A Dataset of Inertial Measurement Units for Handwritten Maths Numbers and Symbols

The dataset consists of Inertial Measurement Unit (IMU) data corresponding to the 10 numeric digits (0–9) and 4 mathematical symbols: plus (+), minus (−), multiplication (×), and division (÷). The data was collected using an IMU 6050 sensor, which was attached to a marker held by participants during the handwriting process. The IMU sensor captures accelerations along three axes (X, Y, Z) and rotational velocities along the same three axes, providing detailed motion profiles for each written symbol.

Data Collection Process:

Twenty students participated in the data collection process for this study. Each student was tasked with writing all 14 characters (10 digits + 4 symbols) twice — once with the IMU sensor attached to the upper part of a marker and once with the sensor attached to the lower part. This dual positioning helped investigate how sensor placement affects motion data distinctiveness and recognition performance. Each student thus contributed 28 samples (14 characters × 2 sensor positions), creating a balanced and diverse dataset.

The data collection process was conducted over a four-month period to ensure ample data diversity and volume. In each session, students wrote each symbol 250 times on a whiteboard. To precisely record the start and end of each character-writing event, participants used a button they pressed before starting and released upon completion. The IMU sensor continuously recorded data for all 250 repetitions of each character written by each student. This approach resulted in a rich, large-scale dataset well-suited for deep learning and machine learning applications.

Labeling:

The data is labeled numerically from 0 to 13. Labels 0–9 correspond to digits '0' through '9', label 10 represents the plus sign '+', label 11 represents the minus sign '−', label 12 represents the multiplication sign '×', and label 13 represents the division sign '÷'. This straightforward labeling allows for efficient classification and identification during analysis and modeling.

Data Interpretation and Usage:

Character Recognition: Train and evaluate machine learning models to recognize numeric digits and basic arithmetic symbols based on IMU data.

Sensor Analysis: Investigate how different sensor placements affect recognition accuracy and develop methods for sensor position normalization or compensation.

Handwriting Dynamics: Analyze the writing speed, stroke patterns, angular velocity, and other motion features associated with handwritten digits and symbols.

This dataset offers a valuable resource for researchers and developers working on handwriting recognition using inertial sensor data, particularly in the context of numeric and symbolic input recognition.

本数据集包含对应10个数字（0–9）与4个数学符号：加号（+）、减号（−）、乘号（×）和除号（÷）的惯性测量单元（Inertial Measurement Unit, IMU）数据。数据采集采用IMU 6050传感器，该传感器被固定于受试者书写过程中握持的书写笔上。IMU传感器可采集沿X、Y、Z三个轴的加速度与同轴向的旋转角速度，为每个书写符号提供详尽的运动特征曲线。

数据采集流程：
本研究共招募20名学生参与数据采集。每名学生需完成全部14类字符（10个数字+4个符号）的书写各两次：一次将传感器固定于书写笔上段，另一次固定于书写笔下段。该双位置布设方案用于探究传感器安装位置对运动数据辨识度与识别性能的影响。因此每名学生可提供28条样本（14类字符×2种传感器安装位置），最终构建出均衡且多样的数据集。

数据采集周期为4个月，以确保数据集具备充足的多样性与规模。每次采集会话中，学生需在白板上重复书写每个符号250次。为精准记录每次字符书写的起止时刻，受试者需在书写开始前按下按钮，并于书写结束后松开该按钮。IMU传感器会持续采集每名学生书写的每个符号的全部250次重复书写过程的数据。该采集方案最终得到了丰富的大规模数据集，可很好地适配深度学习与机器学习相关应用。

标签标注：
数据采用0至13的数字标签进行标注：标签0–9分别对应数字0至9，标签10代表加号（+），标签11代表减号（−），标签12代表乘号（×），标签13代表除号（÷）。该简洁的标注方案可在分析与建模过程中实现高效的分类与识别任务。

数据解读与应用场景：
字符识别：基于IMU数据训练并评估机器学习模型，以实现数字与基础算术符号的识别。
传感器分析：探究不同传感器安装位置对识别精度的影响，并提出传感器位置归一化或补偿方法。
书写动力学分析：分析手写数字与符号相关的书写速度、笔画模式、角速度及其他运动特征。
本数据集可为从事基于惯性传感器数据的手写识别研究的科研人员与开发者提供宝贵的资源，尤其适用于数字与符号输入识别相关的研究方向。