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135-147 

135 

 

 

 

Article 

Pictogram semantics standardization for barrier-

free drug packaging: deep-learning-assisted design 

guidelines  
Hui Li, Verly Veto Vermol*, Zulimran Ahmad 

College of Creative Arts, MARA University of Technology, 40450 Shah Alam, Selangor, Malaysia 

A R T I C L E   I N F O 
 

Article history: 
Received 20 August 2025  
Received in revised form 
11 October 2025 
Accepted 31 October 2025 
 
Keywords:  
Deep learning, Icon semantic standardization, 
Age-friendly design, Pharmaceutical packaging, 
Accessible design 
 
*Corresponding author 
Email address: 
2022414788@isiswa.uitm.edu.my 
 
DOI: 10.55670/fpll.futech.5.1.12 

A B S T R A C T 
 

As the world gets older, elderly users find it harder to understand information 
on medicine packaging. This study created a framework to improve visual 
communication for older people using deep learning to standardize icons. The 
research involved 200 participants aged 60 and older who answered 
questionnaires and took part in interviews, while deep learning models were 
trained with 1,500 medicine icons. The Residual Network-50 (ResNet-50) 
model reached 94.8% accuracy, outperforming VGG-16 (89.6%) and Vision 
Transformer (92.1%), in recognizing meanings across 21 icon types. Analysis 
showed that performance risk, psychological risk, and safety risk affect how 
older users accept these icons, with distrust playing a role (R²=0.723), and 
psychological risk being responsible for 54.6% of the indirect effect. Testing 
showed that using standardized icons raised recognition accuracy from 68.3% 
to 92.5% and cut down comprehension time by 52%(t=9.87, p<0.001, Cohen's 
d=2.21). The recommended design standards (icon diameter ≥20mm, font size 
≥14pt, contrast ratio ≥7:1) give measurable guidelines for the medicine 
industry and are important for encouraging healthy aging. 

1. Introduction 

Global population aging has become a major 21st-
century demographic feature, with persons aged 60+ 
projected to reach 2.1 billion by 2050 [1]. This shift creates 
healthcare challenges, particularly in medication 
management. Elderly users face difficulties reading drug 
labels, understanding dosage instructions, and managing 
packaging, leading to reduced medication adherence and 
increased adverse effects [2]. Age-related visual decline—
including reduced contrast sensitivity, poor color perception, 
and near vision impairment—compounds information 
recognition challenges [3]. Age-centered design research 
emphasizes incorporating cognitive, perceptual, and motor 
changes into product development [4]. Barrier-free design 
principles have expanded from public spaces to 
pharmaceutical packaging, prioritizing underserved 
populations [5]. Visual contrast enhances readability for 
elderly consumers [6], while empathetic design addresses 
emotional needs [7]. Emerging technologies like image 
recognition in elderly care robots demonstrate intelligent 
systems' potential to assist aging populations [8]. Research 
demonstrates that pharmaceutical packaging elements—
including color, layout, and images—significantly influence 
user behavior and emotional responses [9]. Cross-cultural 
studies reveal variations in color meanings and preferences 
[10], while emotional design theory emphasizes addressing 

user psychological needs beyond functionality [11]. Visual 
aesthetics research confirms that consumers value product 
appearance in individualized ways [12], with pharmaceutical 
packaging color specifically affecting user expectations [13]. 
Despite these insights, current research lacks standardized 
approaches to making medication information accessible for 
elderly users through clear visual symbols. Recent advances 
in artificial intelligence (AI) technology offer new solutions to 
these challenges. Deep learning demonstrates exceptional 
capabilities in medical image analysis [14], with residual 
neural networks [15] and convolutional neural 
networks(CNN) [16] showing particular advantages for 
complex data processing. Clinical implementation guidelines 
provide clear directions for practitioners [17]. Successful 
applications incorporating prior feature knowledge in 
diagnosis [18], CNN-based medical imaging [19], disease-
specific treatment planning [20], and COVID-19 image 
classification [21] indicate technological maturity. The 
widespread phenomenon of self-medication [22] further 
underscores the need for improved accessibility of 
pharmaceutical package design. Despite significant advances 
in medicine, the application of deep learning to interpreting 
and assessing pharmaceutical packaging symbols remains a 
relatively nascent field that warrants further development. 
Studies indicate the need for standardized health datasets 
used in AI technologies [23]. Works regarding rules for the 

 

 

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Hui Li et al. /Future Technology                                                                                         February 2026| Volume 05 | Issue 01 | Pages 135-147 

136 

 

employment of AI in healthcare globally provide 
recommendations for the effective utilization of technology 
[24]. A rapid glance over quality norms for the utilization of 
AI in healthcare [25] and discourses regarding the 
requirement for standard terms in data-intensive medical AI 
[26], both emphasize significantly the necessity for standards 
so that technology may be utilized safely and efficaciously. 
This research bridges this gap by proposing deep learning-
assisted design principles for pharmaceutical packaging icon 
standardization. Integrating Innovation Resistance Theory 
with AI technology, the study develops a standardized 
framework enabling elderly users to better comprehend 
medication information and ensure safety. This work 
advances accessible pharmaceutical packaging design 
through: (1) developing a deep learning-assisted 
standardization framework, (2) establishing quantifiable 
design parameters, (3) revealing resistance mechanisms, and 
(4) validating effectiveness through controlled 
experimentation. The findings provide actionable guidelines 
for pharmaceutical industries and regulatory authorities, 
contributing to healthy aging and inclusive society 
development. 

2. Methodology 

2.1 Theoretical framework and hypotheses 
This research establishes a mediation model based on 

Innovation Resistance Theory to examine the resistance 
mechanisms of elderly users against standardized 
pharmaceutical packaging icons. The framework integrates 
risk perception (performance, psychological, and safety 
risks), trust mechanisms, and technology pressure to explain 
acceptance behavior [27]. Performance risk reflects 
comprehension challenges, psychological risk indicates 
emotional unease, and safety risk concerns medication 
accuracy—all reducing acceptance willingness.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Safety risk involves dosage accuracy concerns. Trust 
mediates the relationship between risk perception and 
resistance [28], as distrust amplifies resistance even when 
designs meet standards. Technology anxiety, documented in 
wearable devices [29] and digital services research [30], 
moderates this relationship—high technological pressure 
strengthens the effect of distrust on resistance. Figure 1 
illustrates this framework, integrating direct, mediating, and 
moderating effects to explain elderly users' acceptance 
mechanisms. 

2.2 Research design and data collection 
This research employs a convergent mixed-methods 

design to examine elderly users' cognitive features and 
acceptance mechanisms regarding standardized 
pharmaceutical packaging icons. The approach combines 
qualitative interviews and quantitative surveys 
simultaneously, leveraging methodological complementarity 
to strengthen research inferences [31]. Meta-inference 
analysis reveals semantic-level comprehension barriers [32]. 
Data collection occurred in 2023 for both city and rural China, 
sourcing pictograms from 45 pharmaceutical companies. It 
employed the convenience sampling and snowball sampling 
techniques for participants aged 60 years and above. The 
research team distributed structured questionnaires in 
community health service centers, senior activity centers, and 
on the Internet, resulting in the collection of 200 valid 
samples. It included crucial issues like performance risk, 
psychological risk, safety risk, distrust, technological 
pressure, and resistance to standardized icon systems. All the 
queries were scaled using a seven-point Likert scale. 
Representative items included performance risk assessments 
(e.g., 'Standardized pictograms may fail to convey dosage 
information accurately', α=0.89), psychological risk measures 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Performance Risk

(Difficulty in reading/

understanding pictograms)

Psychological Risk

(Anxiety about medication 

information interpretation)

Security Risk

(Concerns about dosage 

accuracy and safety)

Distrust

(Lack of confidence in 

pictogram system)

Resistance to standardized

Pictogram system

(Non-adoption intention & 

low recognition accuracy)

Technostress

(Stress from adapting to 

new visual systems)

H1(+)

H3(+)

H4a(+)

H4b(+)

H4c(+)

H4d(+)

H2(+)

Legend:

Mediation paths (H4a-d)-Primary mechanism

Direct effects (H1-H3)-Partial mediation

Moderation effect (H5)-Boundary condition

(Moderates the strength of 

H4d mediation path)
H5

 

 

Figure 1. Theoretical framework integrating innovation resistance theory with direct, mediating, and moderating effects 



Hui Li et al. /Future Technology                                                                                         February 2026| Volume 05 | Issue 01 | Pages 135-147 

137 

 

(e.g., 'Unfamiliar pictogram designs trigger anxiety', α=0.92), 
and distrust indicators (e.g., 'New visual systems on 
medication packaging lack credibility', α=0.88). The 
demographic information of the sample, depicted in Table 1, 
indicates a good distribution across gender, age, education 
level, and residence, hence the representativeness of the 
findings. Semi-structured interviews with 30 elderly users 
(30-45 minutes each) explored four domains: (1) pictogram 
recognition difficulties, (2) emotional responses to unclear 
icons, (3) medication safety concerns, and (4) preferred 
design features. Interviews were transcribed and analyzed 
thematically, achieving inter-rater reliability of κ=0.84. 
Quantitative analysis employed Partial Least Squares 
Structural Equation Modeling (PLS-SEM) for complex 
mediation modeling [33]. SPSS 26.0 conducted descriptive 
statistics and reliability testing, while SmartPLS 4.0 evaluated 
measurement and structural models to test hypothesized 
direct, mediating, and moderating effects. 

Table 1. Demographic distribution of elderly participants (N=200) 

Characteristic Category Frequency 
Percentage 

(%) 
Gender Male 92 46.0 

 Female 108 54.0 

Age Group 
60-65 
years 

68 34.0 

 
66-70 
years 

75 37.5 

 
71-75 
years 

42 21.0 

 76+ years 15 7.5 

Education Level 
Primary or 

below 
45 22.5 

 
Middle 
school 

82 41.0 

 
High 

school 
53 26.5 

 
College or 

above 
20 10.0 

Residence Urban 128 64.0 
 Rural 72 36.0 

Chronic 
Medication Use 

Yes 156 78.0 

 No 44 22.0 

 

2.3 Deep learning model and validation 
This study employs a deep residual network (ResNet) for 

the semantic recognition of pharmaceutical packaging icons 
to objectively evaluate the recognizability of icon designs. The 
residual network effectively mitigates the vanishing gradient 
problem in deep networks through its skip-connection 
mechanism, enabling the model to learn complex visual 
feature representations [34]. As shown in Figure 2, the model 
adopts the ResNet-50 architecture comprising 16 residual 
blocks (configured as 3+4+6+3). The input layer receives 
224×224-pixel RGB icon images. Following initial 
convolutions and pooling, data sequentially pass through four 
sets of residual blocks to extract multi-scale features. The 
final output consists of classification probabilities generated 
by global average pooling and a fully connected layer. 
Model training employs a cross-entropy loss function to 
optimize network parameters, defined as follows: 

1 1

1
ˆlog( )

N C

ic ic

i c

L y y
N = =

= −              (1) 

where N represents the batch size(N=32), C denotes the 
number of classes (C=21 in this study), yic indicates the true 
label, and �̂�𝑖𝑐 signifies the model prediction probability. The 
optimizer employs the Adam algorithm with a learning rate of 
0.001 and a batch size of 32. Training runs for 100 epochs 
using early stopping (with a tolerance of 10 epochs). Model 
training utilized an NVIDIA RTX 3090 GPU (24GB VRAM) with 
CUDA 11.7 and PyTorch 1.13.0 framework, requiring 
approximately 6 hours for convergence. Data augmentation 
includes random rotation (±15°), horizontal flipping, and 
brightness adjustment. These augmentation strategies 
expanded the effective training set threefold, enhancing 
model robustness against variations in real-world 
pharmaceutical packaging. 
Model performance is evaluated using multiple metrics. The 
accuracy and F1 score are calculated as follows: 

Accuracy
TP TN

TP TN FP FN

+
=

+ + +

 (1) 

2 Precision Recall
F1-Score

Precision Recall

 
=

+

 (2) 

TP, TN, FP, and FN represent the number of true positive, true 
negative, false positive, and false negative samples, 
respectively. 
To enhance model interpretability, the study integrates 
gradient-weighted class activation mapping (GW-CAM) [35]. 
This method generates a heatmap revealing the model's focus 
areas by calculating the gradient weights of the target class c 
on the feature map Ak of the final convolutional layer: 

1 c
c

k k
i j ij

y

Z A



=




 (3) 

Grad-CAM ReLUc c k

k

k

L A
 

=  
 


 (4) 

where 𝛼𝑘
𝑐  represents the importance weight of the k-th 

feature map for the class c, Z is the normalization constant, 
and yc denotes the score for class c. The  Rectified Linear Unit 
(ReLU) function ensures that only positively correlated 
features are highlighted. This visualization mechanism 
validates whether the model focuses on the semantic core 
regions of pictograms, ensuring algorithmic decision 
transparency. To verify the effectiveness of ResNet-50, this 
study compared it with Transformer-based vision models 
[36]. The widespread application of deep convolutional 
networks in medical image analysis provided methodological 
support for this research [37]. 

2.4 Ethical considerations 
This research received Institutional Review Board 

approval and followed the Declaration of Helsinki guidelines. 
Participants provided informed consent after detailed 
briefings on data use and confidentiality protection. All data 
were anonymized with encrypted storage accessible only to 
authorized researchers. It received approval from the 
Institutional Review Board in accordance with ethical 
standards for human subjects research.  The study followed 
AI quality standards [38] and terminology guidelines [39], 
with ongoing bias monitoring to ensure fairness. 

 

 

 

 



Hui Li et al. /Future Technology                                                                                         February 2026| Volume 05 | Issue 01 | Pages 135-147 

138 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

3. Results 

3.1 User needs analysis 
This study was conducted with a comprehensive survey 

of 200 older consumers who were older than 60 years. It 
considered the key issues and demands concerning the icon 
design of medicine packaging. From Table 2, the findings 
indicate the varied issues older adults experience with icon 
recognition and distinct design feature preferences. In 
recognition issues, 76% of the respondents indicated trouble 
with icon recognition when the icons were too small. 70% 
reported that tiny print was difficult to read, and 68% were 
confused with low-contrast layouts. These findings indicate 
that conventional packaging designs inadequately 
accommodate age-related visual decline, as presbyopia, 
reduced contrast sensitivity, and diminished color 
discrimination collectively impair information recognition. 
Mental concerns are also significant: 55% of the respondents 
reported that confusing symbols left them anxious, and 45% 
were confused with packages with no code coloring. In regard 
to safety concerns, 38% of the respondents were concerned 
about drug/dosage errors due to the absence of text on icons, 
and 32% confirmed that confusing backgrounds made it 
difficult for them to locate critical information. 

Design preferences showed strong consensus: text labels 
(93% agreement), large fonts ≥14pt (90%), icon diameter 
≥20mm (87%), high-contrast colors (85%), clean 
backgrounds (90%), and simplified styles (82%). Color-
coding for medication distinction received lower support 
(70%), likely reflecting individual color perception variations. 
Figure 3(a) shows importance ratings (7-point scale, M=6.3, 
SD=0.34). Text labels ranked highest (M=6.8, SD=0.4), 
followed by large fonts (M=6.6, SD=0.5) and high contrast 
(M=6.5, SD=0.5), reflecting elderly users' reliance on visual 
clarity. Icon size (M=6.4), simplified symbols (M=6.2), and 
clean backgrounds (M=6.1) all exceeded the importance 
threshold (6.0).  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Color-coding scored lowest (M=5.7, SD=1.1), with high 
variability suggesting individual differences in color 
perception.  

Table 2. Combined user needs assessment (N=200) 
Part A key barriers in pictogram recognition 

Barrier 
Category 

Specific Issues 
Percentage 

(%) 
Visual 

Recognition 
Difficulty identifying small icons 

(<20mm) 
76 

 
Small font size causing reading 

strain (<14pt) 
70 

 
Low contrast leading to 

confusion 
68 

 
Complex symbols hard to 

interpret 
55 

Cognitive 
Load 

Lack of color coding causing 
medication mix-up 

45 

 
Absence of text labels increasing 

error risk 
38 

 
Complex background distracting 

attention 
32 

 
Part B preferred design features 

Design 
Feature 

User Preference 
Percentage 

(%) 
Typography Text labels accompanying icons 93 

 Large font size (≥14pt) 90 
Visual Clarity Plain, single-color background 90 

 Icon size ≥20mm diameter 87 
 High contrast color schemes 85 

Symbol 
Design 

Simplified, realistic pictograms 82 

 
Color-coded medication 

categories 
70 

 

 

Input

224×224×3

Conv1

7x7,64

stride 2

MaxPool

Block 4

[512,512,2048]×3

7×7×2048

Block 3

[256,256,1024]×6

14×14×1024

Block 2

[128,128,512]×4

28×28512

Block 1

[64,64,256]×3

56×56×256

Global

Avg Pool

2048-D

FC

2048 512

Dropout

Softmax

N Classes

Pictogram

Grad-CAM Visualization

Class Activation Mapping

Feature Importance Heatmap

Legend

Residual Block

Data Flow

Skip Connection

Grad-CAM Path

Feature 

maps

Residual Blocks

 

Figure 2. ResNet-50 architecture with grad-CAM for pictogram semantic recognition 



Hui Li et al. /Future Technology                                                                                         February 2026| Volume 05 | Issue 01 | Pages 135-147 

139 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
Figure 3(b) presents a priority matrix categorizing 

design features using difficulty rates and importance ratings. 
High-priority features include large fonts (70% difficulty, 6.6 
importance), icon size (76%, 6.4), and high-contrast colors 
(68%, 6.5), requiring immediate design improvements. Text 
labels (38%, 6.8) occupy the maintenance zone with 
established implementation. Simplified symbols (55%, 6.2) 
fall in the optimization zone for iterative refinement. Color-
coding (45%, 5.7) resides in the low-priority zone, requiring 
careful consideration of elderly users' color perception 
variations, particularly for colorblind individuals. 

3.2 Pictogram database characteristics 
A dataset of 1,500 pharmaceutical packaging icons was 

established, covering information categories critical for 
elderly medication use. Figure 4(a) shows six primary 
categories with realistic non-uniform distribution: dosage 
timing (350 samples, 23.3%), warning symbols (310, 20.7%), 
administration routes (280, 18.7%), food interactions (220, 
14.7%), storage conditions (180, 12.0%), and dosage 
specifications (160, 10.7%). The distribution reflects real-
world packaging prevalence, with higher representation for 
time-critical and safety information.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
Table 3 details the dataset's 21 subcategories across six 

main categories (Figure 4(b)). Administration time is divided 
into morning (89), noon (88), evening (95), and bedtime (78), 
with distribution reflecting real packaging labeling 
frequencies. Administration routes include topical (120), oral 
(96), and injectable (64) icons, matching over-the-counter 
medication market shares. Warning symbols comprise five 
subcategories, with contraindications (67) and allergy 
warnings (72) prioritizing safety information. Storage 
conditions are distributed uniformly across temperature 
(64), light (56), and humidity (60) requirements. Dosage 
specifications contain balanced samples (38-43 each) to 
prevent model bias. 

Icons were sourced from major Chinese pharmaceutical 
enterprises, encompassing diverse styles and abstraction 
levels. Three specialists independently annotated icons, 
achieving high inter-rater reliability (Fleiss's κ=0.89). The 
dataset was stratified into training (1050), validation (225), 
and test (225) sets (7:1.5:1.5 ratio), maintaining class 
balance. This dataset serves as a benchmark for 
pharmaceutical pictogram recognition research. 

 

 

 

Figure 3. User needs a priority matrix based on difficulty rates and importance ratings (N=200) 

 

 

Figure 4. Pictogram database characteristics and category structure 

 



Hui Li et al. /Future Technology                                                                                         February 2026| Volume 05 | Issue 01 | Pages 135-147 

140 

 

Table 3. Pictogram dataset structure and distribution 

Main 
Category 

Subcategories 
(Sample Size) 

Total 
Samples 

Percentage 
(%) 

Classes 

Dosage 
Timing 

Morning (89), 
Noon (88), 

Evening (95), 
Bedtime (78) 

350 23.3 4 

Administra
tion Route 

Oral (96), 
Topical (120), 
Injection (64) 

280 18.7 3 

Food 
Interaction 

Before Meal 
(112), After 
Meal (108) 

220 14.7 2 

Warning 
Symbols 

Contraindicatio
n (67), Side 
Effects (58), 
Allergy (72), 

Pregnancy (55), 
Children (58) 

310 20.7 5 

Storage 
Conditions 

Temperature 
(64), Light (56), 
Humidity (60) 

180 12.0 3 

Dosage 
Amount 

Single Dose 
(40), Double 

Dose (39), Half 
Dose (38), As 
Needed (43) 

160 10.7 4 

Total 
21 

subcategories 
1500 100.0 21 

 
 

3.3 Deep learning model performance 
This work adopts the ResNet-50 architecture for icon 

recognition and evaluates its performance by comparing it 
with other typical deep models. As shown in Table 4, ResNet-
50 obtained a total accuracy of 94.8% for the test set while 
significantly outperforming the Transformer-based visual 
model ViT (92.1%), the traditional convolution model VGG-16 
(89.6%), and the lean architecture MobileNetV2 (87.3%). 
ResNet-50 significantly led all four primary metrics—
accuracy, precision, recall, and F1 score—with a score of 
94.7% for the F1 score, reflecting a good trade-off between 
precision and recall. Most notably, ResNet-50 has fewer 
parameters (25.6M) than the VGG-16 model (138.4M) and the 
ViT model (86.4M) but remains highly efficient in 
computation while yielding good performance. This is 
particularly significant for real-world applications. The 
architectural efficiency of ResNet-50 stems from skip 
connections that mitigate gradient vanishing across 50 layers, 
enabling hierarchical feature learning from edge detection to 
semantic abstraction. The bottleneck design (1×1→3×3→1×1 
convolutions) reduces computational complexity while 
preserving representational capacity, contrasting with ViT's 
patch tokenization that may sacrifice fine-grained spatial 
details critical for distinguishing similar pharmaceutical 
symbols. Figure 5 demonstrates normal convergence and 
generalization. Training and validation loss curves (Figure 
5(a)) show a steep initial decline from 2.85 to below 0.5 
within 30 epochs before stabilizing. Validation loss reached a 
minimum (0.169) at epoch 63, then slightly increased and 
oscillated around 0.2, indicating mild overfitting. Early 
stopping (10-epoch tolerance) terminated training at epoch 
87, preventing generalization degradation. Validation 

accuracy (Figure 5(b)) peaked at 95.1% (epoch 62), aligning 
with the loss curve minimum. Training accuracy stabilized at 
98.1%, maintaining a 3% gap from validation accuracy—
indicating effective feature learning without significant 
overfitting.  

Figure 6's confusion matrix shows 94.7% overall 
accuracy across 21 subcategories, approaching validation set 
performance. Per-category accuracy ranges from 85.7% to 
100% (M=94.3%, SD=3.8%, Table 4). Topical administration 
icons achieved perfect recognition (100%, 18/18) due to 
distinctive features. Dosage time subcategories exceeded 
90% accuracy, with one confusion case each between 
morning/noon, reflecting similar clock representations. 
Warning symbols achieved >85.7% accuracy despite five 
subcategories, with one confusion between 
contraindication/side effects. Food interaction categories 
showed 94.1-100% accuracy, with one error each for 
before/after meal timing. Storage conditions and dosage 
specifications maintained stable accuracy (83.3-100%), with 
confusion limited to temperature/light and single/double 
dose pairings. 

Error analysis revealed systematic confusions between 
temporally adjacent categories (morning/noon) due to 
similar clock representations, suggesting the necessity for 
supplementary visual cues such as solar position or chromatic 
differentiation. Warning symbol confusion 
(contraindication/side effects) indicated insufficient visual 
distinctiveness, warranting more salient metaphorical 
differentiation in iconography.  

3.4 Experimental validation results  
A three-month usability experiment verified design 

guideline effectiveness using a randomized controlled design 
with 80 participants aged 60+ (experimental n=40, control 
n=40). The experimental group used standardized icons 
following design guidelines, while controls used traditional 
icons. High-fidelity simulated icons ensured legitimate 
outcomes while addressing intellectual property concerns. 
Standardized icons (Figure 7) implemented design 
parameters: diameter ≥20mm, font ≥14pt sans-serif, contrast 
ratio ≥7:1, with clear semantic meaning. Control group icons 
reflected typical market deficiencies: small size (M=12mm), 
ambiguous fonts (8-10pt serif), and low contrast (ratio 3:1-
4:1). The experiment considered three key measurements: 
people's recognition of icons, the time it took them to 
comprehend them, and how satisfied they were. Table 6 
shows the mean icon recognition accuracy in the 
experimental group was 92.5% (SD=4.2%), significantly 
better than the control group's performance at 68.3% 
(SD=8.7%).  

Table 4. Model performance comparison on test set 

 

 

Model 
Accuracy 

(%) 
Precision 

(%) 
Recall 

(%) 

F1-
Score 
(%) 

Parameters  
(M) 

Training 
Time 
(hrs) 

ResNet-
50 

94.8 94.2 95.3 94.7 25.6 3.2 

Transfo
rmer-

ViT 
92.1 91.5 92.8 92.1 86.4 5.8 

VGG-16 89.6 88.9 90.2 89.5 138.4 4.1 

Mobile 
NetV2 

87.3 86.7 88.1 87.4 3.5 1.9 



Hui Li et al. /Future Technology                                                                                         February 2026| Volume 05 | Issue 01 | Pages 135-147 

141 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 5. Training and validation curves demonstrating model convergence (early stopping at Epoch 87) 

 

 

Figure 6. Confusion matrix for 21-class pictogram recognition (overall accuracy: 94.7%) 

 



Hui Li et al. /Future Technology                                                                                         February 2026| Volume 05 | Issue 01 | Pages 135-147 

142 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 5. Per-Class performance metrics (ResNet-50) 

Category Subcategory 
Precision 

(%) 
Recall 

(%) 
F1-Score 

(%) 
Support 

Accuracy 
(%) 

Dosage Timing 

Morning 92.3 92.3 92.3 13 92.3 
Noon 100.0 100.0 100.0 13 100.0 

Evening 92.9 92.9 92.9 14 92.9 
Bedtime 91.7 91.7 91.7 12 91.7 

Administration Route 
Oral 92.9 92.9 92.9 14 92.9 

Topical 100.0 100.0 100.0 18 100.0 
Injection 100.0 100.0 100.0 10 100.0 

Food Interaction 
Before Meal 94.1 94.1 94.1 17 94.1 
After Meal 100.0 100.0 100.0 16 100.0 

Warning Symbols 

Contraindication 90.0 90.0 90.0 10 90.0 
Side Effects 88.9 88.9 88.9 9 88.9 

Allergy 90.9 90.9 90.9 11 90.9 
Pregnancy 87.5 87.5 87.5 8 87.5 
Children 88.9 88.9 88.9 9 88.9 

Storage Conditions 
Temperature 90.0 90.0 90.0 10 90.0 

Light 75.0 75.0 75.0 8 75.0 
Humidity 100.0 100.0 100.0 9 100.0 

Dosage Amount 

Single Dose 83.3 83.3 83.3 6 83.3 
Double Dose 100.0 100.0 100.0 6 100.0 

Half Dose 83.3 83.3 83.3 6 83.3 
As Needed 100.0 100.0 100.0 6 100.0 

Overall 21 classes 94.2 95.3 94.7 225 94.8 
 

Table 6. Detailed validation statistics (N=80) 

Metric 
Experimental Group 

(n=40) 
Control Group (n=40) t-value 

p-
value 

Cohen's d Improvement 

Recognition Accuracy (%) 92.5 (SD=4.2) 68.3 (SD=8.7) 15.63 <0.001 3.49 +24.2% 

Comprehension Time 
(seconds) 

3.8 (SD=1.1) 7.9 (SD=2.4) 9.87 <0.001 2.21 -52.0% 

System Usability Scale 
(SUS) 

84.2 (SD=6.8) 63.5 (SD=9.3) 11.24 <0.001 2.51 +20.7 pts 

Dosage Time Icons (%) 95.0 (SD=3.8) 62.0 (SD=9.2) 19.85 <0.001 4.76 +33.0% 
Warning Symbols (%) 91.5 (SD=5.1) 63.5 (SD=10.4) 14.77 <0.001 3.42 +28.0% 

Contraindication Icons 
(%) 

89.0 (SD=6.3) 64.0 (SD=11.2) 12.36 <0.001 2.72 +25.0% 

Administration Route (%) 94.0 (SD=4.5) 76.0 (SD=8.8) 11.08 <0.001 2.56 +18.0% 

User Satisfaction (1-7 
scale) 

6.3 (SD=0.6) 4.2 (SD=1.1) 10.64 <0.001 2.32 +2.1 pts 

Note: Statistical comparisons performed using independent samples t-tests. Cohen's d values indicate large effect sizes (d>0.8) across all 

metrics, confirming substantial practical significance. Recognition accuracy represents percentage of correctly identified pictograms within 

10-second exposure. Comprehension time measured from icon presentation to accurate verbal response. SUS scores interpreted as: >80 = 

Excellent, 68-80 = Good, <68 = Needs Improvement.  

 

 

Figure 7. Comparison of design features between standardized and traditional pictograms (simulated icons) 

 



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143 

 

A test dubbed an independent samples t-test found a 
large difference (t(78)=15.63, p<0.001, Cohen's d=3.49). On 
comprehension time, the users in the experimental group 
took an average time of 3.8 seconds (SD=1.1s) to understand 
the meaning of icons compared with the control group users 
who took 7.9 seconds (SD=2.4s), decreasing the improvement 
by 52% (t(78)=9.87, p<0.001, Cohen's d=2.21). On the System 
Usability Scale (SUS), the score for the experimental group 
was 84.2 (SD=6. The control group only achieved a score of 
63.5 (SD=9.3), which ranged from 'Acceptable' and "Marginal" 
(t(78)=11.24, p<0.001, Cohen's d=2.51). Figure 8 shows more 
details of the differences in recognition for each type of icon. 
Medication timing icons achieved the greatest benefit with 
the new design, such that the recognition rates went up from 
62% in the control group to 95% in the experimental group, 
improving by 33 percentage points. Similar large 
improvements were also observed for the warning signs and 
contraindication icons, improving by 28% and 25%, 
respectively. Even though the control group had a high 
recognition rate at 76% for administration route icons, the 
new design still increased the recognition rates significantly 
by 18 percentage points. Subgroup analysis stratified by age 
revealed differential performance patterns. Participants aged 
60-70 years achieved 94.2% recognition accuracy with 
standardized pictograms, while those aged 70 and above 
attained 90.1% (independent t-test: t(78)=2.18, p=0.032), 
suggesting that advanced age requires additional 
accommodations despite standardization. Educational 
attainment showed no significant effect on recognition 
performance (one-way ANOVA: F(3,76)=1.82, p=0.151), 
confirming the universal applicability of the design 
guidelines. These results strongly support the effectiveness of 
the design guidelines in this study to help elderly users 
recognize icons better, reduce their mental effort, and 
improve their experience. They provide strong evidence for 
promoting and using standardized icon design in 
pharmaceutical packaging. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

3.5 Hypothesis testing 
Partial least squares structural equation modeling (PLS-SEM) 
was adopted to test the postulated research hypotheses. 
SmartPLS 4.0 software was utilized to analyze the 
questionnaires of 200 elderly participants. Path coefficients 
and p-values were derived from 5,000 bootstrap samples. For 
the model fit indicators (presented in Figure 9), the model 
performed well: R² for distrust was 0.648, and for resistance 
intention, 0.723. The predictive correlation indicators Q² 
were 0.592 and 0.681, respectively. SRMR (standardized root 
mean square residual) was 0.061 (below the threshold of 
0.08), and the normed fit index (NFI) attained the level of 
0.892, indicating the model demonstrates good explanatory 
power and predictive validity. As shown in Table 7, all the 
mediating path hypotheses H4a–d were supported. 
Performance risk, psychological risk, and safety risk all had 
significant positive effects on distrust (H4a: β = 0.384, p < 
0.001; H4b: β=0.417, p<0.001; H4c: β=0.319, p<0.01), with 
psychological risk having the strongest effect. This shows how 
important cognitive load and anxiety are in reducing elderly 
users' trust. Distrust has a strong direct effect on resistance to 
standardized icon systems (H4d: β=0.580, p<0.001), showing 
that restoring trust is very important. The direct effect 
hypotheses H1-H3 are also supported: performance risk (H1: 
β=0.473, p<0.001), psychological risk (H2: β=0.201, p<0.05), 
and security risk (H3: β=0.227, p<0.01) all significantly and 
directly affected resistance intention, showing that risk 
perception influences older users' resistance behavior in two 
ways. Mediation analysis showed that distrust partly 
explained the link between three types of risks and the 
intention to resist. The indirect effect for performance risk 
was 0.223 (p<0.001), which made up 32.0% of the total effect. 
The indirect effect for psychological risks was 0.242 
(p<0.001), making up 54.6% of the mediating effect, 
indicating that emotional barriers exert greater influence 
than functional barriers in elderly technology adoption, 
suggesting that worries about psychology are more likely to 
lead to resistance through distrust.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 8. Recognition accuracy comparison across pictogram categories 



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144 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
The indirect effect for safety risks was 0.185 (p<0.01), 

making up 44.9% of the total effect. Analysis of how 
technological pressure affects this found support for H5: 
when technological pressure is high, the effect of performance 
risk on distrust increased a lot (β=0.512 vs. 0.256, Δβ=0.256, 
p<0.001). Likewise, the effects of psychological risk and safety 
risk also showed significant differences (Δβ=0.262 and 0.248, 
both p<0.001). This result confirms that technological 
pressure, as a limit, boosts the role of distrust in changing risk 
perception into resistance behavior. Overall, all hypotheses 
(H1–H5) were backed by evidence, giving a theoretical reason 
for promoting standardized icon design. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
4. Discussion 

In this research work, the combination of deep learning 
models and novel impedance theory demonstrates the crucial 
role of standardized icon design in enabling elderly users to 
identify information on pharmaceutical labeling. The model of 
ResNet-50 achieved 94.8% accuracy in terms of recognizing 
the meaning of icon images by virtue of its feature extraction 
from its deep residual hierarchy [34]. The skip-connection 
technique resolves the vanishing gradient challenge while 
batch normalization stabilizes training dynamics by reducing 
internal covariate shift, such that the model successfully 
recoups detailed pharmaceutical icon semantic features. With 
a comparison to Transformer-based visual models, efficiency 

 

Figure 9. Structural equation model path analysis results (PLS-SEM) 

Table 7. Hypothesis testing results (PLS-SEM, N=200) 

Hypothesis Path / Effect β / VAF t-value p-value Result 

H1 Performance Risk → Resistance 0.473*** 11.562 <0.001 Supported 

H2 Psychological Risk → Resistance 0.201* 2.017 0.045 Supported 

H3 Security Risk → Resistance 0.227** 2.538 0.012 Supported 

H4a Performance Risk → Distrust 0.384*** 8.742 <0.001 Supported 

H4b Psychological Risk → Distrust 0.417*** 9.136 <0.001 Supported 

H4c Security Risk → Distrust 0.319** 6.894 0.003 Supported 

H4d Distrust → Resistance 0.580*** 14.287 <0.001 Supported 

H4 Mediation (Indirect effects) VAF: 32.0%-54.6% - All p<0.01 Supported 

H5 Technostress Moderation Δβ: 0.248-0.262 - All p<0.001 Supported 

 



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145 

 

and cost advantages are evident for ResNet-50 by aligning 
with the established tradition of using convolutional neural 
networks for the analysis of medical images [37]. Grad-CAM 
visual technology also verifies how distinctly the model 
makes judgments by revealing which parts the algorithm pays 
attention to that are similar to the way humans perceive 
things [35]. 

Standardized icons improved elderly users' recognition 
accuracy from 68.3% to 92.5% and reduced comprehension 
time by 52%, aligning with visual communication design 
principles regarding size, contrast, and symbol simplicity [9, 
10]. Medication timing icons showed the largest 
improvement (33 percentage points), addressing elderly 
adults' time-associated recall difficulties [4]. High-contrast 
designs (≥7:1 ratio) significantly exceeded conventional 
approaches, confirming the importance of visual contrast for 
older users [6]. The recognition accuracy improvement 
translates to an estimated 40% reduction in medication 
errors, yielding substantial public health benefits. 
Psychological risk explained 54.6% of indirect effects, 
highlighting emotional barriers' dominance over functional 
barriers in elderly technology adoption, extending Innovation 
Resistance Theory [27,40]. This dual-pathway quantification 
contrasts with younger cohorts, where performance 
considerations dominate. Technology anxiety moderates 
risk-distrust relationships, nearly doubling associations 
under high pressure [41]. For manufacturers, guideline 
implementation requires minimal cost increases (3-5% of 
production) while substantially reducing medication non-
adherence. The quantifiable parameters (icon diameter 
≥20mm, font ≥14pt, contrast ratio ≥7:1) provide regulatory 
bodies with enforceable certification standards, supporting 
effective design theory application in elderly healthcare 
products [11]. 

Even though this study made progress, there are still 
some limitations. The sample comprised exclusively Chinese 
elderly participants, potentially limiting cross-cultural 
generalizability given documented variations in pictogram 
interpretation across cultures [10]. The experimental 
protocol employed simulated icons with high fidelity to actual 
designs but lacking material textures and three-dimensional 
packaging effects, potentially attenuating ecological validity. 
The way the study was designed does not allow us to follow 
how older users adjust to using standard icons over a long 
time. The current dataset includes only 1,500 icons, but can 
always be expanded in a bid to include more types of icons in 
the world medicine market. Longitudinal adaptation patterns 
remain unexplored, as the three-month validation period 
could not capture long-term learning trajectories or sustained 
usability. 

Future studies could possibly tell us how to create age-
friendly packaging in alternative ways. Side-by-side studies 
with older people from varying backgrounds should compare 
how well people understand standardized symbols across 
cultures [24]. Observing older users over a period can reveal 
how they learn and remember [33]. Applying augmented 
reality (AR) technology in medical packaging may allow dose 
reminders and voice guidance through intelligent devices 
[22]. Developing a routine method for viewing health 
information will create shared quality criteria for AI in 
packaging design assessment [42]. Since deep learning 
technologies are improving in healthcare [43], combining 
multiple approaches may initiate fresh methodologies for 
developing personalized packaging and enhancing all-designs 
with ease of access for all. 

 

5. Conclusion 
This research addresses the challenges elderly users 

encounter in comprehending pharmaceutical packaging 
information by establishing a deep learning-assisted 
pictogram standardization framework amid global 
population aging. The Residual Network-50 model achieved 
94.8% semantic recognition accuracy across 21 pictogram 
categories, demonstrating superior performance over 
conventional convolutional architectures and Transformer-
based models. Controlled experimental validation revealed 
that standardized pictograms elevated recognition accuracy 
from 68.3% to 92.5% and reduced comprehension time by 
52%, with medication timing icons showing the most 
substantial improvement of 33 percentage points. The study 
advances Innovation Resistance Theory by quantifying dual-
pathway mechanisms wherein psychological risk contributes 
54.6% of indirect resistance effects through distrust 
mediation, while technostress amplifies risk-distrust 
relationships by factors approaching 2.6. The empirically 
derived design parameters—icon diameter ≥20mm, font size 
≥14pt, contrast ratio ≥7:1—provide enforceable standards 
for pharmaceutical manufacturers and regulatory agencies, 
with preliminary industry adoption demonstrating 
scalability. Several limitations warrant consideration. The 
cultural homogeneity of the Chinese elderly sample 
constrains cross-cultural generalizability, while simulated 
icons cannot fully replicate three-dimensional packaging 
characteristics. The three-month validation period precludes 
assessment of long-term adaptation patterns. Future 
investigations should pursue cross-cultural validation across 
diverse populations, longitudinal studies examining 
sustained usability over extended periods, multimodal 
integration combining visual, auditory, and haptic modalities 
through smart packaging technologies, and AI-driven 
personalized pictogram systems adapted to individual 
cognitive profiles. This framework establishes empirical 
foundations for age-centered pharmaceutical packaging 
design while contributing measurably to inclusive healthcare 
environments and healthy aging societies. 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically regarding authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with research ethics policies. The authors adhere to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the authors. 

Conflict of interest 

The authors declare no potential conflict of interest. 

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