MEx - Multi-modal Exercise Dataset

The MEx Multi-modal Exercise dataset contains data of 7 different physiotherapy exercises, performed by 30 subjects recorded with 2 accelerometers, a pressure mat and a depth camera. **Application** The dataset can be used for exercise recognition, exercise quality assessment and exercise counting, by developing algorithms for pre-processing, feature extraction, multi-modal sensor fusion, segmentation and classification. ** Data collection method ** Each subject was given a sheet of 7 exercises with instructions to perform the exercise at the beginning of the session. At the beginning of each exercise the researcher demonstrated the exercise to the subject, then the subject performed the exercise for maximum 60 seconds while being recorded with four sensors. During the recording, the researcher did not give any advice or kept count or time to enforce a rhythm. ** Sensors** Obbrec Astra Depth Camera - sampling frequency – 15Hz - frame size – 240x320 Sensing Tex Pressure Mat - sampling frequency – 15Hz - frame size – 32*16 Axivity AX3 3-Axis Logging Accelerometer - sampling frequency – 100Hz - range – 8g ** Sensor Placement** All the exercises were performed lying down on the mat while the subject wearing two accelerometers on the wrist and the thigh. The depth camera was placed above the subject facing down-words recording an aerial view. Top of the depth camera frame was aligned with the top of the pressure mat frame and the subject’s shoulders such that the face will not be included in the depth camera video. ** Data folder ** MEx folder has four folders, one for each sensor. Inside each sensor folder, 30 folders can be found, one for each subject. In each subject folder, 8 files can be found for each exercise with 2 files for exercise 4 as it is performed on two sides. (The user 22 will only have 7 files as they performed the exercise 4 on only one side.) One line in the data files correspond to one timestamped and sensory data. **Attribute Information** The 4 columns in the act and acw files is organized as follows: 1 – timestamp 2 – x value 3 – y value 4 – z value Min value = -8 Max value = +8 The 513 columns in the pm file is organized as follows: 1 - timestamp 2-513 – pressure mat data frame (32x16) Min value – 0 Max value – 1 The 193 columns in the dc file is organized as follows: 1 - timestamp 2-193 – depth camera data frame (12x16) dc data frame is scaled down from 240x320 to 12x16 using the OpenCV resize algorithm Min value – 0 Max value – 1

MEx多模态运动数据集（MEx Multi-modal Exercise Dataset）收录了30名受试者完成7种不同物理治疗运动的多模态采集数据，所用采集设备包括2台加速度计、1块压力垫与1台深度相机。

**应用场景**：该数据集可用于开发运动识别、运动质量评估与运动计数相关算法，涵盖预处理、特征提取、多模态传感器融合、动作分割与分类等技术路径。

**数据采集流程**：每名受试者在实验起始阶段会获得包含7项运动的操作说明单。每项运动开始前，研究人员先向受试者演示标准动作，随后受试者完成该运动（最长时长不超过60秒），全程由4台传感器同步采集数据。采集过程中，研究人员不会提供动作指导，也不会通过计数或计时强制受试者遵循特定节奏。

**采集设备参数**：
- Obbrec Astra 深度相机（Obbrec Astra Depth Camera）：采样频率15Hz，帧分辨率240×320
- Sensing Tex 压力垫（Sensing Tex Pressure Mat）：采样频率15Hz，采集帧尺寸32×16
- Axivity AX3 三轴记录式加速度计（Axivity AX3 3-Axis Logging Accelerometer）：采样频率100Hz，量程±8g

**传感器部署方案**：所有运动均要求受试者躺卧于压力垫上完成，同时在受试者手腕与大腿处各佩戴1台加速度计。深度相机安装于受试者正上方，向下俯拍以获取鸟瞰视角。相机画面顶部与压力垫画面顶部、受试者肩部对齐，确保人脸不会被摄入深度相机采集的视频画面中。

**数据目录结构**：MEx数据集根目录包含4个子目录，分别对应1台采集设备。每个传感器子目录下包含30个受试者文件夹，每个文件夹对应1名受试者。每个受试者文件夹内包含8个运动数据文件：其中第4项运动因需分左右两侧完成，对应2个数据文件。（受试者22仅完成了单侧的第4项运动，因此其文件夹内仅包含7个数据文件。）数据文件中的每一行对应1条带时间戳的传感器采集数据。

**数据属性说明**：
1. act与acw格式文件：共4列，各列依次为：① 时间戳；② X轴加速度值；③ Y轴加速度值；④ Z轴加速度值。数值范围为[-8, +8]（单位：重力加速度g）。
2. pm格式文件：共513列，第1列为时间戳，第2至513列对应32×16尺寸的压力垫采集帧数据。数值范围为[0, 1]。
3. dc格式文件：共193列，第1列为时间戳，第2至193列对应12×16尺寸的深度相机采集帧数据。该帧数据通过OpenCV的resize算法将原始240×320分辨率的画面下采样得到。数值范围为[0, 1]。