You will be provided with a reference and some statements. Please determine whether each statement is 'supported', 'unsupported', or 'unknown' with respect to the reference. Please note:
First, assess whether the reference contains any valid content. If the reference contains no valid information, such as a 'page not found' message, then all statements should be considered 'unknown'.
If the reference is valid, for a given statement: if the facts or data it contains can be found entirely or partially within the reference, it is considered 'supported' (data accepts rounding); if all facts and data in the statement cannot be found in the reference, it is considered 'unsupported'.

You should return the result in a JSON list format, where each item in the list contains the statement's index and the judgment result, for example:
[
    {
        "idx": 1,
        "result": "supported"
    },
    {
        "idx": 2,
        "result": "unsupported"
    }
]

Below are the reference and statements:
<reference>
Multimodal algorithmic fusion model for physical education assessment: spatiotemporal comparison of inertial measurement units and traditional scales - PubMed

This study provides a feasible multimodal digital assessment pipeline for standardized and formative physical education evaluation. The system intelligently reproduces expert-level teacher scoring rather than objective biomechanical measurement, which makes it a powerful auxiliary tool for classroom …

Clipboard, Search History, and several other advanced features are temporarily unavailable.

Skip to main page content

An official website of the United States government

Here's how you know

The .gov means it’s official.

Federal government websites often end in .gov or .mil. Before
sharing sensitive information, make sure you’re on a federal
government site.

The site is secure.

The
https://
ensures that you are connecting to the
official website and that any information you provide is encrypted
and transmitted securely.

Log in

Show account info

Close

Account

Logged in as:

username

Dashboard

Publications

Account settings

Log out

Access keys

NCBI Homepage

MyNCBI Homepage

Main Content

Main Navigation

Search:

Search

Advanced

Clipboard

User Guide

Save

Email

Send to

Clipboard

My Bibliography
Collections
Citation manager

Display options

Display options

Format

Abstract

PubMed

PMID

Save citation to file

Format:

Summary (text)

PubMed

PMID

Abstract (text)

CSV

Create file

Cancel

Email citation

Email address has not been verified. Go to

My NCBI account settings

to confirm your email and then refresh this page.

To:

Subject:

Body:

Format:

Summary

Summary (text)

Abstract

Abstract (text)

MeSH and other data

Send email

Cancel

Add to Collections

Create a new collection

Add to an existing collection

Name your collection:

Name must be less than 100 characters

Choose a collection:

Unable to load your collection due to an error

Please try again

Add

Cancel

Add to My Bibliography

My Bibliography

Unable to load your delegates due to an error

Please try again

Add

Cancel

Your saved search

Name of saved search:

Search terms:

Test search terms

Would you like email updates of new search results?

Saved Search Alert Radio Buttons

Yes

No

Email:

(
change
)

Frequency:

Monthly

Weekly

Daily

Which day?

The first Sunday

The first Monday

The first Tuesday

The first Wednesday

The first Thursday

The first Friday

The first Saturday

The first day

The first weekday

Which day?

Sunday

Monday

Tuesday

Wednesday

Thursday

Friday

Saturday

Report format:

Summary

Summary (text)

Abstract

Abstract (text)

PubMed

Send at most:

1 item

5 items

10 items

20 items

50 items

100 items

200 items

Send even when there aren't any new results

Optional text in email:

Save

Cancel

Create a file for external citation management software

Create file

Cancel

Your RSS Feed

Name of RSS Feed:

Number of items displayed:

5

10

15

20

50

100

Create RSS

Cancel

RSS Link

Copy

Full text links

BioMed Central

Free PMC article

Full text links

Actions
Cite
Collections
Add to Collections
Create a new collection
Add to an existing collection

Name your collection:

Name must be less than 100 characters

Choose a collection:

Unable to load your collection due to an error
Please try again

Add

Cancel

Permalink
Permalink
Copy
Display options

Display options

Format

Abstract
PubMed
PMID

Page navigation

Title & authors

Abstract

Conflict of interest statement

Figures

References

LinkOut - more resources

Title & authors

Abstract

Conflict of interest statement

Figures

References

LinkOut - more resources

BMC Sports Sci Med Rehabil

Actions

Search in PubMed

Search in NLM Catalog

Add to Search

.
2026 Jun 12;18(1):364.

doi: 10.1186/s13102-026-01792-9.

Multimodal algorithmic fusion model for physical education assessment: spatiotemporal comparison of inertial measurement units and traditional scales

Junlin Cheng

1

,
Zhen Li

2

3

Affiliations

Expand

Affiliations

1
School of Physical Education, Central China Normal University, Wuhan, Hubei, 430079, China.

2
Physical education department, Xingtai Medical College, Xingtai, Hebei, 054000, China. 295647298@163.com.

3
Physical Education Research Office, YiLing High School, Yi Chang, HuBei, 443100, China. 295647298@163.com.

PMID:

42286751

PMCID:

PMC13487921

DOI:

10.1186/s13102-026-01792-9

Item in Clipboard

Multimodal algorithmic fusion model for physical education assessment: spatiotemporal comparison of inertial measurement units and traditional scales

Junlin Cheng
et al.

BMC Sports Sci Med Rehabil
.

2026
.

Show details

Display options

Display options

Format

Abstract

PubMed

PMID

BMC Sports Sci Med Rehabil

Actions

Search in PubMed

Search in NLM Catalog

Add to Search

.
2026 Jun 12;18(1):364.

doi: 10.1186/s13102-026-01792-9.

Authors

Junlin Cheng

1

,
Zhen Li

2

3

Affiliations

1
School of Physical Education, Central China Normal University, Wuhan, Hubei, 430079, China.

2
Physical education department, Xingtai Medical College, Xingtai, Hebei, 054000, China. 295647298@163.com.

3
Physical Education Research Office, YiLing High School, Yi Chang, HuBei, 443100, China. 295647298@163.com.

PMID:

42286751

PMCID:

PMC13487921

DOI:

10.1186/s13102-026-01792-9

Item in Clipboard

Full text links

Cite
Display options

Display options

Format

Abstract
PubMed
PMID

Abstract

Background:

Conventional physical education assessment depends on subjective teacher observation and simple rating scales, often resulting in subjective bias, low evaluation efficiency, and delayed instructional feedback.

Methods:

This study constructed a hierarchical multimodal fusion framework integrating inertial measurement unit (IMU) data and expert rubric scores for standardized PE skill evaluation. The framework adopts Dynamic Time Warping for spatiotemporal alignment to resolve asynchrony between continuous sensor signals and discrete manual scoring, and applies adaptive gated fusion to balance modality weights according to data quality. A total of 3,920 skill samples from 245 middle-school students across three schools and four sports were independently annotated by three calibrated PE teachers, achieving high inter-rater reliability (ICC(2,k) = 0.87, 95% CI [0.84, 0.90]). All experiments were conducted with participant-wise data splitting and ten repeated random-seed trials to ensure result stability.

Results:

The proposed multimodal model achieved an overall accuracy of 91.3 ± 0.4%, significantly surpassing single-modality baselines (p < 1 × 10⁻⁸). Spatiotemporal alignment effectively eliminated temporal mismatch, and the model maintained stable performance under simulated data missingness and sensor failure. The 43.2 ms single-sample inference speed meets real-time classroom assessment demands. Ablation experiments verified the essential role of cross-modal attention in multimodal learning. Leave-one-school-out and cross-stratum evaluations confirmed stable performance across different schools, genders, age groups and skill levels. The current multi-validation results support reliable within-population model performance, while external validation with independent cohorts is still required for broader generalizability. Model interpretability analysis further validated the biomechanical rationality of the learned assessment patterns.

Conclusions:

This study provides a feasible multimodal digital assessment pipeline for standardized and formative physical education evaluation. The system intelligently reproduces expert-level teacher scoring rather than objective biomechanical measurement, which makes it a powerful auxiliary tool for classroom teaching rather than a substitute for professional teacher judgment. This work offers a practical digital transformation pathway for PE assessment, with standardized curriculum alignment and staged teacher professional development supporting reliable real-world educational deployment.

Keywords:

Formative assessment; Inertial measurement unit; Leave-one-school-out validation; Model interpretability; Multimodal fusion; Physical education assessment; Spatiotemporal alignment.

© 2026. The Author(s).

PubMed Disclaimer

Conflict of interest statement

Declaration. Ethics approval and consent to participate: Ethical approval for this study was obtained from the Central China Normal University Research Ethics Committee, in compliance with the Declaration of Helsinki. Informed consent was obtained from all participating students and their legal guardians, and strict measures were implemented to protect the privacy and data security of the subjects. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1

IMU data preprocessing and feature…

Fig. 1

IMU data preprocessing and feature extraction process.
a
Raw signal acquisition from a…

Fig. 1

IMU data preprocessing and feature extraction process.
a
Raw signal acquisition from a 9-axis IMU at 100 Hz, showing tri-axial accelerometer, gyroscope, and magnetometer streams.
b
Noise filtering pipeline, with a complementary filter followed by an adaptive Kalman filter applied to the raw signals to suppress drift and high-frequency vibration.
c
Attitude calculation via the quaternion method to avoid gimbal lock, producing the quaternion stream used downstream.
d
Feature extraction across time-domain (mean, standard deviation, peak, RMS), frequency-domain (FFT-derived dominant frequency, spectral energy), and time-frequency (wavelet coefficient) representations

Fig. 2

Hierarchical attention fusion model structure.
…

Fig. 2

Hierarchical attention fusion model structure.
a
IMU encoder built from stacked bidirectional LSTM…

Fig. 2

Hierarchical attention fusion model structure.
a
IMU encoder built from stacked bidirectional LSTM layers (hidden = 128 per direction) producing a 256-d kinematic representation from the 9 × T raw input stream.
b
Scale encoder consisting of fully-connected layers with ReLU activations (FC 64 → 128 → 128) producing a 128-d rubric representation from the N-feature scale input.
c
Cross-modal attention block with multi-head scaled dot-product attention (4 heads, dk = 64) over query, key, and value projections.
d
Fusion decision module with gated fusion followed by parallel classification (FC 256 → 64 → 4, four-tier Excellent/Good/Pass/Fail softmax) and regression (FC 256 → 64 → 1, continuous score 0–10) heads

Fig. 3

Classification accuracy across the four…

Fig. 3

Classification accuracy across the four sports for IMU-only, scale-only, and the proposed fusion…

Fig. 3

Classification accuracy across the four sports for IMU-only, scale-only, and the proposed fusion model. The horizontal axis, from left to right, lists Basketball Dribbling, Standing Long Jump, Sit-ups, and 50 m Sprint (i.e., one bar-group per sport, with the previously duplicated “Standing Long Jump” label corrected). Bars represent the mean over ten random seeds; error bars denote one standard deviation

Fig. 4

Time complexity comparison of different…

Fig. 4

Time complexity comparison of different algorithms.
a
Single-sample inference time (ms) across seven…

Fig. 4

Time complexity comparison of different algorithms.
a
Single-sample inference time (ms) across seven methods — SVM (8.2 ms), Random Forest (15.3 ms), CNN (28.6 ms), LSTM (35.1 ms), Early Fusion (38.9 ms), Late Fusion (41.5 ms), and the proposed method (43.2 ms) — with the 50 ms real-time threshold marked as a vertical reference line; all methods fall below the threshold required for terminal within-session formative feedback.
b
GPU memory usage trajectory of the proposed method across 100 training epochs, decomposed into four components (model weights, gradients, activations, and buffer/cache); peak memory reaches 9.4 GB and stabilizes after approximately 20 epochs

Fig. 5

Robustness analysis of the fusion…

Fig. 5

Robustness analysis of the fusion model against (
a
) additive Gaussian noise…

Fig. 5

Robustness analysis of the fusion model against (
a
) additive Gaussian noise at varying signal-to-noise ratios, (
b
) random IMU-data missing at varying missing rates, and (
c
) temporal misalignment between IMU streams and rubric scoring events. In each panel, the solid purple curve denotes the fusion model, the dashed blue curve denotes IMU-only, and the dotted green curve denotes scale-only. The shaded band around each curve represents one standard deviation across ten random seeds

Fig. 6

Correlation heatmap between IMU features…

Fig. 6

Correlation heatmap between IMU features and scale scores

Fig. 6

Correlation heatmap between IMU features and scale scores

Fig. 7

Prediction error distribution and source…

Fig. 7

Prediction error distribution and source analysis.
a
Histogram of prediction errors pooled across…

Fig. 7

Prediction error distribution and source analysis.
a
Histogram of prediction errors pooled across the ten random seeds, fitted with a Gaussian curve (mean = 0.05, median = 0.03, SD = 0.48 score points on the 0–10 scale).
b
Scatter plot of absolute prediction error against per-sample difficulty score with linear regression overlay and 95% confidence band (Pearson r = 0.68, p < 0.001).
c
Sport-level scatter of inter-rater ICC(2,k) against model mean absolute error across the four sports, with linear regression overlay confirming the strong negative association (Pearson r = −0.92).
d
Box plot comparing per-seed mean absolute error before and after teacher-confidence-weighted optimization, showing a 30.1% relative improvement (paired t(9) = 11.3, p = 1.2 × 10⁻⁶)

Fig. 8

Performance comparison radar chart of…

Fig. 8

Performance comparison radar chart of different methods across sports

Fig. 8

Performance comparison radar chart of different methods across sports

Fig. 9

Interpretability analysis of the trained…

Fig. 9

Interpretability analysis of the trained fusion model. (
a
) Cross-modal attention weights…

Fig. 9

Interpretability analysis of the trained fusion model. (
a
) Cross-modal attention weights per sport; (
b
) sensor contribution heatmap across the six IMU nodes; (
c
) top-three feature importance ranking per sport

All figures (9)

See this image and copyright information in PMC

References

Huang X, Wang Q, Zhang S. A novel lower extremity non-contact injury risk prediction model based on multimodal fusion and interpretable machine learning. Front Physiol. 2022;13:937546. 10.3389/fphys.2022.937546.

-

DOI

-

PMC

-

PubMed

Biermann H, Muller S, Schmidt S. Synchronization of passes in event and spatiotemporal soccer data. Sci Rep. 2023;13(1):39616. 10.1038/s41598-023-39616-2.

-

DOI

-

PMC

-

PubMed

Wallace J, Scanlon D, Calderón A. Digital technology and teacher digital competency in physical education: a holistic view of teacher and student perspectives. Curriculum Stud Health Phys Educ. 2023;14(3):271–87. 10.1080/25742981.2022.2106881.

-

DOI

Venek V, Kranzinger S, Schwameder H, Stöggl T. Human movement quality assessment using sensor technologies in recreational and professional sports: a scoping review. Sensors. 2022;22(13):4786. 10.3390/s22134786.

-

DOI

-

PMC

-

PubMed

Barnett LM, Jerebine A, Keegan R, Watson-Mackie K, Arundell L, Ridgers ND, Salmon J, Dudley D. Validity, reliability, and feasibility of physical literacy assessments designed for school children: a systematic review. Sports Med. 2023;53(10):1905–29. 10.1007/s40279-023-01867-4.

-

DOI

-

PMC

-

PubMed

Show all 30 references

LinkOut - more resources

Full Text Sources

BioMed Central

PubMed Central

Full text links

[x]

BioMed Central

Free PMC article

[x]

Cite

Copy

Download .nbib

.nbib

Format:

AMA

APA

MLA

NLM

Send To

Clipboard

Email

Save

My Bibliography

Collections

Citation Manager

[x]

NCBI Literature Resources

MeSH

PMC

Bookshelf

Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

Follow NCBI

Connect with NLM

National Library of Medicine

8600 Rockville Pike

Bethesda, MD 20894

Web Policies

FOIA

HHS Vulnerability Disclosure

Help

Accessibility

Careers

NLM

NIH

HHS

USA.gov
</reference>

<statements>
1. Functional Operationalization in Athletic Environments for the Domain Model: Encodes normative 3D kinematic manifolds, kinetic energy transfer chains, phase boundaries, and standardized competition judging criteria
2. The domain model in a sports ITS cannot rely on static declarative facts. Instead, it must represent expert movement trajectories, dynamic stability constraints, musculoskeletal load capacities, and sport-specific scoring rubrics
3. Capturing this continuous kinetic chain using a single sensor type introduces operational vulnerabilities: computer vision faces visual occlusions and field-of-view constraints, while wearable inertial sensors cannot capture environmental reference frames or the external judging gaze
4. Wearable Inertial Measurement Units Operational Sampling Rates: 100–500 Hz
5. Wearable Inertial Measurement Units Extracted Physical & Biomechanical Variables: Angular velocities, linear accelerations, segmental orientation (quaternions), impact shocks
6. Wearable Inertial Measurement Units Complementary Advantages: High temporal resolution, field-deployable across open spaces, immune to visual occlusion and lighting shifts
7. For Early (Feature-Level) Fusion, systemic bottlenecks include high sensitivity to sensor dropouts and susceptibility to dimensionality expansion.
8. In the fusion architecture paradigm comparison, Hybrid (Deep Representation) Fusion is listed as a fusion architecture paradigm.
9. For Hybrid (Deep Representation) Fusion, the algorithmic implementation strategy is spatiotemporal Graph Convolutional Networks paired with Cross-Modal Attention and Gated Units.
10. For Hybrid (Deep Representation) Fusion, the ideal sports ITS deployment scenario is real-time Action Quality Assessment and continuous biomechanical error diagnosis.
11. For Hybrid (Deep Representation) Fusion, key operational advantages include dynamically weighing modalities based on signal quality and capturing spatial and temporal dependencies.
12. For Late (Decision-Level) Fusion, key operational advantages include resilience against individual hardware failures and modular integration of algorithms.
13. Hybrid deep fusion architectures provide the strongest performance for real-time motion analysis by balancing shared representations with modality-specific feature extraction.
14. To merge this spatial skeleton with temporal IMU feature maps, cross-modal attention mechanisms map dependencies across feature spaces.
15. If a camera feed is obscured, the gate alpha shifts weight toward the IMU representations, maintaining continuous tracking.
16. These correlations confirm that multimodal action analytics can reliably replicate expert movement assessments.
17. In school-based physical education, automated systems address challenges stemming from large student-to-teacher ratios and subjective assessments [9].
18. A multi-center validation study across three middle schools evaluated 3,920 skill executions from 245 students across basketball, volleyball, gymnastics, and track events [9].
19. Integrating wearable IMU data with automated video tracking yielded an assessment consistency of \(\text{ICC}(2, k) = 0.87\) when compared against calibrated master physical education instructors [9].
20. The system maintained an average inference latency of 43.2 ms per sample on edge devices, supporting continuous tracking and assessment during standard classroom rotations [9].
21. Providing concurrent biofeedback during high-speed sports requires an end-to-end processing latency below 50 milliseconds [9].
22. Conversely, markerless computer vision provides an unencumbered capture environment but remains vulnerable to self-occlusions, changing ambient lighting, and boundary constraints in outdoor settings [9].
23. At the algorithmic level, cross-modal view synthesis and generative pose estimation architectures are being developed to reconstruct occluded body segments by combining camera views with lightweight telemetry from consumer smartwatches or earbuds
24. In educational and youth sports contexts, recording student video requires adherence to legal protections, including FERPA, GDPR, and biometric privacy standards
25. Action recognition and pose estimation models trained predominantly on homogeneous adult athletic cohorts frequently exhibit higher error rates when applied to pediatric students, female athletes, or individuals with atypical gait patterns
26. Ensuring equitable instructional guidance requires validating systems across diverse demographics and body profiles
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.