README.md

---
license: mit
language:
- en
task_categories:
- time-series-forecasting
task_ids:
- univariate-time-series-forecasting
size_categories:
- n<1K
tags:
- tsfile
- timeseries
- modality:timeseries
- format:tsfile
- health
- sleep
- fitbit
modality: timeseries
pretty_name: Fitbit Sleep Score Data (TsFile format)
configs:
- config_name: default
  data_files:
  - split: train
    path: "sleep_score_fitbit.tsfile"
---

# Fitbit Sleep Score Data (TsFile format)

The Fitbit Sleep Score dataset contains timestamped sleep metrics collected
from one consenting individual's Fitbit Versa 4 device. It is a case study for
sleep analysis, health monitoring, and wellness technology rather than a
population-level cohort.

Modalities: Time-series

## Source and scale

- Original dataset: [aai530-group6/sleep-score-fitbit](https://huggingface.co/datasets/aai530-group6/sleep-score-fitbit)
- Kaggle source named by the original card: [Fitbit Sleep Score Data](https://www.kaggle.com/datasets/mbalos/fitbit-sleep-score-data/data)
- Source revision: b99745afef52c32b608edba1acf25f3e0f6089c4
- One train file with 291 observations, one timestamp per sleep record.
- Source time range: 2022-12-23T09:20:30Z through 2023-11-18T07:59:00Z.

## TsFile schema

| Column | Role | TsFile type | Source meaning |
|---|---|---|---|
| Time | TIME | INT64 (ms) | timestamp, parsed as UTC |
| overall_score | FIELD | INT64 | Aggregate sleep score (up to 100) |
| revitalization_score | FIELD | INT64 | Rejuvenating quality score |
| deep_sleep_in_minutes | FIELD | INT64 | Deep sleep duration in minutes |
| resting_heart_rate | FIELD | INT64 | Average resting heart rate |
| restlessness | FIELD | DOUBLE | Restlessness measure |

There are no device TAG columns because the source contains one individual's
single series.

## Conversion notes

- ISO-8601 timestamps are interpreted in UTC and represented losslessly as
  integer milliseconds in Time; the original timestamp text is not duplicated
  as a FIELD.
- All five measurements and all 291 rows are retained and sorted by Time.
- No demographic or additional subject information is inferred from the source.

## Files and usage

- sleep_score_fitbit.tsfile

~~~python
from pathlib import Path
from tsfile import TsFileReader

path = Path("sleep_score_fitbit.tsfile")
with TsFileReader(str(path)) as reader:
    table_name = next(iter(reader.get_all_table_schemas()))
    with reader.query_table(table_name, ["overall_score", "restlessness"], batch_size=512) as result:
        batch = result.read_arrow_batch()
        if batch is not None:
            print(batch.to_pandas().head())
~~~

## License and attribution

The source dataset is released under the MIT license. Please respect the
source card's privacy and ethical-use notes for this single-person case study.
See the [original dataset card](https://huggingface.co/datasets/aai530-group6/sleep-score-fitbit).

## Usage

Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file:

```python
from pathlib import Path
from tsfile import TsFileReader

path = Path("sleep_score_fitbit.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    columns = [column.get_column_name() for column in table.get_columns()]
    print("columns:", columns)
    field_names = [
        column.get_column_name()
        for column in table.get_columns()
        if column.get_column_name() not in {"Time", "time"}
    ]
    if field_names:
        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
            batch = result.read_arrow_batch()
            if batch is not None:
                print(batch.to_pandas().head())
```