---
pretty_name: "Lendt et al. 2024 — thigh-worn accelerometry, healthy adults"
license: cc-by-4.0
language:
  - en
tags:
  - accelerometry
  - wearable
  - human-activity-recognition
  - time-series-classification
  - thigh-worn
  - physical-activity
  - movement-behaviour
  - time-series
task_categories:
  - tabular-classification
  - other
size_categories:
  - 1M<n<10M
configs:
  - config_name: default
    data_files:
      - split: train
        path: "harmonized/*.parquet"
---

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

Thigh-worn accelerometry (SENS motion, 12.5 Hz, ±4 g) with
video-labelled activity ground truth in 35 healthy adults (lab) / 32
(free-living), covering both a laboratory protocol and ≈1 h of
annotated free-living per subject. Harmonized from the original
Zenodo release into per-subject parquet — one row per raw
accelerometer sample, with 1 Hz activity labels asof-joined on.

35 participants, 6.9 M samples, 40 % labelled, 61.3 h labeled.

## Source

- **Paper**: Lendt, C., Braun, T., Biallas, B., Froböse, I., & Johansson, P. J. (2024).
  Thigh-worn accelerometry: A comparative study of two no-code classification
  methods for identifying physical activity types.
  *International Journal of Behavioral Nutrition and Physical Activity*, *21*(1), 77.
  https://doi.org/10.1186/s12966-024-01627-1
- **Raw data**: Zenodo record [12704412](https://zenodo.org/records/12704412)
- **License**: CC-BY-4.0

## Protocol

- **Participants**: 38 recruited (30.8 ± 9.6 y, 53 % female); 35 included in lab, 32 in free-living — see [Harmonization notes](#harmonization-notes).
- **Sensor**: SENS motion (SENS Innovation, Denmark). Triaxial
  accelerometer, ±4 g, 12.5 Hz fixed. Adhesive patch on the right
  lateral thigh, 10 cm above the lateral epicondyle (harmonized data
  transformed to front-thigh frame — see [Axis orientation](#axis-orientation)).
- **Lab**: ≈46 min per subject across 14 sub-conditions of 3–5 min
  each — 6 activity types: `stand`; `sit`; `lie[supine]`, `lie[side]`,
  `lie[prone]`; treadmill `walk` at 0.5 / 0.8 / 1.2 m·s⁻¹; treadmill
  `run` at 1.8 / 2.3 / 2.8 m·s⁻¹; cycle ergometer `bicycle` at
  50 W/40 rpm, 75 W/60 rpm, 100 W/80 rpm. Transitions and waiting
  periods between sub-conditions are unlabelled.
- **Free-living**: ≈60 min per subject of unrestricted activity
  (cycling encouraged), video-annotated throughout.
- **Cohort-wide labelled reference**: 61.3 h (26.7 h lab + 34.6 h
  free-living).

### Ground-truth provenance

- Lab: timestamps logged manually via a custom R Shiny app at activity
  onsets.
- Free-living: chest-mounted GoPro Hero 4 Black (30 fps, 1920×1080),
  frame-by-frame annotation in ELAN v6.4 by a single rater. Inter-rater
  Kappa 0.95 on 5 double-coded videos.
- Reference resolution: **1 s** (start/end rounded).
- Sensor–video sync: 3 heel drops separated by 15 s rest. Lab sync via
  sensor flip.

## Schema

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

| column      | dtype                          | notes                                                                    |
|-------------|--------------------------------|--------------------------------------------------------------------------|
| subject     | `string`                       | pseudonymized alias, e.g. `happy-otter`                                  |
| timestamp   | `timestamp[ns, Europe/Berlin]` | ~80 ms cadence (12.5 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                             |
| condition   | `string` (nullable)            | `laboratory` \| `free-living`; null where `label` is null                |
| label       | `string` (nullable)            | activity label; null outside any labeled window                          |
| variant     | `string` (nullable)            | intensity tier or form variant; null for uncharacterised activities      |
| speed_kph   | `float64` (nullable)           | treadmill speed in km/h (walk/run lab stages only)                       |
| power_w     | `float64` (nullable)           | cycling power in W (bicycle lab stages only)                             |

Example rows:

```
subject      timestamp                   condition    label     variant           speed_kph  power_w
happy-otter  2022-06-01 09:14:32+02:00   laboratory   stand     null              null       null
happy-otter  2022-06-01 09:22:10+02:00   laboratory   walk      slow              1.8        null
happy-otter  2022-06-01 09:35:00+02:00   laboratory   bicycle   fast              null       100.0
happy-otter  2022-06-01 09:55:12+02:00   laboratory   lie       supine            null       null
happy-otter  2022-06-01 10:02:15+02:00   free-living  walk      null              null       null
happy-otter  2022-06-01 10:15:30+02:00   free-living  bicycle   pedalling-seated  null       null
happy-otter  2022-06-01 09:13:01+02:00   null         null      null              null       null
```

### Label vocabulary

`label` and `variant` are stored in separate columns. `variant` is null
for activities with no controlled intensity or characterised form.

**`label` values** (8):
`bicycle`, `lie`, `run`, `shuffle`, `sit`, `stairs`, `stand`, `walk`.

### Label glossary

Full meaning of every `(label, variant)` combination in the dataset:

| label | variant | condition | meaning | labeled min |
|---|---|---|---|---|
| `bicycle` | `slow` | laboratory | Cycle ergometer 50 W / 40 rpm | 104.5 |
| `bicycle` | `moderate` | laboratory | Cycle ergometer 75 W / 60 rpm | 104.7 |
| `bicycle` | `fast` | laboratory | Cycle ergometer 100 W / 80 rpm | 104.9 |
| `bicycle` | `pedalling-seated` | free-living | Cycling seated on saddle, actively pedalling | 237.2 |
| `bicycle` | `coasting` | free-living | Cycling without pedalling (downhill / momentum); seated/standing posture not distinguished in free-living annotation | 49.4 |
| `bicycle` | `pedalling-standing` | free-living | Cycling while standing on pedals | 10.3 |
| `lie` | `supine` | laboratory | Lying on back, face up | 104.9 |
| `lie` | `side` | laboratory | Lying on side | 104.9 |
| `lie` | `prone` | laboratory | Lying face down | 104.9 |
| `lie` | — | free-living | Lying; specific posture not recorded | 41.9 |
| `run` | `slow` | laboratory | Treadmill 6.48 km/h (1.8 m/s) | 104.9 |
| `run` | `moderate` | laboratory | Treadmill 8.28 km/h (2.3 m/s) | 104.9 |
| `run` | `fast` | laboratory | Treadmill 10.08 km/h (2.8 m/s) | 101.9 |
| `run` | — | free-living | Running; speed uncontrolled | 134.9 |
| `shuffle` | — | free-living | Standing with small continuous body movements (dynamic standing) | 76.0 |
| `sit` | — | laboratory | Sedentary; seated at rest | 174.8 |
| `sit` | — | free-living | Sedentary; seated at rest | 722.8 |
| `stairs` | — | free-living | Stair climbing | 13.1 |
| `stand` | — | laboratory | Upright, stationary standing | 174.7 |
| `stand` | — | free-living | Upright, stationary standing | 220.1 |
| `walk` | `slow` | laboratory | Treadmill 1.8 km/h (0.5 m/s) | 104.9 |
| `walk` | `moderate` | laboratory | Treadmill 2.88 km/h (0.8 m/s) | 104.9 |
| `walk` | `fast` | laboratory | Treadmill 4.32 km/h (1.2 m/s) | 104.9 |
| `walk` | — | free-living | Walking; speed uncontrolled | 569.3 |

Total labeled: 3,680 min / 61.3 h across 35 participants (lab) / 32 (free-living).

### Coverage

Every raw accelerometer sample is kept, even when no ground-truth
label covers it — typically in-lab transitions between sub-conditions
or pre/post-protocol margins. Each sample carries the most recent
ground-truth label whose 1 Hz timestamp falls at or before the
sample's timestamp, within 1 s; samples outside any labelled window
have `label = null` and `condition = null`.

Cohort-wide: **6.9 M samples, 40 % (2.76 M) labelled**. Per-subject
labelled fraction ranges from 17 % to 75 %. Filter with
`df[df['label'].notna()]` to keep only labelled rows.

### Axis orientation

Accelerometer values are **not** raw SENS motion output. They are transformed into 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 thigh. The physical sensor (SENS motion) was originally worn on the **right lateral thigh** (10 cm above the lateral epicondyle); two corrections transform the native lateral-thigh axes to the front-thigh convention:

Native lateral-thigh axes (standing upright): X=down, Y=forward (anterior), Z=left (medial).

1. **Negate x** — `acc_x ← -acc_x`. X=down → x=up.
2. **Rotate 90° about x** — `(acc_y, acc_z) ← (-acc_z, acc_y)`. Y=forward (anterior), Z=left (medial) → y=right, z=forward. This rotation accounts for the ~90° angular difference between lateral and anterior thigh placement.

The result is identical to hub-standard front-thigh data (consistent with the HARTH thigh sensor and all other datasets on this hub).

For raw SENS-native (lateral-thigh) values, ingest the upstream Zenodo record directly ([record 12704412](https://zenodo.org/records/12704412)).

### Harmonization notes

Participant exclusions: 35 of 38 included — 3 dropped for missing
upstream annotations; a further 3 dropped from free-living for
sensor detachment / poor video (free-living n = 32).

Per-sample exclusions:

- `merry-ocelot`: `run[fast]` segments dropped (author-flagged invalid).

`clever-lynx`'s free-living annotations are merged from two upstream folders
(both windows from the same continuous recording).

## Use

Intended for human activity recognition (HAR) from thigh-worn
accelerometry — predicting `label` (8-class) from the `acc_x/y/z`
signal. The original paper collapses these into 5 classes: sedentary,
standing, walking, running, cycling.

The standard evaluation protocol is **leave-one-subject-out (LOSO)
cross-validation**: 35 folds for lab data, 32 for free-living (3
subjects have lab data only). Avoid random row-level splits — they
leak temporal and subject-level context across folds.

## Loading

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

Load all subjects into one table:

```python
from datasets import load_dataset

ds = load_dataset("josefheidler/har_adults_2024-lendt")
df = ds["train"].to_pandas()
```

Load a single subject with pandas:

```python
import pandas as pd

df = pd.read_parquet(
    "hf://datasets/josefheidler/har_adults_2024-lendt/harmonized/happy-otter.parquet"
)
```

Load all subjects individually (preserving identity):

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

fs = HfFileSystem()
files = fs.glob("datasets/josefheidler/har_adults_2024-lendt/harmonized/*.parquet")
dfs = {
    f.split("/")[-1].replace(".parquet", ""): pd.read_parquet(f"hf://{f}")
    for f in files
}
```

## Citation

Lendt, C., Braun, T., Biallas, B., Froböse, I., & Johansson, P. J. (2024). Thigh-worn accelerometry: A comparative study of two no-code classification methods for identifying physical activity types. *International Journal of Behavioral Nutrition and Physical Activity*, *21*(1), 77. https://doi.org/10.1186/s12966-024-01627-1