# Har Wearable Sensor Eval

> Evaluates deep learning architectures (CNNs, LSTMs, DNNs) for frame-by-frame human activity recognition using wearable sensor time-series data. It probes the models' capacity to capture temporal dependencies and generalize across diverse domains (kitchen gestures, lifestyle/exercise, and medical gait analysis) while handling severe class imbalance. Use when the user wants to benchmark on Opportunity, PAMAP2, Daphnet Gait, or asks about evaluating this task. Reports mean f1-score.

- Skill: `qhjqhj00/har-wearable-sensor-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/har-wearable-sensor-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/har-wearable-sensor-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/har-wearable-sensor-eval

---


# har-wearable-sensor-eval

> Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables — Hammerla et al. (2016) (arXiv:1604.08880, 2016)

## What this evaluates

Evaluates deep learning architectures (CNNs, LSTMs, DNNs) for frame-by-frame human activity recognition using wearable sensor time-series data. It probes the models' capacity to capture temporal dependencies and generalize across diverse domains (kitchen gestures, lifestyle/exercise, and medical gait analysis) while handling severe class imbalance.

## Datasets

- **Opportunity** — total ?; splits: train (-1), val (-1), test (-1)
- **PAMAP2** — total ?; splits: train (-1), val (-1), test (-1)
- **Daphnet Gait** — total ?; splits: train (-1), val (-1), test (-1)

## Metrics

- `mean f1-score` **(primary)** — range: [0, 1]
  - Unbiased average of per-class F1 scores: $F_{m}=\frac{2}{|c|}\sum_{c}\frac{\text{prec}_{c}\times\text{recall}_{c}}{\text{prec}_{c}+\text{recall}_{c}}$. Computed independently of class distribution to handle dataset bias.
- `weighted f1-score` — range: [0, 1]
  - Class-proportion-weighted average of per-class F1 scores: $F_{w}=2\sum_{c}\frac{N_{c}}{N_{\text{total}}}\frac{\text{prec}_{c}\times\text{recall}_{c}}{\text{prec}_{c}+\text{recall}_{c}}$, where $N_{c}$ is samples in class $c$ and $N_{\text{total}}$ is total samples. Used for direct comparison with prior state-of-the-art.

## Input / output format

**Input**: Sliding windows of multi-axis wearable sensor data (accelerometer/IMU) sampled at dataset-specific frequencies (30Hz, 33.3Hz, or 32Hz). Window duration and overlap vary by dataset (e.g., 1s/50% overlap for Opportunity and Daphnet Gait; 5.12s/78% overlap for PAMAP2).

**Output**: Frame-by-frame activity or gesture classification labels corresponding to the input window.

## Scoring recipe

```python
def compute_mean_f1(predictions, gold, num_classes):
    precisions = []
    recalls = []
    for c in range(num_classes):
        tp = sum(p == c and g == c for p, g in zip(predictions, gold))
        fp = sum(p == c and g != c for p, g in zip(predictions, gold))
        fn = sum(p != c and g == c for p, g in zip(predictions, gold))
        prec = tp / (tp + fp) if (tp + fp) > 0 else 0.0
        rec = tp / (tp + fn) if (tp + fn) > 0 else 0.0
        precisions.append(prec)
        recalls.append(rec)
    f1_scores = [2 * p * r / (p + r) if (p + r) > 0 else 0.0 for p, r in zip(precisions, recalls)]
    return sum(f1_scores) / num_classes
```

## Common pitfalls

- Datasets are highly imbalanced; using standard accuracy instead of mean F1 will severely misrepresent model performance.
- Splits are strictly subject-dependent (e.g., runs 4-5 from subjects 2-3 for Opportunity test). Random or subject-independent splits will invalidate comparisons with reported baselines.
- Windowing parameters (duration and overlap) differ across datasets and must be replicated exactly to match reported frame counts and temporal resolution.

## Evidence (verbatim from paper)

> As the datasets utilised in this work are highly biased we require performance metrics that are independent of the class distribution. We opted to estimate the mean f1-score: $F_{m}=\frac{2}{|c|}\sum_{c}\frac{\text{prec}_{c}\times\text{recall}_{c}}{\text{prec}_{c}+\text{recall}_{c}}$ (1) Related work has previously used the weighted f1-score as primary performance metric (for Opportunity). In order to compare our results to the state-of-the-art we estimate the weighted f1-score: $F_{w}\=2\sum_{c}\frac{N_{c}}{N_{\text{total}}}\frac{\text{prec}_{c}\times\text{recall}_{c}}{\text{prec}_{c}+\text{recall}_{c}},$ (2) where $N_{c}$ is the number of samples in class $c$, and $N_{\text{total}}$ is the total number of samples.

## Citation

```bibtex
@misc{hammerla2016deep,
  title={Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables},
  author={Hammerla et al. (2016)},
  year={2016},
  note={arXiv:1604.08880}
}
```

- arXiv: 1604.08880