A Synchronized Kinematic and Optical Dataset for Mobility-Aware Predictive Modeling in Indoor LiFi Systems

This dataset contains synchronized motion and optical measurements collected for the study of dynamic indoor Visible Light Communication (LiFi) channels under user mobility and device orientation changes. The objective of the dataset is to support research on channel aging, predictive modeling, adaptive communication, and sensor-based LiFi system analysis.

Data were collected using a custom ESP32-based sensing platform equipped with an Inertial Measurement Unit (IMU) and an optical photodiode receiver. Measurements were sampled at 100 Hz with aligned timestamps to ensure consistency between motion and optical observations.

The dataset includes five experimental scenarios representing progressively different operating conditions:

S1: Static baseline alignment
S2: Controlled single-axis motion
S3: Multi-axis orientation analysis
S4: Realistic handheld motion
S5: Strong random motion with intermittent signal degradation

Each record may include timestamps, inter-sample intervals, acceleration, angular velocity, raw ADC optical readings, calibrated voltage, and scenario labels.

This dataset can be used for:

LiFi channel prediction
Channel aging analysis
Machine learning for time-series forecasting
Markov and state-based modeling
Sensor fusion research
Mobility-aware handover methods
Real-time embedded communication studies

The dataset was collected in a controlled indoor environment using repeatable experimental procedures.