高齡社會設計國際論壇
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
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)
關於超高齡社會的設計想像 Design Imaginations for the Super-Aged Society
在這個論壇中，將聚焦於探索超高齡社會所帶來的挑戰和機遇，並專注於大學如何透過創新的設計思維提供解決方案。隨著社會人口結構的轉變，老年人口的比例急速上升，這不僅帶來了對健康護理、生活支持與社會參與的需求，同時也促使我們重新定義教育的價值和目的。
本論壇將邀請學者、設計師、教育工作者及政策制定者共同參與，旨在促進跨領域的對話與合作，激發新的教學方法和學習模式，以滿足這個日益增長的人口群體的多樣化需求。從課堂內容到學習環境的設計，從專業技能到生活技能的培養，本次研討會將深入探討如何通過教育的力量，不僅提高老年人的生活質量，也為社會的可持續發展貢獻智慧和力量。
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)
主要講者 Key Speaker
Four word-class thought leaders will inspire and inform you.
Prof. Suzuki Masayuki
Chiba University, Japan
Prof. Yamada Kyota
University of Tsukuba , Japan
Prof. Suzuki Masayuki
Chiba University, Japan
Prof. Suzuki Masayuki
Chiba University, Japan
Prof. Yamada Kyota
University of Tsukuba , Japan
Prof. Yamada Kyota
University of Tsukuba , Japan
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可以只參加其中一天嗎？
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