---
pretty_name: "HAR70+ — Older Adult Activity Recognition (Thigh + Back Accelerometry)"
license: cc-by-4.0
language:
  - en
tags:
  - accelerometry
  - wearable
  - human-activity-recognition
  - time-series-classification
  - thigh-worn
  - physical-activity
  - movement-behaviour
  - time-series
  - older-adults
  - free-living
task_categories:
  - tabular-classification
  - other
size_categories:
  - 1M<n<10M
configs:
  - config_name: default
    data_files:
      - split: train
        path: "harmonized/*.parquet"
---

# HAR70+ — Older Adult Activity Recognition (Thigh + Back Accelerometry)

Two Axivity AX3 accelerometers (right thigh and lower back, 50 Hz) worn during
semi-structured free-living sessions by 18 older adults (age 70–95) in Norway.
Harmonized from the [NTNU HAR70+ release](https://github.com/ntnu-ai-lab/harth-ml-experiments/tree/main/har70plus)
into per-subject parquet — one row per raw sample. 18 participants, 2.26 M samples,
100% labelled, 12.6 h labeled.

## Source

- **Paper**: Ustad, A., Logacjov, A., Trollebø, S. Ø., Thingstad, P., Vereijken, B.,
  Bach, K., & Mork, P. J. (2023). Validation of an Activity Type Recognition Model
  Classifying Daily Physical Behavior in Older Adults. *Sensors*, 23(1), 130.
  https://doi.org/10.3390/s23010130
- **Raw data**: https://github.com/ntnu-ai-lab/harth-ml-experiments/tree/main/har70plus
- **License**: CC-BY-4.0

## Protocol

- **Participants**: 18 older adults, age 70–95 (five used walking aids), recruited from
  a rehabilitation clinic in Trondheim, Norway.
- **Sensor**: Axivity AX3 triaxial accelerometer (±8 g, 50 Hz), placed on the right
  thigh (≈10 cm above the kneecap) and lower back (≈3rd lumbar vertebra), USB connector
  pointing down.
- **Protocol**: ≈40-minute semi-structured free-living session per participant.
  Participants performed their usual daily activities while a trained observer accompanied
  them and annotated behaviour in real time. Seven activity types were annotated: walking,
  shuffling, stair climbing (ascending and descending), standing, sitting, and lying.

### Ground-truth provenance

Activities were annotated by a trained observer accompanying each participant during the
session using a mobile logging application. Timestamps were recorded manually. No video
annotation was used; inter-rater reliability is not reported in the paper. All 2,259,597
samples are labeled — there are no unlabeled transition windows.

## Schema

All timestamps are tz-aware **Europe/Oslo**.

| column       | dtype                       | notes                                                                      |
|--------------|-----------------------------|----------------------------------------------------------------------------|
| subject      | string                      | participant alias (e.g. `agile-anteater`)                                  |
| timestamp    | datetime64[ns, Europe/Oslo] | real wall-clock time of the recording, tz-aware Europe/Oslo                |
| thigh_acc_x  | float64                     | right-thigh acceleration, corrected frame (see Axis orientation), units = g |
| thigh_acc_y  | float64                     | right-thigh acceleration, corrected frame, units = g                        |
| thigh_acc_z  | float64                     | right-thigh acceleration, corrected frame, units = g                        |
| back_acc_x   | float64                     | lower-back acceleration, corrected frame (see Axis orientation), units = g  |
| back_acc_y   | float64                     | lower-back acceleration, corrected frame, units = g                         |
| back_acc_z   | float64                     | lower-back acceleration, corrected frame, units = g                         |
| label        | string (nullable)           | activity base (`walk`, `shuffle`, `stairs`, `stand`, `sit`, `lie`)          |
| variant      | string (nullable)           | direction modifier (`ascending`, `descending`); null for most activities    |

**Example rows**

```
subject          timestamp                       thigh_acc_x  thigh_acc_y  thigh_acc_z  back_acc_x  back_acc_y  back_acc_z  label   variant
agile-anteater   2021-03-24 14:42:03.839+01:00   0.980       -0.112        0.048         0.999       0.063       0.141      stand   <NA>
agile-anteater   2021-03-24 14:43:32.599+01:00   0.987       -0.219       -0.000         1.042       0.083      -0.181      walk    <NA>
agile-anteater   2021-03-24 14:53:31.280+01:00   0.982       -0.103        0.196         0.989      -0.094      -0.261      stairs  descending
```

### Label vocabulary

`label` and `variant` are stored in separate columns. `variant` is null for all
activities except `stairs`.

| label   | variant    | meaning                           | labeled min |
|---------|------------|-----------------------------------|-------------|
| lie     | —          | lying                             | 67.7        |
| shuffle | —          | shuffling / slow side-step        | 22.0        |
| sit     | —          | sitting                           | 161.2       |
| stairs  | ascending  | stair climbing upward             | 1.5         |
| stairs  | descending | stair climbing downward           | 1.7         |
| stand   | —          | standing                          | 139.4       |
| walk    | —          | walking                           | 359.8       |

Total labeled: 753.3 min / 12.6 h across 18 participants.

### Coverage

All 2,259,597 rows are labeled — this dataset has no unlabeled transition windows.
Filter to labeled rows (no-op but safe to use consistently across the hub):

```python
df_labeled = df[df["label"].notna()]
```

### Axis orientation

All accelerometer columns use the **hub standard axis convention**, shared across all
datasets on this hub:

- **x** runs along the body segment toward the head — reads +1 g when the person stands
  upright, drops toward 0 when they lie down.
- **y** points to the person's right — positive when tilting right, negative when tilting left.
- **z** points forward — positive when leaning or stepping forward, negative when leaning backward.
- At rest standing upright: x ≈ +1 g, y ≈ 0, z ≈ 0.

**Thigh sensor** (right thigh, ≈10 cm above the kneecap, USB connector pointing down):

Native frame (standing): x=down, y=right, z=backward. Correction: negate x and z (keep y).
Hub frame: x=up, y=right, z=forward.

**Back sensor** (lower back, ≈3rd lumbar vertebra, USB connector pointing down):

Native frame (standing): x=down, y=left, z=forward. Correction: negate x and y (keep z).
Hub frame: x=up, y=right, z=forward.

The raw native-frame values are available in the source CSV files at the GitHub link above.

### Harmonization notes

No participants were excluded. All 18 participants from the GitHub release are included.

## Use

Intended for human activity recognition (HAR) benchmarking on older-adult populations.
Unlike the HARTH adult dataset, HAR70+ contains only free-living sessions with no
controlled laboratory stages, making it suitable for evaluating model generalization
to natural movement in this cohort — including shuffling gait patterns typical of older
adults. Evaluate with leave-one-subject-out (LOSO) cross-validation; avoid random
row-level splits which would leak temporal context across subjects.

## Loading

```python
from datasets import load_dataset

ds = load_dataset("josefheidler/har_older-adults_2023-harth")
df = ds["train"].to_pandas()
```

Load a single subject:

```python
import pandas as pd

df = pd.read_parquet(
    "hf://datasets/josefheidler/har_older-adults_2023-harth/harmonized/agile-anteater.parquet"
)
```

## Citation

Ustad, A., Logacjov, A., Trollebø, S. Ø., Thingstad, P., Vereijken, B., Bach, K., & Mork, P. J.
(2023). Validation of an Activity Type Recognition Model Classifying Daily Physical Behavior
in Older Adults. *Sensors*, 23(1), 130. https://doi.org/10.3390/s23010130