MBJamshidi/HomeMonitoringSystem

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

from datasets import load_dataset

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

pandas

import pandas as pd

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

Example Preprocessing

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:

@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