高齡社會國際設計論壇 International Conference on Design for Aging Society

高齡社會國際設計論壇
International Conference on Design for Aging Society

Designing Sustainable and Inclusive Communities for an Aging Society - Challenges, Solutions, and Future Talents

2024.8.8(Thu)-8.9(Fri)
國立成功大學力行校區崇華廳

import pandas as pd

# Assume these are the national average values

average_values = {

"age_range": ["65-70", "71-75", "76-80", "81-85", "86+"],

"upper_body_strength": [50, 45, 40, 35, 30], # Upper body strength

"lower_body_strength": [60, 55, 50, 45, 40], # Lower body strength

"grip_strength": [30, 28, 25, 22, 20], # Grip strength

"reaction_speed": [0.5, 0.6, 0.7, 0.8, 0.9], # Reaction speed (seconds)

"muscle_mass": [70, 65, 60, 55, 50] # Muscle mass (kg)

}

# Convert the average values to a DataFrame

average_df = pd.DataFrame(average_values)

def analyze_elderly_fitness(age, upper_body_strength, lower_body_strength, grip_strength, reaction_speed, muscle_mass):

# Determine the age range based on age

if 65 <= age <= 70:

age_range = "65-70"

elif 71 <= age <= 75:

age_range = "71-75"

elif 76 <= age <= 80:

age_range = "76-80"

elif 81 <= age <= 85:

age_range = "81-85"

else:

age_range = "86+"

# Find the corresponding average values

avg_values = average_df[average_df['age_range'] == age_range].iloc[0]

# Calculate the difference percentage

def calculate_difference_percentage(actual, average):

return ((actual - average) / average) * 100

differences = {

"upper_body_strength_diff": calculate_difference_percentage(upper_body_strength, avg_values["upper_body_strength"]),

"lower_body_strength_diff": calculate_difference_percentage(lower_body_strength, avg_values["lower_body_strength"]),

"grip_strength_diff": calculate_difference_percentage(grip_strength, avg_values["grip_strength"]),

"reaction_speed_diff": calculate_difference_percentage(reaction_speed, avg_values["reaction_speed"]),

"muscle_mass_diff": calculate_difference_percentage(muscle_mass, avg_values["muscle_mass"]),

}

return differences

# Test data

age = 72

upper_body_strength = 50

lower_body_strength = 55

grip_strength = 27

reaction_speed = 0.65

muscle_mass = 64

result = analyze_elderly_fitness(age, upper_body_strength, lower_body_strength, grip_strength, reaction_speed, muscle_mass)

print(result)

import pandas as pd

# Assume these are the national average values

average_values = {

"age_range": ["65-70", "71-75", "76-80", "81-85", "86+"],

"upper_body_strength": [50, 45, 40, 35, 30], # Upper body strength

"lower_body_strength": [60, 55, 50, 45, 40], # Lower body strength

"grip_strength": [30, 28, 25, 22, 20], # Grip strength

"reaction_speed": [0.5, 0.6, 0.7, 0.8, 0.9], # Reaction speed (seconds)

"muscle_mass": [70, 65, 60, 55, 50] # Muscle mass (kg)

}

# Convert the average values to a DataFrame

average_df = pd.DataFrame(average_values)

def analyze_elderly_fitness(age, upper_body_strength, lower_body_strength, grip_strength, reaction_speed, muscle_mass):

# Determine the age range based on age

if 65 <= age <= 70:

age_range = "65-70"

elif 71 <= age <= 75:

age_range = "71-75"

elif 76 <= age <= 80:

age_range = "76-80"

elif 81 <= age <= 85:

age_range = "81-85"

else:

age_range = "86+"

# Find the corresponding average values

avg_values = average_df[average_df['age_range'] == age_range].iloc[0]

# Calculate the difference percentage

def calculate_difference_percentage(actual, average):

return ((actual - average) / average) * 100

differences = {

"upper_body_strength_diff": calculate_difference_percentage(upper_body_strength, avg_values["upper_body_strength"]),

"lower_body_strength_diff": calculate_difference_percentage(lower_body_strength, avg_values["lower_body_strength"]),

"grip_strength_diff": calculate_difference_percentage(grip_strength, avg_values["grip_strength"]),

"reaction_speed_diff": calculate_difference_percentage(reaction_speed, avg_values["reaction_speed"]),

"muscle_mass_diff": calculate_difference_percentage(muscle_mass, avg_values["muscle_mass"]),

}

return differences

# Test data

age = 72

upper_body_strength = 50

lower_body_strength = 55

grip_strength = 27

reaction_speed = 0.65

muscle_mass = 64

result = analyze_elderly_fitness(age, upper_body_strength, lower_body_strength, grip_strength, reaction_speed, muscle_mass)

print(result)

import pandas as pd

# Assume these are the national average values

average_values = {

"age_range": ["65-70", "71-75", "76-80", "81-85", "86+"],

"upper_body_strength": [50, 45, 40, 35, 30], # Upper body strength

"lower_body_strength": [60, 55, 50, 45, 40], # Lower body strength

"grip_strength": [30, 28, 25, 22, 20], # Grip strength

"reaction_speed": [0.5, 0.6, 0.7, 0.8, 0.9], # Reaction speed (seconds)

"muscle_mass": [70, 65, 60, 55, 50] # Muscle mass (kg)

}

# Convert the average values to a DataFrame

average_df = pd.DataFrame(average_values)

def analyze_elderly_fitness(age, upper_body_strength, lower_body_strength, grip_strength, reaction_speed, muscle_mass):

# Determine the age range based on age

if 65 <= age <= 70:

age_range = "65-70"

elif 71 <= age <= 75:

age_range = "71-75"

elif 76 <= age <= 80:

age_range = "76-80"

elif 81 <= age <= 85:

age_range = "81-85"

else:

age_range = "86+"

# Find the corresponding average values

avg_values = average_df[average_df['age_range'] == age_range].iloc[0]

# Calculate the difference percentage

def calculate_difference_percentage(actual, average):

return ((actual - average) / average) * 100

differences = {

"upper_body_strength_diff": calculate_difference_percentage(upper_body_strength, avg_values["upper_body_strength"]),

"lower_body_strength_diff": calculate_difference_percentage(lower_body_strength, avg_values["lower_body_strength"]),

"grip_strength_diff": calculate_difference_percentage(grip_strength, avg_values["grip_strength"]),

"reaction_speed_diff": calculate_difference_percentage(reaction_speed, avg_values["reaction_speed"]),

"muscle_mass_diff": calculate_difference_percentage(muscle_mass, avg_values["muscle_mass"]),

}

return differences

# Test data

age = 72

upper_body_strength = 50

lower_body_strength = 55

grip_strength = 27

reaction_speed = 0.65

muscle_mass = 64

result = analyze_elderly_fitness(age, upper_body_strength, lower_body_strength, grip_strength, reaction_speed, muscle_mass)

print(result)

import pandas as pd

# Assume these are the national average values

average_values = {

"age_range": ["65-70", "71-75", "76-80", "81-85", "86+"],

"upper_body_strength": [50, 45, 40, 35, 30], # Upper body strength

"lower_body_strength": [60, 55, 50, 45, 40], # Lower body strength

"grip_strength": [30, 28, 25, 22, 20], # Grip strength

"reaction_speed": [0.5, 0.6, 0.7, 0.8, 0.9], # Reaction speed (seconds)

"muscle_mass": [70, 65, 60, 55, 50] # Muscle mass (kg)

}

# Convert the average values to a DataFrame

average_df = pd.DataFrame(average_values)

def analyze_elderly_fitness(age, upper_body_strength, lower_body_strength, grip_strength, reaction_speed, muscle_mass):

# Determine the age range based on age

if 65 <= age <= 70:

age_range = "65-70"

elif 71 <= age <= 75:

age_range = "71-75"

elif 76 <= age <= 80:

age_range = "76-80"

elif 81 <= age <= 85:

age_range = "81-85"

else:

age_range = "86+"

# Find the corresponding average values

avg_values = average_df[average_df['age_range'] == age_range].iloc[0]

# Calculate the difference percentage

def calculate_difference_percentage(actual, average):

return ((actual - average) / average) * 100

differences = {

"upper_body_strength_diff": calculate_difference_percentage(upper_body_strength, avg_values["upper_body_strength"]),

"lower_body_strength_diff": calculate_difference_percentage(lower_body_strength, avg_values["lower_body_strength"]),

"grip_strength_diff": calculate_difference_percentage(grip_strength, avg_values["grip_strength"]),

"reaction_speed_diff": calculate_difference_percentage(reaction_speed, avg_values["reaction_speed"]),

"muscle_mass_diff": calculate_difference_percentage(muscle_mass, avg_values["muscle_mass"]),

}

return differences

# Test data

age = 72

upper_body_strength = 50

lower_body_strength = 55

grip_strength = 27

reaction_speed = 0.65

muscle_mass = 64

result = analyze_elderly_fitness(age, upper_body_strength, lower_body_strength, grip_strength, reaction_speed, muscle_mass)

print(result)

關於論壇 About

全球人口分布正以前所未有速度邁向超高齡化，為滿足實現新高齡世代的獨特需求，我們需以更快腳步規劃與設計下世代新的高齡城市、社區、產品、服務與未來生活情境，本次國際論壇即聚焦於此些當前關鍵議題，致力於探討營造一個具創新思維與未來人才準備的的新高齡永續共融環境。

As the global population ages at an unprecedented rate, there is an urgent need to reimagine and redesign our cities, communities, products, services, and future life to meet the unique needs of the new elderly. This conference is dedicated to addressing these critical issues and fostering an environment where innovative ideas and future talents can flourish.

瞭解更多

import pandas as pd

# Assume these are the national average values

average_values = {

"age_range": ["65-70", "71-75", "76-80", "81-85", "86+"],

"upper_body_strength": [50, 45, 40, 35, 30], # Upper body strength

"lower_body_strength": [60, 55, 50, 45, 40], # Lower body strength

"grip_strength": [30, 28, 25, 22, 20], # Grip strength

"reaction_speed": [0.5, 0.6, 0.7, 0.8, 0.9], # Reaction speed (seconds)

"muscle_mass": [70, 65, 60, 55, 50] # Muscle mass (kg)

}

# Convert the average values to a DataFrame

average_df = pd.DataFrame(average_values)

def analyze_elderly_fitness(age, upper_body_strength, lower_body_strength, grip_strength, reaction_speed, muscle_mass):

# Determine the age range based on age

if 65 <= age <= 70:

age_range = "65-70"

elif 71 <= age <= 75:

age_range = "71-75"

elif 76 <= age <= 80:

age_range = "76-80"

elif 81 <= age <= 85:

age_range = "81-85"

else:

age_range = "86+"

# Find the corresponding average values

avg_values = average_df[average_df['age_range'] == age_range].iloc[0]

# Calculate the difference percentage

def calculate_difference_percentage(actual, average):

return ((actual - average) / average) * 100

differences = {

"upper_body_strength_diff": calculate_difference_percentage(upper_body_strength, avg_values["upper_body_strength"]),

"lower_body_strength_diff": calculate_difference_percentage(lower_body_strength, avg_values["lower_body_strength"]),

"grip_strength_diff": calculate_difference_percentage(grip_strength, avg_values["grip_strength"]),

"reaction_speed_diff": calculate_difference_percentage(reaction_speed, avg_values["reaction_speed"]),

"muscle_mass_diff": calculate_difference_percentage(muscle_mass, avg_values["muscle_mass"]),

}

return differences

# Test data

age = 72

upper_body_strength = 50

lower_body_strength = 55

grip_strength = 27

reaction_speed = 0.65

muscle_mass = 64

result = analyze_elderly_fitness(age, upper_body_strength, lower_body_strength, grip_strength, reaction_speed, muscle_mass)

print(result)

import pandas as pd

# Assume these are the national average values

average_values = {

"age_range": ["65-70", "71-75", "76-80", "81-85", "86+"],

"upper_body_strength": [50, 45, 40, 35, 30], # Upper body strength

"lower_body_strength": [60, 55, 50, 45, 40], # Lower body strength

"grip_strength": [30, 28, 25, 22, 20], # Grip strength

"reaction_speed": [0.5, 0.6, 0.7, 0.8, 0.9], # Reaction speed (seconds)

"muscle_mass": [70, 65, 60, 55, 50] # Muscle mass (kg)

}

# Convert the average values to a DataFrame

average_df = pd.DataFrame(average_values)

def analyze_elderly_fitness(age, upper_body_strength, lower_body_strength, grip_strength, reaction_speed, muscle_mass):

# Determine the age range based on age

if 65 <= age <= 70:

age_range = "65-70"

elif 71 <= age <= 75:

age_range = "71-75"

elif 76 <= age <= 80:

age_range = "76-80"

elif 81 <= age <= 85:

age_range = "81-85"

else:

age_range = "86+"

# Find the corresponding average values

avg_values = average_df[average_df['age_range'] == age_range].iloc[0]

# Calculate the difference percentage

def calculate_difference_percentage(actual, average):

return ((actual - average) / average) * 100

differences = {

"upper_body_strength_diff": calculate_difference_percentage(upper_body_strength, avg_values["upper_body_strength"]),

"lower_body_strength_diff": calculate_difference_percentage(lower_body_strength, avg_values["lower_body_strength"]),

"grip_strength_diff": calculate_difference_percentage(grip_strength, avg_values["grip_strength"]),

"reaction_speed_diff": calculate_difference_percentage(reaction_speed, avg_values["reaction_speed"]),

"muscle_mass_diff": calculate_difference_percentage(muscle_mass, avg_values["muscle_mass"]),

}

return differences

# Test data

age = 72

upper_body_strength = 50

lower_body_strength = 55

grip_strength = 27

reaction_speed = 0.65

muscle_mass = 64

result = analyze_elderly_fitness(age, upper_body_strength, lower_body_strength, grip_strength, reaction_speed, muscle_mass)

print(result)

import pandas as pd

# Assume these are the national average values

average_values = {

"age_range": ["65-70", "71-75", "76-80", "81-85", "86+"],

"upper_body_strength": [50, 45, 40, 35, 30], # Upper body strength

"lower_body_strength": [60, 55, 50, 45, 40], # Lower body strength

"grip_strength": [30, 28, 25, 22, 20], # Grip strength

"reaction_speed": [0.5, 0.6, 0.7, 0.8, 0.9], # Reaction speed (seconds)

"muscle_mass": [70, 65, 60, 55, 50] # Muscle mass (kg)

}

# Convert the average values to a DataFrame

average_df = pd.DataFrame(average_values)

def analyze_elderly_fitness(age, upper_body_strength, lower_body_strength, grip_strength, reaction_speed, muscle_mass):

# Determine the age range based on age

if 65 <= age <= 70:

age_range = "65-70"

elif 71 <= age <= 75:

age_range = "71-75"

elif 76 <= age <= 80:

age_range = "76-80"

elif 81 <= age <= 85:

age_range = "81-85"

else:

age_range = "86+"

# Find the corresponding average values

avg_values = average_df[average_df['age_range'] == age_range].iloc[0]

# Calculate the difference percentage

def calculate_difference_percentage(actual, average):

return ((actual - average) / average) * 100

differences = {

"upper_body_strength_diff": calculate_difference_percentage(upper_body_strength, avg_values["upper_body_strength"]),

"lower_body_strength_diff": calculate_difference_percentage(lower_body_strength, avg_values["lower_body_strength"]),

"grip_strength_diff": calculate_difference_percentage(grip_strength, avg_values["grip_strength"]),

"reaction_speed_diff": calculate_difference_percentage(reaction_speed, avg_values["reaction_speed"]),

"muscle_mass_diff": calculate_difference_percentage(muscle_mass, avg_values["muscle_mass"]),

}

return differences

# Test data

age = 72

upper_body_strength = 50

lower_body_strength = 55

grip_strength = 27

reaction_speed = 0.65

muscle_mass = 64

result = analyze_elderly_fitness(age, upper_body_strength, lower_body_strength, grip_strength, reaction_speed, muscle_mass)

print(result)

import pandas as pd

# Assume these are the national average values

average_values = {

"age_range": ["65-70", "71-75", "76-80", "81-85", "86+"],

"upper_body_strength": [50, 45, 40, 35, 30], # Upper body strength

"lower_body_strength": [60, 55, 50, 45, 40], # Lower body strength

"grip_strength": [30, 28, 25, 22, 20], # Grip strength

"reaction_speed": [0.5, 0.6, 0.7, 0.8, 0.9], # Reaction speed (seconds)

"muscle_mass": [70, 65, 60, 55, 50] # Muscle mass (kg)

}

# Convert the average values to a DataFrame

average_df = pd.DataFrame(average_values)

def analyze_elderly_fitness(age, upper_body_strength, lower_body_strength, grip_strength, reaction_speed, muscle_mass):

# Determine the age range based on age

if 65 <= age <= 70:

age_range = "65-70"

elif 71 <= age <= 75:

age_range = "71-75"

elif 76 <= age <= 80:

age_range = "76-80"

elif 81 <= age <= 85:

age_range = "81-85"

else:

age_range = "86+"

# Find the corresponding average values

avg_values = average_df[average_df['age_range'] == age_range].iloc[0]

# Calculate the difference percentage

def calculate_difference_percentage(actual, average):

return ((actual - average) / average) * 100

differences = {

"upper_body_strength_diff": calculate_difference_percentage(upper_body_strength, avg_values["upper_body_strength"]),

"lower_body_strength_diff": calculate_difference_percentage(lower_body_strength, avg_values["lower_body_strength"]),

"grip_strength_diff": calculate_difference_percentage(grip_strength, avg_values["grip_strength"]),

"reaction_speed_diff": calculate_difference_percentage(reaction_speed, avg_values["reaction_speed"]),

"muscle_mass_diff": calculate_difference_percentage(muscle_mass, avg_values["muscle_mass"]),

}

return differences

# Test data

age = 72

upper_body_strength = 50

lower_body_strength = 55

grip_strength = 27

reaction_speed = 0.65

muscle_mass = 64

result = analyze_elderly_fitness(age, upper_body_strength, lower_body_strength, grip_strength, reaction_speed, muscle_mass)

print(result)

import pandas as pd

# Assume these are the national average values

average_values = {

"age_range": ["65-70", "71-75", "76-80", "81-85", "86+"],

"upper_body_strength": [50, 45, 40, 35, 30], # Upper body strength

"lower_body_strength": [60, 55, 50, 45, 40], # Lower body strength

"grip_strength": [30, 28, 25, 22, 20], # Grip strength

"reaction_speed": [0.5, 0.6, 0.7, 0.8, 0.9], # Reaction speed (seconds)

"muscle_mass": [70, 65, 60, 55, 50] # Muscle mass (kg)

}

# Convert the average values to a DataFrame

average_df = pd.DataFrame(average_values)

def analyze_elderly_fitness(age, upper_body_strength, lower_body_strength, grip_strength, reaction_speed, muscle_mass):

# Determine the age range based on age

if 65 <= age <= 70:

age_range = "65-70"

elif 71 <= age <= 75:

age_range = "71-75"

elif 76 <= age <= 80:

age_range = "76-80"

elif 81 <= age <= 85:

age_range = "81-85"

else:

age_range = "86+"

# Find the corresponding average values

avg_values = average_df[average_df['age_range'] == age_range].iloc[0]

# Calculate the difference percentage

def calculate_difference_percentage(actual, average):

return ((actual - average) / average) * 100

differences = {

"upper_body_strength_diff": calculate_difference_percentage(upper_body_strength, avg_values["upper_body_strength"]),

"lower_body_strength_diff": calculate_difference_percentage(lower_body_strength, avg_values["lower_body_strength"]),

"grip_strength_diff": calculate_difference_percentage(grip_strength, avg_values["grip_strength"]),

"reaction_speed_diff": calculate_difference_percentage(reaction_speed, avg_values["reaction_speed"]),

"muscle_mass_diff": calculate_difference_percentage(muscle_mass, avg_values["muscle_mass"]),

}

return differences

# Test data

age = 72

upper_body_strength = 50

lower_body_strength = 55

grip_strength = 27

reaction_speed = 0.65

muscle_mass = 64

result = analyze_elderly_fitness(age, upper_body_strength, lower_body_strength, grip_strength, reaction_speed, muscle_mass)

print(result)

import pandas as pd

# Assume these are the national average values

average_values = {

"age_range": ["65-70", "71-75", "76-80", "81-85", "86+"],

"upper_body_strength": [50, 45, 40, 35, 30], # Upper body strength

"lower_body_strength": [60, 55, 50, 45, 40], # Lower body strength

"grip_strength": [30, 28, 25, 22, 20], # Grip strength

"reaction_speed": [0.5, 0.6, 0.7, 0.8, 0.9], # Reaction speed (seconds)

"muscle_mass": [70, 65, 60, 55, 50] # Muscle mass (kg)

}

# Convert the average values to a DataFrame

average_df = pd.DataFrame(average_values)

def analyze_elderly_fitness(age, upper_body_strength, lower_body_strength, grip_strength, reaction_speed, muscle_mass):

# Determine the age range based on age

if 65 <= age <= 70:

age_range = "65-70"

elif 71 <= age <= 75:

age_range = "71-75"

elif 76 <= age <= 80:

age_range = "76-80"

elif 81 <= age <= 85:

age_range = "81-85"

else:

age_range = "86+"

# Find the corresponding average values

avg_values = average_df[average_df['age_range'] == age_range].iloc[0]

# Calculate the difference percentage

def calculate_difference_percentage(actual, average):

return ((actual - average) / average) * 100

differences = {

"upper_body_strength_diff": calculate_difference_percentage(upper_body_strength, avg_values["upper_body_strength"]),

"lower_body_strength_diff": calculate_difference_percentage(lower_body_strength, avg_values["lower_body_strength"]),

"grip_strength_diff": calculate_difference_percentage(grip_strength, avg_values["grip_strength"]),

"reaction_speed_diff": calculate_difference_percentage(reaction_speed, avg_values["reaction_speed"]),

"muscle_mass_diff": calculate_difference_percentage(muscle_mass, avg_values["muscle_mass"]),

}

return differences

# Test data

age = 72

upper_body_strength = 50

lower_body_strength = 55

grip_strength = 27

reaction_speed = 0.65

muscle_mass = 64

result = analyze_elderly_fitness(age, upper_body_strength, lower_body_strength, grip_strength, reaction_speed, muscle_mass)

print(result)

議程 Agenda

Day 1

Aug. 8 (Thu)

永續高齡社會的未來情境設計

Designing a future scenario of sustainable aging society

第一日議程

Day 2

Aug. 9 (Fri)

大學社會責任下的場域人才培育

Talent cultivation under university social responsibility

第二日議程

主要講者 Key Speaker

顏慶全院長
A.P. YEN, Ching-Chiuan

新加坡新加坡國立大學 | NUS, Singapore

饒尹凌副教授
A.P. JAO, Ying-Ling

美國賓州州立大學｜PSU, USA

稻村德州助理教授
Asst. Prof. INAMURA Tokushu

日本九州大學｜Kyushu University, Japan

楊宜青主任
Dr. YANG, Yi-Ching

臺灣成大醫院高齡醫學部｜NCKU Hospital, Taiwan

蔡岡廷部長
Dr. TSAI, Kang-Ting

臺灣奇美醫院老年醫學科｜Chi Mei Hospital, Taiwan

鈴木雅之教授
Prof. SUZUKI Masayuki

日本千葉大學 | Chiba University, Japan

陳正見博士
Dr. Kelvin TAN

新加坡新加坡社科大學 | SUSS, Singapore

山田協太准教授
A.P. YAMADA Kyota

日本筑波大學 | University of Tsukuba , Japan

鄭健雄特聘教授
Distinguished Prof. CHENG, Jen-Son

臺灣國立暨南國際大學 | NCNU, Taiwan

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論壇發表是以何種語言進行呢？

可以只參加其中一天嗎？

是否會提供參加證明？

論壇發表是以何種語言進行呢？

可以只參加其中一天嗎？

是否會提供參加證明？

指導單位︰教育部

主辦單位︰國立成功大學

執行單位︰國立成功大學規劃與設計學院、國立成功大學USR 相伴2026計畫

合辦單位︰國立成功大學人文社會科學中心之社會創新型USR資源中心

聯絡我們

Email: ncku.usr@gmail.com

06-2757575#54159

聯絡我們

Email: ncku.usr@gmail.com

06-2757575#54159

聯絡我們

Email: ncku.usr@gmail.com

06-2757575#54159