Datasets:

DzaisPhuc
/

soict-memory-task-fpv-frames

---
pretty_name: SOICT Memory-Task FPV (VR Supermarket)
license: other
license_name: tbd-by-authors
language:
  - en
  - vi
task_categories:
  - video-classification
  - other
tags:
  - first-person-video
  - egocentric
  - virtual-reality
  - behavior-analysis
  - temporal-action-localization
  - prospective-memory
  - cognitive-assessment
size_categories:
  - 100K<n<1M
configs:
  - config_name: videos
    data_files:
      - split: train
        path: videos/**
  - config_name: activity_segments
    data_files:
      - split: train
        path: labels/activity_segments.csv
  - config_name: event_segments
    data_files:
      - split: train
        path: labels/event_segments.csv
---
# SOICT Memory-Task FPV — VR Supermarket Dataset

First-person-view (FPV) recordings of a **memory-oriented shopping task in a virtual-reality supermarket**, together with
**two independent sets of temporal annotations** (what the participant is doing, and whether the way they do it is unusual)
and the **video frames** extracted from every recording.

The dataset accompanies the paper *Gameplay-to-Behavior of Memory-Task First-Person Video with Resource-Controlled Latent
Cognitive Interpretation* (URA Research Group, HCMUT).

> **Access:** this repository is currently **private**. File names contain participant identifiers (see
> [Privacy & ethics](#privacy--ethics)).

## At a glance

| | |
|---|---|
| Sessions (videos) | 12 |
| Participants | 6 recorded names (several have more than one session) |
| Total duration | 3,856 s (≈ 64.3 min) |
| Resolution / frame rate | 1920 × 1080, ≈ 30 fps (HEVC / `.mp4`) |
| Total frames | 115,685 |
| Device | Standalone Meta Quest headset (screen recordings, `com.oculus.vrshell-*`) |
| Annotation streams | Activity (8 classes) and Event (5 classes) |
| Annotators | 2 independent passes per video |
| Annotation language | English (free-text `note` / `evidence`) |

## The task

Participants wear a VR headset and complete a goal-directed shopping mission in a virtual supermarket. They must
**remember and act on a shopping list**:

1. read the item list on a virtual board (*task-list consultation*);
2. walk the aisles and point a controller ray at items, which shows a tooltip with name and price (*product inspection*);
3. grab the target item (*product selection*) and review the cart (*cart interaction*);
4. react to yellow banners that substitute or add items mid-trial (*task-change notice*);
5. pay at the cashier (*checkout*).

Because the list has to be held in memory and can change, the recordings contain rich behaviour beyond simple task
completion: searching, revisiting the list, hesitating, making mistakes and recovering from them.

## Repository structure

```
.
├── README.md
├── videos/
│   ├── <video_id>.mp4              # 12 full-length FPV recordings
│   └── metadata.csv                # video_id, session date, duration, frame count, size
├── labels/
│   ├── activity_segments.csv       # all activity segments, both annotators (flat table)
│   ├── event_segments.csv          # all event segments, both annotators (flat table)
│   ├── annotator_1/<video_id>_{activity,events}.json
│   └── annotator_2/<video_id>_{activity,events}.json
├── all_frames/
│   └── <video_id>/<video_id>_partNNN.tar     # every frame, packed in tar shards
└── selected_clear_frames/
    ├── by_video/<video_id>/                  # sharp, representative frames (plain .jpg)
    └── by_activity/<activity>/               # the same kind of frames, grouped by activity
```

### Video IDs

`<participant>-<ddmm>[-<n>]`: participant slug, session date (day + month, 2026), and a run index when a participant has
several sessions on the same day.

`khoi-1708-1`, `khoi-1708-2`, `khoi-1708-3`, `le-hien-2108-1`, `le-hien-2108-2`, `nguyen-an-2108-1`,
`nguyen-an-2108-2`, `nguyen-kiet-2108-1`, `nguyen-kiet-2108-2`, `nguyen-lich-2108-1`, `nguyen-lich-2108-2`,
`tran-quyen-1808`.

The same ID is used for the video, its labels, and its frame folder.

### Frames

* `all_frames/<video_id>/<video_id>_partNNN.tar` — every frame of the video (`frame_000001.jpg`, `frame_000002.jpg`, …,
  numbered from 1, extracted at the native frame rate). Frames are packed into shards of ≤ 5,000 images because the Hub
  limits a folder to 10,000 files. Total ≈ 115.7k images.
* `selected_clear_frames/` — a small curated subset (≈ 163 MB) of sharp, unblurred frames, kept as individual images,
  organised both by video and by activity. Handy for quick inspection or for building small training/validation sets.

Frame index ↔ time: `time_sec ≈ (frame_index − 1) / fps`, with fps ≈ 30 (see `videos/metadata.csv` for exact
per-video frame counts and durations).

## Annotations

Every video was labelled in **two separate passes** (activity, then event) by **two independent annotators**, following the
protocol `ura-fpv-behavior-events-v3-two-branch`. Annotators do not see each other's work. Both versions are released as-is
(`annotator_1`, `annotator_2`); they are **not merged** into a consensus. Timestamps are in **seconds from the start of the
video**, to 0.1 s.

Only intervals where something is observed are annotated — gaps mean "no measured action" / "normal behaviour"; they are
not labelled.

### Activity stream — "what is the participant doing?"

File suffix `_activity.json`; flat table `labels/activity_segments.csv`.

| Field | Description |
|---|---|
| `start_sec`, `end_sec` | Segment boundaries in seconds |
| `activity` | One of the 8 classes below |
| `confidence` | Annotator confidence in [0, 1] |
| `note` | Free-text justification |

| Class | Meaning |
|---|---|
| `task_list_consultation` | The shopping-list board is visible and readable |
| `cart_interaction` | The cart panel is open and covers most of the view |
| `product_inspection` | A tooltip (name, price) is shown while pointing at an item — looking, not yet taking |
| `product_selection` | An item is actually taken off the shelf / put in the cart |
| `product_search` | Scanning several shelves or areas for a target item |
| `navigation` | Walking or turning with no panel open |
| `checkout` | The payment screen is shown, or the participant is at the cashier |
| `task_change_notice` | A banner announces a task change (app-generated, not a participant action) |

Derived categories (re-consultation of the list, location revisits, "other task action", "no measured action") are **not**
annotated by hand; they are inferred from the above.

### Event stream — "is the way they do it unusual?"

File suffix `_events.json`; flat table `labels/event_segments.csv`. Only *abnormal* intervals are listed.

| Field | Description |
|---|---|
| `start_sec`, `end_sec` | Segment boundaries in seconds |
| `behavior_state` | One of the 5 classes below |
| `certain` | `y` / `n` — annotator certainty |
| `evidence` | Free-text description of what was observed |

| Class | Meaning |
|---|---|
| `hesitation` | Gaze or pointer wavers between options, or approaches then backs away from an item before deciding |
| `freezing` | View stays almost motionless for several seconds while work remains to be done |
| `distraction` | Attention lingers on something unrelated to shopping |
| `task_error` | A visible mistake against the task (wrong item, wrong quantity, premature checkout, …) |
| `recovery` | Correcting a previously recorded `task_error` |

Overlapping events are recorded as separate rows.

### Flat-table columns

`activity_segments.csv`: `video_id, annotator, start_sec, end_sec, activity, confidence, note`
`event_segments.csv`: `video_id, annotator, start_sec, end_sec, behavior_state, certain, evidence`

### Label statistics (per annotator)

| Activity | Count (A1 + A2) | | Event | Count (A1 + A2) |
|---|---:|---|---|---:|
| `product_inspection` | 168 | | `hesitation` | 82 |
| `navigation` | 162 | | `task_error` | 58 |
| `product_selection` | 86 | | `recovery` | 22 |
| `cart_interaction` | 68 | | `distraction` | 16 |
| `task_list_consultation` | 54 | | `freezing` | 6 |
| `checkout` | 34 | | | |
| `product_search` | 26 | | | |
| `task_change_notice` | 22 | | **Total** | **184** |
| **Total** | **620** | | | |

Each annotator contributes 310 activity segments and 92 event segments.

## Usage

```python
from huggingface_hub import hf_hub_download
import pandas as pd
repo = "studyforptd/soict-memory-task-fpv-frames"
# Segment tables
acts = pd.read_csv(hf_hub_download(repo, "labels/activity_segments.csv", repo_type="dataset"))
evts = pd.read_csv(hf_hub_download(repo, "labels/event_segments.csv", repo_type="dataset"))
# One video
video = hf_hub_download(repo, "videos/khoi-1708-1.mp4", repo_type="dataset")
```

Download only what you need (the full repo is ≈ 20 GB):

```bash
# labels + video metadata only
hf download studyforptd/soict-memory-task-fpv-frames --repo-type dataset \
  --include "labels/*" "videos/metadata.csv" --local-dir soict_fpv
# curated frames (~163 MB)
hf download studyforptd/soict-memory-task-fpv-frames --repo-type dataset \
  --include "selected_clear_frames/*" --local-dir soict_fpv
# everything, then unpack the frame shards
hf download studyforptd/soict-memory-task-fpv-frames --repo-type dataset --local-dir soict_fpv
cd soict_fpv && for t in all_frames/*/*.tar; do tar -xf "$t" -C all_frames; done
```

## Intended uses

* Temporal action localisation / segmentation on egocentric VR gameplay video.
* Detecting hesitation, freezing, distraction, task errors and recovery in goal-directed behaviour.
* Benchmarking video-language models on long first-person videos with sparse, interpretable events.
* Research on prospective memory and cognitive-load proxies derived from gameplay behaviour.

## Limitations and out-of-scope use

* **Small scale:** 12 sessions from a small group of participants; results will not generalise to other populations,
  environments or headsets without further validation.
* **Single scenario:** one VR supermarket and one family of shopping tasks. Overlay UI (list board, tooltip, cart panel,
  banners) is part of the signal, and many activity labels depend on it.
* **Sparse, subjective event labels:** event categories such as hesitation or distraction rely on human judgement;
  `certain = n` marks low-confidence rows. The two annotators sometimes disagree — that is retained deliberately.
* **Not a clinical tool:** the behaviour labels are research annotations, **not** a diagnosis or cognitive assessment of
  any person. Do not use this data to infer medical or psychological conditions.
* Do not use it to identify or profile the individual participants.

## Privacy & ethics

The recordings are headset screen captures of a virtual environment; they show the virtual scene and interface, not the
participants' real-world faces. Video, label and frame names nevertheless contain participant names and session dates.
Treat the data as **research-only and non-public** until the authors have confirmed participant consent and the
final release terms, and consider replacing names with anonymous IDs before any public release.

## License

To be confirmed by the authors (`license: other` in the metadata). Until a licence is added, treat the data as available
for non-commercial research use by the authors' team only.

## Citation

```bibtex
@misc{soict_memory_task_fpv,
  title  = {Gameplay-to-Behavior of Memory-Task First-Person Video with Resource-Controlled Latent Cognitive Interpretation},
  author = {Bourgoin, Enzo and Dao, Phuc T. and Phan, Dung T. and Vo, Quynh T.N. and Ngo, Man T.T. and Tran, Hieu D.
            and Nguyen, Giao T.H. and Phan, Minh H. and Ha, Huong T.T. and Quan, Tho T.},
  note   = {URA Research Group, HCMUT. Dataset: studyforptd/soict-memory-task-fpv-frames}
}
```

## Contact

Hugging Face: [`studyforptd`](https://huggingface.co/studyforptd)

	---
	pretty_name: SOICT Memory-Task FPV (VR Supermarket)
	license: other
	license_name: tbd-by-authors
	language:
	- en
	- vi
	task_categories:
	- video-classification
	- other
	tags:
	- first-person-video
	- egocentric
	- virtual-reality
	- behavior-analysis
	- temporal-action-localization
	- prospective-memory
	- cognitive-assessment
	size_categories:
	- 100K<n<1M
	configs:
	- config_name: videos
	data_files:
	- split: train
	path: videos/**
	- config_name: activity_segments
	data_files:
	- split: train
	path: labels/activity_segments.csv
	- config_name: event_segments
	data_files:
	- split: train
	path: labels/event_segments.csv
	---

	# SOICT Memory-Task FPV — VR Supermarket Dataset

	First-person-view (FPV) recordings of a memory-oriented shopping task in a virtual-reality supermarket, together with
	two independent sets of temporal annotations (what the participant is doing, and whether the way they do it is unusual)
	and the video frames extracted from every recording.

	The dataset accompanies the paper *Gameplay-to-Behavior of Memory-Task First-Person Video with Resource-Controlled Latent
	Cognitive Interpretation* (URA Research Group, HCMUT).

	> Access: this repository is currently private. File names contain participant identifiers (see
	> [Privacy & ethics](#privacy--ethics)).

	## At a glance

	\| \| \|
	\|---\|---\|
	\| Sessions (videos) \| 12 \|
	\| Participants \| 6 recorded names (several have more than one session) \|
	\| Total duration \| 3,856 s (≈ 64.3 min) \|
	\| Resolution / frame rate \| 1920 × 1080, ≈ 30 fps (HEVC / `.mp4`) \|
	\| Total frames \| 115,685 \|
	\| Device \| Standalone Meta Quest headset (screen recordings, `com.oculus.vrshell-*`) \|
	\| Annotation streams \| Activity (8 classes) and Event (5 classes) \|
	\| Annotators \| 2 independent passes per video \|
	\| Annotation language \| English (free-text `note` / `evidence`) \|

	## The task

	Participants wear a VR headset and complete a goal-directed shopping mission in a virtual supermarket. They must
	remember and act on a shopping list:

	1. read the item list on a virtual board (task-list consultation);
	2. walk the aisles and point a controller ray at items, which shows a tooltip with name and price (product inspection);
	3. grab the target item (product selection) and review the cart (cart interaction);
	4. react to yellow banners that substitute or add items mid-trial (task-change notice);
	5. pay at the cashier (checkout).

	Because the list has to be held in memory and can change, the recordings contain rich behaviour beyond simple task
	completion: searching, revisiting the list, hesitating, making mistakes and recovering from them.

	## Repository structure

	```
	.
	├── README.md
	├── videos/
	│ ├── <video_id>.mp4 # 12 full-length FPV recordings
	│ └── metadata.csv # video_id, session date, duration, frame count, size
	├── labels/
	│ ├── activity_segments.csv # all activity segments, both annotators (flat table)
	│ ├── event_segments.csv # all event segments, both annotators (flat table)
	│ ├── annotator_1/<video_id>_{activity,events}.json
	│ └── annotator_2/<video_id>_{activity,events}.json
	├── all_frames/
	│ └── <video_id>/<video_id>_partNNN.tar # every frame, packed in tar shards
	└── selected_clear_frames/
	├── by_video/<video_id>/ # sharp, representative frames (plain .jpg)
	└── by_activity/<activity>/ # the same kind of frames, grouped by activity
	```

	### Video IDs

	`<participant>-<ddmm>[-<n>]`: participant slug, session date (day + month, 2026), and a run index when a participant has
	several sessions on the same day.

	`khoi-1708-1`, `khoi-1708-2`, `khoi-1708-3`, `le-hien-2108-1`, `le-hien-2108-2`, `nguyen-an-2108-1`,
	`nguyen-an-2108-2`, `nguyen-kiet-2108-1`, `nguyen-kiet-2108-2`, `nguyen-lich-2108-1`, `nguyen-lich-2108-2`,
	`tran-quyen-1808`.

	The same ID is used for the video, its labels, and its frame folder.

	### Frames

	* `all_frames/<video_id>/<video_id>_partNNN.tar` — every frame of the video (`frame_000001.jpg`, `frame_000002.jpg`, …,
	numbered from 1, extracted at the native frame rate). Frames are packed into shards of ≤ 5,000 images because the Hub
	limits a folder to 10,000 files. Total ≈ 115.7k images.
	* `selected_clear_frames/` — a small curated subset (≈ 163 MB) of sharp, unblurred frames, kept as individual images,
	organised both by video and by activity. Handy for quick inspection or for building small training/validation sets.

	Frame index ↔ time: `time_sec ≈ (frame_index − 1) / fps`, with fps ≈ 30 (see `videos/metadata.csv` for exact
	per-video frame counts and durations).

	## Annotations

	Every video was labelled in two separate passes (activity, then event) by two independent annotators, following the
	protocol `ura-fpv-behavior-events-v3-two-branch`. Annotators do not see each other's work. Both versions are released as-is
	(`annotator_1`, `annotator_2`); they are not merged into a consensus. Timestamps are in **seconds from the start of the
	video**, to 0.1 s.

	Only intervals where something is observed are annotated — gaps mean "no measured action" / "normal behaviour"; they are
	not labelled.

	### Activity stream — "what is the participant doing?"

	File suffix `_activity.json`; flat table `labels/activity_segments.csv`.

	\| Field \| Description \|
	\|---\|---\|
	\| `start_sec`, `end_sec` \| Segment boundaries in seconds \|
	\| `activity` \| One of the 8 classes below \|
	\| `confidence` \| Annotator confidence in [0, 1] \|
	\| `note` \| Free-text justification \|

	\| Class \| Meaning \|
	\|---\|---\|
	\| `task_list_consultation` \| The shopping-list board is visible and readable \|
	\| `cart_interaction` \| The cart panel is open and covers most of the view \|
	\| `product_inspection` \| A tooltip (name, price) is shown while pointing at an item — looking, not yet taking \|
	\| `product_selection` \| An item is actually taken off the shelf / put in the cart \|
	\| `product_search` \| Scanning several shelves or areas for a target item \|
	\| `navigation` \| Walking or turning with no panel open \|
	\| `checkout` \| The payment screen is shown, or the participant is at the cashier \|
	\| `task_change_notice` \| A banner announces a task change (app-generated, not a participant action) \|

	Derived categories (re-consultation of the list, location revisits, "other task action", "no measured action") are not
	annotated by hand; they are inferred from the above.

	### Event stream — "is the way they do it unusual?"

	File suffix `_events.json`; flat table `labels/event_segments.csv`. Only abnormal intervals are listed.

	\| Field \| Description \|
	\|---\|---\|
	\| `start_sec`, `end_sec` \| Segment boundaries in seconds \|
	\| `behavior_state` \| One of the 5 classes below \|
	\| `certain` \| `y` / `n` — annotator certainty \|
	\| `evidence` \| Free-text description of what was observed \|

	\| Class \| Meaning \|
	\|---\|---\|
	\| `hesitation` \| Gaze or pointer wavers between options, or approaches then backs away from an item before deciding \|
	\| `freezing` \| View stays almost motionless for several seconds while work remains to be done \|
	\| `distraction` \| Attention lingers on something unrelated to shopping \|
	\| `task_error` \| A visible mistake against the task (wrong item, wrong quantity, premature checkout, …) \|
	\| `recovery` \| Correcting a previously recorded `task_error` \|

	Overlapping events are recorded as separate rows.

	### Flat-table columns

	`activity_segments.csv`: `video_id, annotator, start_sec, end_sec, activity, confidence, note`
	`event_segments.csv`: `video_id, annotator, start_sec, end_sec, behavior_state, certain, evidence`

	### Label statistics (per annotator)

	\| Activity \| Count (A1 + A2) \| \| Event \| Count (A1 + A2) \|
	\|---\|---:\|---\|---\|---:\|
	\| `product_inspection` \| 168 \| \| `hesitation` \| 82 \|
	\| `navigation` \| 162 \| \| `task_error` \| 58 \|
	\| `product_selection` \| 86 \| \| `recovery` \| 22 \|
	\| `cart_interaction` \| 68 \| \| `distraction` \| 16 \|
	\| `task_list_consultation` \| 54 \| \| `freezing` \| 6 \|
	\| `checkout` \| 34 \| \| \| \|
	\| `product_search` \| 26 \| \| \| \|
	\| `task_change_notice` \| 22 \| \| Total \| 184 \|
	\| Total \| 620 \| \| \| \|

	Each annotator contributes 310 activity segments and 92 event segments.

	## Usage

	```python
	from huggingface_hub import hf_hub_download
	import pandas as pd

	repo = "studyforptd/soict-memory-task-fpv-frames"

	# Segment tables
	acts = pd.read_csv(hf_hub_download(repo, "labels/activity_segments.csv", repo_type="dataset"))
	evts = pd.read_csv(hf_hub_download(repo, "labels/event_segments.csv", repo_type="dataset"))

	# One video
	video = hf_hub_download(repo, "videos/khoi-1708-1.mp4", repo_type="dataset")
	```

	Download only what you need (the full repo is ≈ 20 GB):

	```bash
	# labels + video metadata only
	hf download studyforptd/soict-memory-task-fpv-frames --repo-type dataset \
	--include "labels/*" "videos/metadata.csv" --local-dir soict_fpv

	# curated frames (~163 MB)
	hf download studyforptd/soict-memory-task-fpv-frames --repo-type dataset \
	--include "selected_clear_frames/*" --local-dir soict_fpv

	# everything, then unpack the frame shards
	hf download studyforptd/soict-memory-task-fpv-frames --repo-type dataset --local-dir soict_fpv
	cd soict_fpv && for t in all_frames//.tar; do tar -xf "$t" -C all_frames; done
	```

	## Intended uses

	* Temporal action localisation / segmentation on egocentric VR gameplay video.
	* Detecting hesitation, freezing, distraction, task errors and recovery in goal-directed behaviour.
	* Benchmarking video-language models on long first-person videos with sparse, interpretable events.
	* Research on prospective memory and cognitive-load proxies derived from gameplay behaviour.

	## Limitations and out-of-scope use

	* Small scale: 12 sessions from a small group of participants; results will not generalise to other populations,
	environments or headsets without further validation.
	* Single scenario: one VR supermarket and one family of shopping tasks. Overlay UI (list board, tooltip, cart panel,
	banners) is part of the signal, and many activity labels depend on it.
	* Sparse, subjective event labels: event categories such as hesitation or distraction rely on human judgement;
	`certain = n` marks low-confidence rows. The two annotators sometimes disagree — that is retained deliberately.
	* Not a clinical tool: the behaviour labels are research annotations, not a diagnosis or cognitive assessment of
	any person. Do not use this data to infer medical or psychological conditions.
	* Do not use it to identify or profile the individual participants.

	## Privacy & ethics

	The recordings are headset screen captures of a virtual environment; they show the virtual scene and interface, not the
	participants' real-world faces. Video, label and frame names nevertheless contain participant names and session dates.
	Treat the data as research-only and non-public until the authors have confirmed participant consent and the
	final release terms, and consider replacing names with anonymous IDs before any public release.

	## License

	To be confirmed by the authors (`license: other` in the metadata). Until a licence is added, treat the data as available
	for non-commercial research use by the authors' team only.

	## Citation

	```bibtex
	@misc{soict_memory_task_fpv,
	title = {Gameplay-to-Behavior of Memory-Task First-Person Video with Resource-Controlled Latent Cognitive Interpretation},
	author = {Bourgoin, Enzo and Dao, Phuc T. and Phan, Dung T. and Vo, Quynh T.N. and Ngo, Man T.T. and Tran, Hieu D.
	and Nguyen, Giao T.H. and Phan, Minh H. and Ha, Huong T.T. and Quan, Tho T.},
	note = {URA Research Group, HCMUT. Dataset: studyforptd/soict-memory-task-fpv-frames}
	}
	```

	## Contact

	Hugging Face: [`studyforptd`](https://huggingface.co/studyforptd)