---
title: Drone-Assisted Naloxone Delivery Simulation Dataset
url: https://www.emergentmind.com/topics/drone-assisted-naloxone-delivery-simulation-dataset-dandsd
type: topic
---

# Drone-Assisted Naloxone Delivery Simulation Dataset

The **Drone-Assisted Naloxone Delivery Simulation Dataset (DANDSD)** is a **public, annotated video dataset** of **drone-assisted naloxone delivery simulations** designed to support **real-time bystander situation awareness (SA) estimation** during **simulated opioid overdose emergency (OOE)** scenarios. It captures **human-autonomy teaming (HAT)** from the **observer/bystander perspective**, with **medically untrained college students** acting as bystanders who attempt to administer **intranasal naloxone (Narcan™)** delivered by a drone to a **mock overdose victim (mannequin)**. The dataset was introduced together with a video-based real-time SA assessment framework that combines **graph embeddings** and **transformer models** for SA prediction and temporal segmentation [2510.03558].

## 1. Dataset definition and research role

DANDSD was created to address a specific gap: there was **no publicly available dataset** for analyzing **bystander behavior and SA** in drone-assisted naloxone delivery during opioid overdose emergencies. The dataset is explicitly designed to support **real-time assessment of the bystander’s situational awareness** while an emergency response scenario unfolds. In the source paper, it is positioned as a first-of-its-kind dataset for **real-time drone-assisted first aid**, **observer-rated SA annotations**, **temporal event boundary annotations**, and downstream support for both **SA prediction** and **temporal segmentation** [2510.03558].

The motivating premise is that the success of drone-assisted emergency first aid depends not only on autonomous delivery, but also on the bystander’s ability to **perceive the scene**, **understand what to do**, and **anticipate what happens next**. This framing aligns the dataset with SA-centric HAT research rather than generic action recognition. A plausible implication is that DANDSD is intended less as a conventional vision benchmark than as a substrate for modeling cognitive state transitions during task execution.

The scenario captured by DANDSD is task-driven and spans the full rescue workflow: the bystander encounters the victim, the drone assists with naloxone delivery, the bystander interacts with the drone and scene, and naloxone is successfully administered. The recordings therefore encode action, instruction following, object interaction, and state transition within a single continuous emergency-response sequence.

## 2. Scenario construction, participants, and corpus statistics

DANDSD is derived from a prior simulation study comprising **17 simulated trials**, from which the dataset contains **11 continuous, uninterrupted videos**. Each video is **2–3 minutes long**, recorded at **50 fps**, with a total of **92,917 frames**. The machine-learning split reported in the paper includes **5,575 sequences** for training and **620 sequences** for testing, with **each sequence length = 15 frames**; sequences are shuffled during training [2510.03558].

The human participants are **college students without medical training**, who serve as bystanders. Other scene entities are the **mock overdose victim / mannequin**, an **instructor**, and a **drone**. These four entities also define the object set used later in the interaction graph formulation. The bystander’s task is to recognize the emergency, interact with the drone-delivered naloxone, follow guidance, and administer intranasal naloxone to the mannequin.

| Dataset component | Reported value |
|---|---|
| Simulated trials | 17 |
| Continuous videos | 11 |
| Video duration | 2–3 minutes |
| Frame rate | 50 fps |
| Total frames | 92,917 |
| Training sequences | 5,575 |
| Testing sequences | 620 |
| Sequence length | 15 frames |

Because the corpus consists of **simulated** OOEs rather than real emergency footage, it should not be construed as a naturalistic dataset of field opioid overdoses. The paper instead emphasizes its utility for controlled study of SA in drone-assisted first aid. This suggests that its principal methodological value lies in structured annotation, temporal continuity, and explicit HAT interaction, rather than in uncontrolled real-world variability.

## 3. Annotation design and SA label construction

DANDSD contains **two annotation types**: **time interval annotations** and **SA rating annotations**. For temporal structure, the videos were manually segmented into **distinct events** based on bystander actions. **Two annotators independently reviewed the videos**, reached **consensus** on event boundaries, and marked boundaries by specific movements or actions between events. The temporal segmentation task uses **five predefined events** [2510.03558].

For SA, each video was divided into **10 equally sized clips**, yielding **110 clips** total, with **clip length = 30 seconds**. **Two domain experts** independently rated each clip on a **1–5 scale** for three SA levels:

1. **Perception**: “To what extent has the bystander observed all necessary visual cues?”
2. **Comprehension**: “How well does the bystander understand the situation and required actions?”
3. **Projection**: “To what degree does the bystander anticipate future developments and consequences?”

A critical methodological detail is that **each expert gives one single rating per clip after reviewing the full clip**. The reported interpretation is that each rating reflects the expert’s holistic judgment at the **final timestamp of the clip**. To obtain frame-level supervision, the authors **linearly interpolated** between rated points and **reset SA to 0 at event boundaries**, thereby producing a continuous SA trajectory over time.

The paper formulates SA classification in both binary and ternary forms. For each SA dimension, binary labels are thresholded at 3:

$$
\text{High SA} \iff \text{rating} \ge 3,\quad \text{Low SA} \iff \text{rating} < 3
$$

For frame \(i\),

$$
[Per_i, Com_i, Pro_i], \quad Per_i,Com_i,Pro_i \in \{0,1\}
$$

where \(1\) indicates high SA.

The ternary formulation uses Endsley’s hierarchical model, in which higher SA levels are meaningful only if lower levels are achieved. The binary levels are accumulated into a ternary class label \(\{0,1,2\}\). The paper gives illustrative mappings: \([0,1,1]\) maps to class 0 because perception is not high, while \([1,1,1]\) maps to the highest class.

This annotation design matters because the dataset does not merely provide class labels for isolated clips; it provides a mechanism for deriving **continuous SA curves** linked to event structure. A plausible implication is that DANDSD supports hybrid inference over both cognition and action boundaries, which is unusual in task-specific HAT datasets.

## 4. Modalities, feature representation, and compositional framework

The associated framework is described as a **two-stage compositional learning framework** consisting of a **Perception Module** and a **Comprehension Module**. The architecture is intended to make SA prediction more interpretable and modular than an end-to-end black-box model [2510.03558].

The Perception Module handles low-level visual understanding, including **object detection**, **tracking**, and **pose estimation**. It uses **YOLOv8** for object detection, **BoT-SORT** for multi-object tracking, and 2D keypoint estimation for body pose. Its outputs include bystander bounding boxes and pose keypoints. For each frame \(i\), the bystander bounding box is

$$
b_i = \{x_1, y_1, x_2, y_2\}
$$

and the bystander’s body pose is represented with **17 2D keypoints**:

$$
P_i := \{p_1, p_2, \dots, p_{17}\}.
$$

The Comprehension Module captures higher-level reasoning through **object centers**, **depth estimates**, **graph embeddings**, and **transformer sequence modeling**. The scene is modeled as a **fully connected graph** over four objects: **bystander**, **instructor**, **patient**, and **drone**. Node attributes include **2D coordinates**, **depth label**, and **body points for humans**; the drone receives **zero-padded pose features** because pose estimation is not applicable. To estimate spatial relations, the method uses monocular depth estimation via **DPT with ViT backbone**. For each frame \(i\), a depth label \(d_i\) is derived from the disparity map \(D_i\) at the center point \(c_i\).

The graph convolution update is defined as

$$
f(\Phi^{(l)}, A) = \sigma(A\Phi^{(l)}W^{(l)})
$$

where \(\Phi^{(l)}\) denotes node features at layer \(l\), \(A\) the adjacency matrix, \(W^{(l)}\) learned weights, and \(\sigma\) the activation function. A two-layer **GCN autoencoder** embeds the interaction graph into compact representations:

$$
(N \times f) \rightarrow (N \times 16) \rightarrow (N \times g).
$$

The resulting graph embedding \(G_i\) is reported to capture **spatial relations**, **pose context**, **relative object proximity**, and **interaction structure**.

The final SA predictor is a **Transformer-based sequence model**, inspired by **TrEP**. For each frame \(i\), the extracted features are concatenated as

$$
x_i = [b_i, P_i, G_i]
$$

and the input sequence is

$$
X_i = \{x_1, x_2, \dots, x_l\}
$$

where \(l\) is the sequence length. The transformer projects features through a shared feed-forward layer, adds positional encoding, applies multiple transformer blocks, then flattens and classifies the output. Each transformer block uses **multi-head self-attention** and **feed-forward sublayers**. The binary SA task uses **sigmoid activation** and **binary cross-entropy**; the ternary SA task uses **softmax activation** and **categorical cross-entropy**.

## 5. Temporal segmentation through latent SA dynamics

A central contribution associated with DANDSD is the use of predicted SA trajectories to infer **event boundaries**. The stated logic is that **SA changes reflect cognitive transitions**; when SA resets or changes sharply, a new event is likely beginning. On that basis, latent SA cues are used to segment untrimmed video into meaningful actions [2510.03558].

This segmentation procedure depends directly on the annotation and label-construction scheme. Because SA is interpolated over time and reset to 0 at event boundaries, the trajectory is not treated merely as a static supervisory signal but as a temporal state variable. The model tracks SA over time, smooths it, and identifies transition points. The paper applies a **13-frame Gaussian filter**, corresponding to an approximate human reaction time of **0.25 s**; at **50 fps**, **13 frames \(\approx 0.26\) s**, which the paper notes is consistent with the stated reaction time.

Temporal segmentation is evaluated using **Mean over Frames (MoF)** and **Intersection over Union (IoU)**. The reported definitions are

$$
\text{MoF} = \frac{1}{N} \sum_{i=1}^{N} I(y_i = \hat{y}_i)
$$

where \(N\) is the total number of frames, \(y_i\) the true label at frame \(i\), \(\hat{y}_i\) the predicted label at frame \(i\), and \(I(\cdot)\) the indicator function; and

$$
\text{IoU} = \frac{|A \cap B|}{|A \cup B|}
$$

where \(A\) is the predicted segmentation and \(B\) the ground-truth segmentation.

The paper notes that MoF is frame-wise accuracy but can be sensitive to class imbalance. This is relevant because DANDSD contains imbalanced SA labels. For ternary labels, the counts are **34,453** for class 0, **33,587** for class 1, and **24,877** for class 2. For binary labels, the reported counts are **46,498 / 46,419** for Perception, **48,838 / 44,079** for Comprehension, and **55,885 / 37,032** for Projection.

## 6. Evaluation protocol and empirical findings

For SA prediction, the framework is evaluated using **Accuracy**, **AUC**, **F1**, **Precision**, and **Balanced Accuracy (BAcc)**. The reported protocol uses **random sampling for balanced representation**, **10-fold cross-validation**, and **early stopping with tolerance of 5 epochs** [2510.03558].

For ternary SA prediction, the paper compares the proposed method against **C3D**, **I3D**, **X3D**, and **SlowFast**. The reported results are as follows:

| Model | Accuracy | AUC | F1 |
|---|---:|---:|---:|
| C3D | 0.54 | 0.50 | 0.33 |
| I3D | 0.55 | 0.52 | 0.47 |
| X3D | 0.60 | 0.57 | 0.55 |
| SlowFast | 0.60 | 0.58 | 0.56 |
| Ours | 0.63 | 0.64 | 0.62 |

The corresponding **Precision** values are **0.32** for C3D, **0.45** for I3D, **0.54** for X3D, **0.54** for SlowFast, and **0.62** for the proposed method. The paper states that the model improves over the best baseline by about **3% to 8% across metrics**.

Feature ablation further tests **Bbox**, **Pose**, **Graph**, and their combinations. For binary per-class top-1 accuracy, the best result comes from **Bbox + Pose + Graph**: **0.71** for Perception, **0.60** for Comprehension, and **0.74** for Projection. For ternary classification, the best result is again **Bbox + Pose + Graph**, with **Accuracy = 0.63**, **Balanced Accuracy = 0.62**, and **F1 = 0.62**. The paper additionally reports that **pose alone performs better than bbox alone in ternary SA**, that **graph alone is weak by itself**, and that **graph adds value when combined with other features**. In the paper’s interpretation, this supports the importance of **interaction structure** for understanding situational awareness.

For temporal segmentation, the SA-informed model is compared with **TW-FINCH** baselines. The reported results are:

| Method | MoF | IoU |
|---|---:|---:|
| TW-Finch (cls = 6) | 0.41 | 0.23 |
| TW-Finch (cls = 7) | 0.49 | 0.29 |
| TrSA | 0.58 | 0.34 |

The paper states that **TrSA** outperforms **TW-FINCH (cls = 7)** by **9% in MoF** and **5% in IoU**. In the framing of the work, this result indicates that latent SA predictions can improve event segmentation, not merely SA classification.

## 7. Interpretation, significance, and recurrent points of clarification

DANDSD is significant in several explicitly stated ways. For **human-autonomy teaming**, it provides a concrete benchmark for studying how AI systems can support humans in high-stakes collaborative tasks. For **drone-assisted first aid**, it supports systems that assess whether bystanders are prepared to follow life-saving instructions during naloxone delivery. For **adaptive emergency-response systems**, it opens the door to future drones that can **detect confusion**, **adjust instructions**, **time guidance based on bystander SA**, and support more effective first intervention. The paper also states that the dataset is publicly available, together with source code, to enable reproducibility and future work in **SA modeling**, **temporal segmentation**, **emergency robotics**, and **human-drone interaction** [2510.03558].

Several clarifications are important for correct interpretation. First, DANDSD is not a dataset of real OOEs; it is a dataset of **simulated opioid overdose emergency scenarios** with a **mock overdose victim (mannequin)**. Second, its SA labels are not instantaneous self-reports from participants. They are **observer-rated**, clip-level expert judgments assigned after review of the **full clip**, then transformed into continuous supervision by interpolation and boundary reset. Third, the paper does not present graph structure as sufficient on its own; rather, **graph alone is weak by itself**, but contributes when fused with bounding-box and pose information. Fourth, the framework’s temporal segmentation is not derived from direct event classification alone; it is derived from **latent SA changes**, which the paper treats as informative about event transitions.

The broader conceptual claim advanced by the work is that **SA is not just an outcome label**; it is also a useful latent signal for understanding temporal structure. Within that view, DANDSD functions as a dataset for jointly studying perception-level cues, comprehension-level scene relations, and projection-related temporal evolution in an emergency HAT setting. A plausible implication is that the dataset may be most valuable where the central research question concerns how human cognitive state and embodied action co-evolve under real-time autonomous assistance.

Source: https://www.emergentmind.com/topics/drone-assisted-naloxone-delivery-simulation-dataset-dandsd