Overview

The MPDD-AVG Challenge 2026 comprises two age-specific datasets — MPDD-Young and MPDD-Elderly — each featuring three complementary sub-tracks that explore different combinations of behavioral modalities and personality modeling. The challenge uniquely integrates semi-structured interview behavioral data with continuous gait monitoring from wearable sensors, enabling holistic assessment spanning cognitive-linguistic, affective-paralinguistic, and psychomotor domains.

This challenge is an updated version of MPDD2025 @ ACM MM 2025. Compared to the previous edition, MPDD-AVG introduces a new gait modality (IMU-based ambulatory monitoring), the G+P and A-V-G+P sub-tracks, and an extended annotation scheme including health condition labels.

Dataset

MPDD-Young

The MPDD-Young dataset comprises data from 110 college students, investigating how academic stress, social environment, and personality traits contribute to depression in young adults. Participants underwent semi-structured interviews designed to assess academic stress, social functioning, and emotional well-being. Subsequently, participants walked naturally within a designated area while equipped with wearable IMU sensors.

Annotations: PHQ-9 Scale Scores · Big Five-10 personality traits · Demographics (gender, age, birth region)

Classification tasks: Binary (normal / depressed)  ·  Ternary (normal / mild / severe)

MPDD-Elderly

The MPDD-Elderly dataset comprises data from 110 older adults, examining how chronic illnesses, living conditions, and personality traits influence late-life depression manifestation. Participants engaged in semi-structured interviews and then walked freely within a designated area while wearing IMU sensors.

Annotations: PHQ-9 Scale Scores · Big Five-10 personality traits · Demographics (age, gender, family situation, economic status) · Disease labels (endocrine, circulatory, nervous system)

Classification tasks: Binary · Ternary · Quinary (normal / mild / moderate / moderately severe / severe)

Dataset Comparison

As compared to existing datasets, MPDD-AVG significantly enhances both the breadth of behavioral modalities and the depth of individual difference annotations:

80% of data used for training and validation; 20% for testing. Standardized splits provided.

Tracks & Sub-tracks

Each of the two age-specific datasets (MPDD-Young and MPDD-Elderly) features three complementary sub-tracks:

Sub-track Descriptions

• A-V+P: Audio-Visual interview features (acoustic + visual cues) conditioned on Big Five-10 personality traits. Participants may leverage the provided personality annotations to build personality-aware depression detection models.
• A-V-G+P: Full multimodal fusion integrating conversational interview cues, IMU-based ambulatory gait patterns, and Big Five-10 personality information. This is the primary flagship sub-track.
• G+P: Gait-only detection from IMU sensor data, conditioned on personality traits. Explores psychomotor symptoms of depression through ambulatory behavior analysis.

Evaluation Metrics

The Challenge employs comprehensive metrics to evaluate multimodal depression detection models across classification and regression tasks.

Classification Metrics

• Accuracy (ACC) — proportion of correct predictions
• Macro F1-Score — F1 averaged equally across all depression severity classes, accounting for class imbalance
• Cohen's Kappa (κ) — inter-rater agreement accounting for chance, particularly valuable for assessing clinical diagnostic consistency

Regression Metrics

• Root Mean Squared Error (RMSE) — quantifies prediction error magnitude on continuous PHQ-9 scores
• Mean Absolute Error (MAE) — interpretable average prediction error
• Concordance Correlation Coefficient (CCC) — assesses agreement between predicted and actual PHQ-9 scores, combining precision and accuracy

Track-Level Score

The final evaluation score for each track is calculated as:

Scoretrack = α · Macro-F1 + β · CCC + γ · κ

where α + β + γ = 1, reflecting the relative importance of classification performance, continuous score prediction, and diagnostic consistency.

Baseline & Resources

We provide the following materials to all registered participants:

• Baseline code for all 6 sub-tracks (A-V+P, A-V-G+P, G+P for both Young and Elderly) — available on GitHub
• Baseline paper describing the challenge background, dataset details, baseline systems, and results — available ~April 10, 2026
• MPDD-AVG dataset conditioned on signed EULA agreements (feature sets now; raw data pending ethical review)
• Standardized audio-visual and gait feature sets
• Raw individual difference annotations including Big Five-10 personality traits, demographics, and health conditions

CodaLab Leaderboard

Results submission will be hosted on CodaLab (opens May 1, 2026). Links will be provided after that date. Each team is restricted to a maximum of 5 submission attempts per sub-track per day.

• MPDD-Young (all sub-tracks): TBD — opens May 1, 2026
• MPDD-Elderly (all sub-tracks): TBD — opens May 1, 2026

Paper Submission

To be eligible for the final evaluation, each team must submit a system description paper via OpenReview (venue opens July 1, 2026). Papers must include thoroughly explained source code, well-trained models, and associated checkpoints. All submissions undergo peer review by the challenge technical program committee.