator
(Medical Staff)
Scratch
Program UI
Display Elderly
Motion
Output Data
Analysis
Data Preprocessing
Correction

6. Measurement System
• Motion Sensor: KinectV2
• Programing Environment:
• Scratch 1.4, Kinect2Scratch SDK1.5
• PC：
• DELL XPS15, core i5 2.3GHz, Memory 8GB
• Sampling interval:
• about 0.2s, data number per joint:128
• Measured joints:
• both feet, both hands, both knees, spine(apex, end)
• 5 analysis items
1. Walking Speed
2. Stride
3. Vertical Movement Range of Knees
4. Arm Swing Width
5. Shaking Amount of Trunk
6

7. Evaluation Experiment
• Place: Kawauchi hall in Maibara City, Shiga, Japan
• Subject: 16 elderlies (60s-80s, M3, F13)
• Measurement courses:
7
1, Footstep
2, 2m walk course x3
(front-to-back, left-to-right,
right-to-left)

8. Walking Speed
• subjects walk a 2m course 3 times
• Walking speed = distance / time
• Most of the subjects are slower than
normal people
• The walking distance is short
• Walking on tatami (rice straw mats) w/o shoes
• We note some females were very slow
• 3, 4, 5, 12
8
No Age Sex
Ave. Speed
（m/s）
1 65 M 0.505
2 70 M 0.642
3 75 F 0.506
4 Unknown F 0.499
5 75 F 0.436
6 Unknown F 0.599
7 80 F 0.529
8 70 F 0.572
9 70 F 0.705
10 60 F 0.588
11 70 F 0.545
12 70 F 0.516
13 70 F 0.777
14 80 F 0.549
15 80 F 0.570
16 75 M 1.097
Speed ( m/s ) Speed Groups Persons
＞1.3m/s Fast
1.0--1.3m/s Same as healthy ppl 1
0.6--1.0m/s Slightly slower 3
＜0.6m/s Slow 12

9. Stride
• We measured the z-axis position of
<foot-left>, <foot-right>
• Divide the distance from the first step to
the third step by 2
*A survey result: Walk data of 1131elderlies
Our result
• The short stride is reasoned by
• (1) short course, (2) w/o shoes
• Females(4, 5) have weak legs
9
No Age Sex Stride(m)
1 65 M 0.410
2 70 M 0.531
3 75 F 0.462
4 Unknown F 0.246
5 75 F 0.316
6 Unknown F 0.497
7 80 F 0.351
8 70 F 0.390
9 70 F 0.450
10 60 F 0.290
11 70 F 0.274
12 70 F 0.426
13 70 F 0.450
14 80 F 0.361
15 80 F 0.324
16 75 M 0.622
Age Male Female
65-69 0.67±0.09 0.59±0.09
70-74 0.62±0.09 0.53±0.10
75-79 0.59±0.12 0.51±0.09
80- 0.51±0.10 0.44±0.10
Age Male Female
65-69
70-74 0.53 0.40
75-79 0.62 0.39
80- 0.35

10. An Example for Data Correction:
Vertical Movement Range of Knees
• Subject: 70s Male
• Course: Stepping at specified position
• Data: y axis positions of <knee-left>, <knee-right>
• The vertical movement range = average difference between
above and below of same knee
• Correction: Subject accidentally moved forward, resulting in
negative y-axis data (Shown by diagonal line in Graph)
• Fixed by regression analysis
10
-120.000
-115.000
-110.000
-105.000
-100.000
-95.000
-90.000
-85.000
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101106111116121126
Moved forward Returning stepping

11. 11
-120
-115
-110
-105
-100
-95
-90
-85
1 2 3 4 5 6 7 8 9 10 11 12 13 14
kneeleft
山 谷
-120
-115
-110
-105
-100
-95
-90
-85
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
kneeright
山 谷
• Range of left knee: 5.202
• Range of right knee: 4.866
• Difference: 0.336 (6.9%)
• Left knee moves higher than right
• The center of gravity of the body
is shifted to the right
• When his right foot is landing, a
larger weight is placed on his right
knee → increasing failure risk
Vertical Movement Range of Knees (70s, M)

12. Arm Swing Width (70s, Male)
• Stepping at fixed position
• Difference between <hand-left> and <hand-right>
z-axis positions
12
1.500
1.700
1.900
2.100
2.300
2.500
2.700
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
96
1…
1…
1…
1…
1…
1…
hand left z

13. 13
Handleft 山 谷 振 り 幅
（ｍ）
2.248 2.146 0.102
2.252 2.071 0.181
2.261 2.049 0.212
2.367 1.969 0.399
2.298 1.906 0.392
2.339 1.915 0.425
2.281 1.951 0.330
2.269 1.936 0.333
2.256 1.928 0.329
2.184 1.886 0.299
2.249 1.933 0.316
2.198 1.860 0.338
2.251 1.950 0.302
2.194 1.897 0.297
2.181 1.926 0.255
2.179 1.888 0.291
2.113 1.825 0.288
2.059 1.867 0.192
2.142 1.885 0.257
2.335 1.889 0.447
2.369 2.001 0.368
2.412 2.020 0.392
2.399 1.990 0.409
2.406 2.031 0.375
2.371 1.935 0.436
2.363 1.971 0.392
2.427 1.943 0.485
2.306 1.943 0.362
2.381 1.950 0.431
2.334 2.038 0.296
2.451 2.000 0.451
2.321 1.874 0.446
2.206 1.873 0.333
2.381 1.880 0.501
2.366 1.978 0.388
2.286 1.958 0.328
Handright 山 谷 振 り 幅
（ｍ）
2.264 2.097 0.167
2.264 2.063 0.201
2.256 2.066 0.191
2.283 2.116 0.167
2.362 2.056 0.307
2.371 2.069 0.301
2.309 2.068 0.241
2.291 2.014 0.277
2.294 2.047 0.247
2.211 2.030 0.181
2.233 1.995 0.238
2.223 2.000 0.224
2.224 1.982 0.243
2.199 2.004 0.195
2.200 1.951 0.249
2.160 1.919 0.241
2.150 1.928 0.222
2.081 1.914 0.167
2.064 1.950 0.115
2.177 2.096 0.082
2.292 2.131 0.161
2.298 2.112 0.187
2.292 2.152 0.140
2.298 2.082 0.216
2.277 2.074 0.203
2.222 2.083 0.139
2.285 2.150 0.135
2.314 2.126 0.188
2.249 2.081 0.169
2.262 2.139 0.123
2.226 2.046 0.180
2.187 1.983 0.205
2.268 2.050 0.217
2.311 2.068 0.244
2.349 2.058 0.290
• Range of left arm: 0.343m
• Range of right arm: 0.203m
• Difference: 0.14m (69%!)
• Left arm swings significant larger
than right
• Linked to right knee, larger
weight is placed on right knee
• Appearance of failure in right
body
Arm Swing Width (70s, Male)

14. Shaking amount of Trunk
• Subject: 70s, male
• Course: stepping at specified position
• X-axis positions of <spine-shoulder> and <spine-base>
• Calculates left and right shaking of point <spine-shoulder> with
respect to point <spine-base> every 0.2 seconds
• Shaking amount (Xn) ＝ spine-shoulder x(Sn) –spine-base x(Bn)
• If Xn>0 Trunk is shaking to right side
• If Xn＜0 Trunk is shaking to left side
Shaking amount of Trunk = difference between above and below
14
spineshoulder x
spinebase x
-10
-5
0
5
10
15
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
Swing width of Trunk

15. 15
左方向（谷） 右方向（山） 揺れ幅
-2.434 1.168 3.602
-1.970 8.415 10.385
-4.698 6.736 11.434
-4.985 6.036 11.021
-5.212 5.539 10.751
-4.767 7.528 12.295
-3.917 7.095 11.012
-3.279 7.751 11.030
-6.265 7.994 14.260
-1.306 6.530 7.836
-5.361 6.761 12.122
-5.530 7.180 12.709
-3.624 7.652 11.276
-0.437 7.140 7.577
-3.178 6.113 9.292
-1.613 8.463 10.075
-3.697 8.699 12.396
-4.499 9.234 13.733
-0.232 9.322 9.554
-5.271 7.339 12.609
-3.459 5.929 9.388
-4.965 6.539 11.504
-6.825 6.504 13.329
-4.607 6.284 10.891
-3.604 6.645 10.248
-1.593 8.431 10.025
-6.621 6.180 12.801
-5.776 7.333 13.109
-5.601 4.142 9.744
-4.518 7.537 12.055
-5.914 6.434 12.348
-7.877 4.870 12.747
-6.665 4.029 10.695
-6.906 2.613 9.518
-5.005 11.325 16.330
-4.111 5.701 9.813
-3.585 6.100 9.685
• Shaking to left: Ave. -4.322
• Shaking to right: Ave. 6.734
• Entire shaking amount: 11.059
• Shaking ratio: Right 55.8%
• Appearance of failure in waist
• May decrease walking ability and make him
easier to fall
Shaking amount of Trunk (70s, Male)

16. 16
-135
-130
-125
-120
-115
-110
-105
-100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
kneeleft
山 谷
-135
-125
-115
-105
-95
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
kneeright
山 谷
• Range of left knee: 3.228
• Range of right knee: 3.116
• Difference: 0.112 (3.6%)
• The movable ranges
of both knees are too small
• The risk of falling is high
Vertical Movement Range of Knees (70s, F)

17. 17
• Range of left arm: 0.085m
• Range of right arm: 0.057m
• Difference between arms: 0.028m
• We note her arms are barely shaken, the
reason is that her upper body inclined
forward largely
• Both arms stretched back to keep balance
• Very high risk of falls
• In the case of a fall, it is dangerous
because the face directly hits the ground
Arm Swing Width

18. 18
• Measure time per subject
• About 2—3 minutes
• How to run the measurement
• Just click the start flag on GUI (Stretch)
• About walking speed and stride
• Since the distance is short, the result may be different to their normal life
• We analyzed the relative differences between subjects
• About movement range of knees, swing of arms and trunk
• Multiple problems were found by the subject alone
• Next challenges: (1) Increase samples number (2)Improve
correction accuracy, (3) Collaborate with medical
professional
Summary

19. Thank you!
Any question?
19

20. 20

21. y = -0.6269x - 99.577
-114.000
-112.000
-110.000
-108.000
-106.000
-104.000
-102.000
-100.000
-98.000
-96.000
0.0 5.0 10.0 15.0 20.0
Y
X 値 1
X 値 1 観測値グラフ
補正・左膝（山）
概要
回帰統計
重相関 R 0.9096
重決定 R2 0.827373
補正 R2 0.817218
標準誤差 1.65806
観測数 19
分散分析表
自由度 変動 分散
観測された分
散比 有意 F
回帰 1 223.9963 223.9963 81.47802 6.8E-08
残差 17 46.73576 2.749162
合計 18 270.7321
係数 標準誤差 t P-値 下限 95% 上限 95% 下限 95.0% 上限 95.0%
切片 -99.5769 0.791835 -125.755 1.11E-26 -101.248 -97.9063 -101.248 -97.90627
X 値 1 -0.62688 0.069448 -9.02652 6.8E-08 -0.7734 -0.48035 -0.7734 -0.480354
回帰分析の結果より、
R2=0.83
F値＜0.01
P値＜0.01
よって、回帰式による補正
は妥当。
Ｙ0
Ｙ
Δ
Ｙ
21

22. Ｙn＝ｙn ＋△Y
補正前の値(計測
値）
補正後の
値
残差出力
観測値 予測値: Y 残差
1 -100.204 -1.44573
2 -100.831 -0.73185
3 -101.458 -2.22027
4 -102.084 3.857044
5 -102.711 -1.06032
6 -103.338 -0.67544
7 -103.965 1.321438
8 -104.592 1.833116
9 -105.219 1.713794
10 -105.846 0.622472
11 -106.473 -0.97495
12 -107.099 0.463228
13 -107.726 -1.82769
14 -108.353 -0.46532
15 -108.98 1.095761
16 -109.607 -2.10356
17 -110.234 1.084217
18 -110.861 -1.3942
19 -111.488 0.908273
Y-Y0 △Y ｙ Yｎ
0 0 -101.65 -101.65
-0.62688 0.626878 -101.563 -100.936
-1.25376 1.253756 -103.678 -102.424
-1.88063 1.880634 -98.2274 -96.3467
-2.50751 2.507512 -103.772 -101.264
-3.13439 3.13439 -104.014 -100.879
-3.76127 3.761268 -102.644 -98.8823
-4.38815 4.388146 -102.759 -98.3707
-5.01502 5.015024 -103.505 -98.49
-5.6419 5.641902 -105.223 -99.5813
-6.26878 6.26878 -107.448 -101.179
-6.89566 6.895658 -106.636 -99.7405
-7.52254 7.522536 -109.554 -102.031
-8.14941 8.149414 -108.819 -100.669
-8.77629 8.776292 -107.884 -99.108
-9.40317 9.403169 -111.711 -102.307
-10.03 10.03005 -109.15 -99.1196
-10.6569 10.65693 -112.255 -101.598
-11.2838 11.2838 -110.579 -99.2955
y = -0.6269x - 99.577
-114.000
-112.000
-110.000
-108.000
-106.000
-104.000
-102.000
-100.000
-98.000
-96.000
-94.000
0.0 5.0 10.0 15.0 20.0
Y
X 値 1
X 値 1 観測値グラフ
Y
予測値: Y
22