README.md

---
license: mit
pretty_name: Home Monitoring System
task_categories:
- tabular-classification
language:
- en
tags:
- smart-home
- home-monitoring
- iot
- sensor-data
- time-series
- tabular
- anomaly-detection
- activity-monitoring
- energy-monitoring
- ambient-assisted-living
size_categories:
- 1K<n<10K
---

# Home Monitoring System

## Dataset Summary

**Home Monitoring System** is a tabular smart-home sensor dataset for research and prototyping in home monitoring, Internet of Things (IoT), activity-aware systems, energy monitoring, and baseline anomaly-detection workflows.

The dataset contains **5,040 timestamped records** from a home-monitoring scenario sampled at regular 6-minute intervals over 21 days. Each row combines door activity, hallway motion, living-room temperature, fridge power consumption, and a label field.

## Dataset Files

| File | Description |
|---|---|
| `train.csv` | Main dataset file with timestamped smart-home sensor measurements |

## Dataset Details

| Field | Value |
|---|---|
| Dataset type | Tabular time-series sensor data |
| Number of rows | 5,040 data rows |
| Number of columns | 8 |
| Time range | 2025-01-01 00:00:00 to 2025-01-21 23:54:00 |
| Sampling interval | 6 minutes |
| Label values in current file | `none` |
| License | MIT |

## Column Description

| Column | Type | Description |
|---|---|---|
| `timestamp` | datetime | Timestamp for each observation |
| `door_state_front` | numeric | Front-door sensor signal |
| `door_state_front_event_duration_seconds` | numeric | Duration of the front-door event in seconds |
| `motion_detected_hallway` | numeric | Hallway motion sensor signal |
| `motion_detected_hallway_event_duration_minutes` | numeric | Duration of hallway motion event in minutes |
| `temperature_living_room` | numeric | Living-room temperature reading |
| `power_consumption_fridge` | numeric | Fridge power consumption reading |
| `label` | categorical | Event or condition label; current dataset rows are labeled `none` |

## Basic Statistics

| Feature | Minimum | Maximum | Mean |
|---|---:|---:|---:|
| `temperature_living_room` | 9.77 | 37.11 | 20.19 |
| `power_consumption_fridge` | 9 | 605 | 134.80 |
| `door_state_front` | 0 | 5.20 | 0.13 |
| `motion_detected_hallway` | 0 | 5.20 | 1.06 |

Non-zero activity appears in 135 rows for `door_state_front` and 1,104 rows for `motion_detected_hallway`.

## Intended Uses

This dataset can be used for:

- Smart-home monitoring prototypes
- IoT sensor data analysis
- Time-series feature engineering
- Baseline modeling for normal home operation
- Anomaly-detection experiments using normal-only data
- Energy monitoring and appliance-consumption analysis
- Activity-aware home automation research
- Teaching examples for tabular time-series preprocessing

## Out-of-Scope Uses

This dataset should not be used as a standalone safety, health, clinical, elder-care, or security monitoring system. Any deployment in a real home-monitoring environment requires external validation, privacy review, operational testing, and domain-specific safeguards.

## Loading the Dataset

### Hugging Face `datasets`

```python
from datasets import load_dataset

dataset = load_dataset("MBJamshidi/HomeMonitoringSystem")
train = dataset["train"]
print(train[0])
```

### pandas

```python
import pandas as pd

df = pd.read_csv("train.csv", parse_dates=["timestamp"])
print(df.head())
```

## Example Preprocessing

```python
import pandas as pd
from sklearn.model_selection import train_test_split

df = pd.read_csv("train.csv", parse_dates=["timestamp"])

df["hour"] = df["timestamp"].dt.hour
df["day_of_week"] = df["timestamp"].dt.dayofweek

features = [
    "door_state_front",
    "door_state_front_event_duration_seconds",
    "motion_detected_hallway",
    "motion_detected_hallway_event_duration_minutes",
    "temperature_living_room",
    "power_consumption_fridge",
    "hour",
    "day_of_week",
]

X = df[features]
y = df["label"]

X_train, X_test, y_train, y_test = train_test_split(
    X,
    y,
    test_size=0.2,
    shuffle=False,
)
```

## Notes for Machine Learning

- The current `label` column contains only `none`, so supervised multi-class classification is not meaningful without additional labels.
- The dataset is well suited to normal-baseline modeling, exploratory time-series analysis, and unsupervised anomaly-detection workflows.
- Use chronological train/test splitting for time-series experiments.
- Report feature engineering, scaling, split dates, and evaluation metrics clearly for reproducibility.

## Limitations

- The dataset covers one 21-day period only.
- The current file contains normal or unlabeled records only, based on the `none` label.
- Sensor definitions are limited to the available column names and should be interpreted conservatively.
- Models trained on this dataset should be externally validated before use in operational monitoring.

## Citation

If you use this dataset in research, software, reports, or educational material, please cite the dataset repository:

```bibtex
@misc{jamshidi_home_monitoring_system,
  title={Home Monitoring System},
  author={Jamshidi, Mohammad Behdad},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/MBJamshidi/HomeMonitoringSystem}}
}
```

## License

This dataset is released under the MIT License.

## Maintainer

Mohammad Behdad Jamshidi

- Hugging Face: [MBJamshidi](https://huggingface.co/MBJamshidi)