---
pretty_name: "Lendt et al. 2024 — thigh-worn accelerometry + energy expenditure, healthy adults"
license: cc-by-4.0
language:
  - en
tags:
  - accelerometry
  - wearable
  - energy-expenditure
  - indirect-calorimetry
  - time-series
  - human-activity-recognition
  - thigh-worn
  - physical-activity
  - movement-behaviour
task_categories:
  - tabular-classification
  - other
size_categories:
  - 100M<n<1B
configs:
  - config_name: default
    data_files:
      - split: train
        path: "harmonized/train/*.parquet"
      - split: test
        path: "harmonized/test/*.parquet"
  - config_name: participants
    data_files:
      - split: participants
        path: "participants.csv"
---

# Lendt et al. 2024 — thigh-worn accelerometry + energy expenditure, healthy adults

Thigh-worn accelerometry (Axivity AX6, 100 Hz, ±8 g) paired with
indirect-calorimetry energy expenditure in 69 healthy adults across a
standardized laboratory protocol of sitting, standing, walking (4–5
speeds), running (4–5 speeds), and cycling (3–5 power levels).
Harmonized from the original Zenodo release into per-subject parquet —
one row per raw accelerometer sample, with 1 Hz activity labels and
steady-state energy expenditure asof-joined on. 106 M samples total,
33 % labelled (97.9 h labeled out of 294.9 h total).

Sibling cohort to `lendt_adults_2024` (different device, different
subjects, no free-living condition, adds EE signal).

## Source

- **Paper**: Lendt, C., Hansen, N., Froböse, I., & Stewart, T. (2024).
  Composite activity type and stride-specific energy expenditure
  estimation model for thigh-worn accelerometry.
  *International Journal of Behavioral Nutrition and Physical Activity*, *21*(1), 99.
  https://doi.org/10.1186/s12966-024-01646-y
- **Raw data**: Zenodo record [13477128](https://zenodo.org/records/13477128)
- **License**: CC-BY-4.0

## Protocol

- **Participants**: 69 healthy adults (25.2 ± 5.8 y, 49 % female).
  Divided by the authors into a training subset (n = 49) and a
  validation subset (n = 20). The dataset includes the full cohort;
  the authors' split is preserved in the `split` column.
- **Sensor**: Axivity AX6 (Axivity Ltd, Newcastle upon Tyne, UK).
  Triaxial accelerometer, ±8 g, 100 Hz. Pre-session static calibration
  via rectangular plastic cube (6 axes × 10 s); this window is labeled
  `calibration` in the harmonized data. Worn on the **lateral side
  of the dominant thigh**, midway between hip and knee, taped and
  secured with an elastic bandage (harmonized data transformed to
  front-thigh frame — see [Axis orientation](#axis-orientation)).
- **Protocol**: standardized laboratory sequence — sitting → standing →
  walking (4–5 speeds incl. 6 % incline) → running (4–5 speeds incl.
  6 % incline) → cycling (3–5 power levels at 60–80 rpm self-selected).
  Each stage lasts **6 min** with 5 min rest between conditions. The
  training and validation groups use slightly different speed/power
  lists — see `speed_kph` and `power_w` column values.
- **Reference EE**: Cortex Metalyzer 3B (Cortex Biophysik, Leipzig).
  Breath-by-breath VO2/VCO2 converted to kcal/min via Weir's equation.
  **Steady-state EE averaged over the last 3 min of each 6-min stage.**

### Ground-truth provenance

- Stage start times were recorded by researchers during data collection.
- Each stage lasted **6 min**; the 5-min inter-stage rest is unlabeled.
- Reference resolution: **1 s**.

## Schema

All timestamps are tz-aware **Europe/Berlin** (recording-site local
time).

| column          | dtype                          | notes                                                          |
|-----------------|--------------------------------|----------------------------------------------------------------|
| subject         | `string`                       | pseudonymized alias, e.g. `happy-alpaca`                            |
| split           | `string`                       | `train` (n=49) or `test` (n=20)                   |
| timestamp       | `timestamp[ns, Europe/Berlin]` | ~10 ms cadence (100 Hz)                                             |
| acc_x           | `float64`                      | hub standard frame (x=up), units = g                             |
| acc_y           | `float64`                      | hub standard frame (y=right), units = g                          |
| acc_z           | `float64`                      | hub standard frame (z=forward), units = g                        |
| label           | `string` (nullable)            | activity label; null outside any labeled window                     |
| variant         | `string` (nullable)            | intensity tier (`slow` · `moderate` · `fast`); null for sit/stand  |
| speed_kph       | `float64` (nullable)           | treadmill speed in km/h (walk/run only)                             |
| power_w         | `float64` (nullable)           | cycling power in W (bicycle only)                                   |
| incline_pct     | `float64` (nullable)           | treadmill incline in % (6.0 for incline stages, null otherwise)     |
| vo2             | `float64` (nullable)           | L/min, mean over last 3 min of stage                                |
| vco2            | `float64` (nullable)           | L/min, mean over last 3 min of stage                                |
| ee_kcal_min     | `float64` (nullable)           | Weir (3.9·VO2 + 1.1·VCO2), kcal/min                                |
| ee_kcal_min_kg  | `float64` (nullable)           | kcal/kg/min — paper's primary EE unit                               |
| met             | `float64` (nullable)           | metabolic equivalents (≈ kcal/kg/hour)                              |

Example rows:

```
subject       split  timestamp                   label        variant   speed_kph  power_w  incline_pct  ee_kcal_min
happy-alpaca  train  2023-09-07 08:45:00+02:00   calibration  null      null       null     null         null
happy-alpaca  train  2023-09-07 09:14:32+02:00   stand        null      null       null     null         1.71
happy-alpaca  train  2023-09-07 09:22:10+02:00   walk         slow      2.1        null     null         3.37
happy-alpaca  train  2023-09-07 09:55:00+02:00   walk         moderate  3.7        null     6.0          5.84
happy-alpaca  train  2023-09-07 10:15:00+02:00   bicycle      fast      null       150.0    null         10.32
happy-alpaca  train  2023-09-07 08:50:00+02:00   null         null      null       null     null         null
```

### Label vocabulary

**`label`** (6 values): `calibration`, `sit`, `stand`, `walk`, `run`, `bicycle`.

**`variant`** — intensity tier for walk/run/bicycle; null for sit/stand:

| variant | walk (km/h) | run (km/h) | bicycle (W) |
|---|---|---|---|
| `slow` | 2.1 (train) · 2.5 (val) | 7.5 (train) | 30 |
| `moderate` | 2.9–3.7 (train) · 3.3 (val) | 8.5–9.0 | 60 |
| `fast` | 4.5 (train) · 4.1 (val) | 9.5–10.5 (train) · 10.0 (val) | 90–150 |

Multiple protocol stages can share the same `variant` — the exact
value is always in `speed_kph` or `power_w`. Incline stages (6 %
treadmill gradient) share the base variant of their speed but have
`incline_pct = 6.0`.

All unique `(label, variant, speed_kph, power_w, incline_pct)` combinations present in the data:

| label | variant | speed_kph | power_w | incline_pct | group |
|---|---|---|---|---|---|
| `sit` | — | — | — | — | test |
| `stand` | — | — | — | — | both |
| `walk` | `slow` | 2.1 | — | — | train |
| `walk` | `slow` | 2.5 | — | — | test |
| `walk` | `moderate` | 2.9 | — | — | train |
| `walk` | `moderate` | 3.3 | — | — | test |
| `walk` | `moderate` | 3.3 | — | 6.0 | test |
| `walk` | `moderate` | 3.7 | — | — | train |
| `walk` | `moderate` | 3.7 | — | 6.0 | train |
| `walk` | `fast` | 4.1 | — | — | test |
| `walk` | `fast` | 4.5 | — | — | train |
| `run` | `slow` | 7.5 | — | — | train |
| `run` | `moderate` | 8.0 | — | — | test |
| `run` | `moderate` | 8.0 | — | 6.0 | test |
| `run` | `moderate` | 8.5 | — | — | train |
| `run` | `moderate` | 9.0 | — | — | test |
| `run` | `fast` | 9.5 | — | — | train |
| `run` | `fast` | 9.5 | — | 6.0 | train |
| `run` | `fast` | 10.0 | — | — | test |
| `run` | `fast` | 10.5 | — | — | train |
| `bicycle` | `slow` | — | 30 | — | both |
| `bicycle` | `moderate` | — | 60 | — | both |
| `bicycle` | `fast` | — | 90 | — | both |
| `bicycle` | `fast` | — | 120 | — | train |
| `bicycle` | `fast` | — | 150 | — | train |

### Label glossary

Full meaning of every `(label, variant)` combination in the dataset.
The exact numeric intensity is always in `speed_kph`, `power_w`, or
`incline_pct`; the variant is the intensity tier.

| label | variant | meaning | labeled min |
|---|---|---|---|
| `bicycle` | `slow` | Cycle ergometer 30 W | 384.1 |
| `bicycle` | `moderate` | Cycle ergometer 60 W | 384.1 |
| `bicycle` | `fast` | Cycle ergometer 90–150 W (training: 90/120/150; validation: 90) | 904.2 |
| `calibration` | — | Pre-session static calibration: 6 sensor orientations × 10 s on a rectangular plastic cube, immediately before the activity protocol | 97.1 |
| `run` | `slow` | Treadmill 7.5 km/h (training protocol only) | 282.0 |
| `run` | `moderate` | Treadmill 8.5 km/h (train) · 8.0–9.0 km/h (val); includes an 8.0 km/h + 6 % incline stage | 620.5 |
| `run` | `fast` | Treadmill 9.5–10.5 km/h (train) · 10.0 km/h (val); includes a 9.5 km/h + 6 % incline stage | 862.1 |
| `sit` | — | Seated at rest (validation protocol only; training protocol starts from standing) | 118.2 |
| `stand` | — | Upright stationary standing (both protocols) | 404.0 |
| `walk` | `slow` | Treadmill 2.1 km/h (train) · 2.5 km/h (val) | 405.9 |
| `walk` | `moderate` | Treadmill 2.9–3.7 km/h (train) · 3.3 km/h (val); includes a 6 % incline stage | 1103.5 |
| `walk` | `fast` | Treadmill 4.5 km/h (train) · 4.1 km/h (val) | 407.8 |

Total activity labeled: 5876.4 min / 97.9 h across 69 participants (calibration excluded).

### Coverage

Every raw accelerometer sample is kept, even outside labeled windows.
Rows outside any labeled stage have `label = null` and all derived
columns null — typically the 5-min inter-stage rest periods and the
pre/post-protocol margins.

Each sample carries the most recent stage label whose 1 Hz timestamp
falls at or before the sample's timestamp, within 1 s. Samples outside
any labeled window get `label = null`.

The `calibration` label marks the pre-session static calibration block
(≈90 s per subject, immediately before the activity protocol). Exclude them from HAR or EE analysis with
`df[~df['label'].isin(['calibration', None])]` or keep them if your
pipeline needs a static reference period.

The EE columns (`vo2`, `vco2`, `ee_kcal_min`, `ee_kcal_min_kg`, `met`)
are **stage-level steady-state summaries** — constant across all
samples within a stage, not breath-by-breath.

### Axis orientation

Accelerometer values use the **hub standard axis convention**, shared across all datasets on this hub:

- **acc_x** runs along the thigh toward the head — reads +1 g when the person stands upright.
- **acc_y** points to the person's right — positive when tilting right, negative when tilting left.
- **acc_z** points forward — positive when leaning or stepping forward, negative when leaning backward.
- At rest standing upright: acc_x ≈ +1 g, acc_y ≈ 0, acc_z ≈ 0.

Harmonized accelerometer data is presented in a **front-thigh frame** — as if the sensor were mounted on the anterior surface of the dominant thigh. The physical sensor (Axivity AX6) was originally worn on the **lateral side of the dominant thigh** (midway between hip and knee); corrections transform the native lateral-thigh axes to the front-thigh convention.

Native lateral-thigh axes depend on which thigh (standing upright):

| axis | right thigh (original placement) | left thigh (original placement) |
|------|----------------------------------|----------------------------------|
| x    | down                             | down                             |
| y    | right (laterally outward)        | left (laterally outward)         |
| z    | backward                         | forward                          |

The left-thigh z is forward (not backward) because the mirrored placement rotates the device ≈180° around the long axis, flipping y and z simultaneously.

Correction applied to reach front-thigh (hub) frame — x=up, y=right, z=forward:

- **Right-dominant**: negate x and z — `acc_x ← -acc_x`, `acc_z ← -acc_z`.
- **Left-dominant**: negate x and y — `acc_x ← -acc_x`, `acc_y ← -acc_y`.

`plucky-python` and `royal-robin` have `dominant_leg` overridden to `right` per the upstream `code/main.ipynb` correction, so they receive the right-dominant correction path.

For raw Axivity-native (lateral-thigh) values, ingest the upstream Zenodo record directly ([record 13477128](https://zenodo.org/records/13477128)).

> **Gravitational calibration not applied.** 
> No autocalibration (e.g. sphere-fitting) has been
> performed on the harmonized data. The `calibration` label marks the
> 6-position static calibration block recorded before each session —
> these rows can be used to derive and apply a gravitational calibration
> if your downstream analysis requires it.

### Harmonization notes

All 69 participants have both raw accelerometry and CPET data — no
subjects excluded.

Participants `plucky-python` and `royal-robin` have `dominant_leg` overridden to `right` in the
participant manifest (`config.yaml`), matching the upstream `code/main.ipynb` correction — they follow the right-dominant (no y-negation) path.

Per-participant demographics (split, gender, age, dominant_leg, height,
weight, bmi, leg_length) are available in `participants.csv` at the
repository root, also loadable as the `participants` dataset config —
see [Loading](#loading).

Label format uses bare base labels only (`stand`, `sit`, `walk`, `run`,
`bicycle`). Stage intensity is preserved in the `variant`, `speed_kph`,
`power_w`, and `incline_pct` columns rather than `base[modifier]` encoding
— see [Label vocabulary](#label-vocabulary).

## Use

Intended for energy expenditure estimation and human activity
recognition from thigh-worn accelerometry. Predict `ee_kcal_min` or
`met` from the `acc_x/y/z` signal, or `label` for HAR.

With 69 subjects split into a predefined train/validation partition
(the `split` column contains `train` or `test`), the standard
evaluation protocol respects this split: **train on the train split, evaluate
on the test split**.

## Loading

Each subject is stored as a separate parquet file under `harmonized/`.
Filenames are pseudonymized aliases (`happy-alpaca.parquet`,
`brave-bison.parquet`, …). Every row carries a `subject` column with
the same alias, so identity is preserved when loading all files together.

Load train and test splits:

```python
from datasets import load_dataset

ds = load_dataset("josefheidler/har_ee_adults_2024-lendt")
train_df = ds["train"].to_pandas()
test_df = ds["test"].to_pandas()
```

Load all subjects into one table (both splits combined):

```python
import pandas as pd
import datasets

ds = load_dataset("josefheidler/har_ee_adults_2024-lendt")
df = pd.concat([ds["train"].to_pandas(), ds["test"].to_pandas()], ignore_index=True)
```

Load participant demographics:

```python
from datasets import load_dataset

participants = load_dataset(
    "josefheidler/har_ee_adults_2024-lendt",
    name="participants",
)["participants"].to_pandas()
```

Load a single subject with pandas:

```python
import pandas as pd

df = pd.read_parquet(
    "hf://datasets/josefheidler/har_ee_adults_2024-lendt/harmonized/train/happy-alpaca.parquet"
)
```

## Citation

Lendt, C., Hansen, N., Froböse, I., & Stewart, T. (2024). Composite activity type and stride-specific energy expenditure estimation model for thigh-worn accelerometry. *International Journal of Behavioral Nutrition and Physical Activity*, *21*(1), 99. https://doi.org/10.1186/s12966-024-01646-y