物流搬运作业人力姿态分析数据

该数据在人力装卸搬运服务中具有重要的应用价值。能够提供实时作业姿态捕捉，更精确地识别异常负重动作，帮助安全监管人员进行姿态优化和劳损风险评估。在物流仓储、码头装卸和建筑材料搬运等场景中具有广泛的应用价值，特别是在分拣区搬运岗位、装卸区重物提举场景和高频重复搬运区域，能够提高作业动作标准化水平，降低作业伤害发生率，提供作业过程数字化管理支持。数据收集：
通过在搬运工作现场安装高清摄像头，采集工人搬运过程中的视频帧图像，作为原始输入图像。人工标注每帧图像中的17个人体关键点坐标（包含肩膀、肘、手腕、膝盖、脚踝等关键位置），并根据专家定义的标准搬运动作规范为每帧赋予搬运类别标签（例如：标准提举、弯腰搬运、单腿支撑等）。采集数据包含图像、真实人体关键点坐标和搬运类别。

数据预处理：
图像帧统一调整大小，并进行像素归一化，提取RGB均值作为简化输入表示。关键点数据进行标准化处理以适应不同身高个体，所有坐标值转换至相对坐标系统。搬运类别转换为整数编码（0-4分别对应，标准提举、弯腰搬运、单腿支撑、后仰、其他）用于分类任务训练。

模型构建：
采用基于卷积神经网络的多任务学习模型同时预测关键点位置与搬运类别。关键点检测模块使用卷积回归网络，分类模块使用全连接结构进行搬运类别预测。

核心计算公式如下：特征提取过程：F = FeatureEncoder(I)。其中，I为原始图像，F为提取后的特征张量，FeatureEncoder()为特征提取网络。

多任务输出过程：
P_kp = KeypointRegressor(F)，P_cls = PostureClassifier(F)。其中，P_kp为预测关键点位置，P_cls为搬运类别概率输出，KeypointRegressor为关键点检测网络，PostureClassifier（为识别网络）。

总损失函数定义为：
L_total = L_keypoint + α * L_classification。其中，L_keypoint为关键点位置的平均欧式距离损失，L_classification为姿态类别的交叉熵损失，α为任务权重因子，用于平衡两个任务的重要性。模型训练过程中目标是同时最小化预测关键点位置误差和搬运类别误差，最终在实际场景中实现对搬运动作的高精度识别和实时风险提示。评价标准为平均检测准确率与平均关键点预测误差。

This dataset holds significant application value in manual loading, unloading and material handling services. It enables real-time work posture capture, more accurate identification of abnormal load-bearing movements, and assists safety supervisors in conducting posture optimization and strain risk assessment. It has broad application value in scenarios such as logistics warehouses, terminal loading and unloading, and construction material handling, especially in sorting zone handling positions, heavy object lifting scenarios in loading/unloading zones, and high-frequency repetitive handling areas. It can improve the standardization of work movements, reduce the incidence of work-related injuries, and provide digital management support for the work process.

Data Collection:
High-definition cameras are installed at the handling work site to collect video frame images of workers during the handling process, which serve as the original input images. Each frame image is manually annotated with 17 human body keypoint coordinates (including key positions such as shoulders, elbows, wrists, knees, and ankles), and each frame is assigned a handling category label based on the expert-defined standard handling movement specifications (e.g., standard lifting, bent-over handling, single-leg support, backward leaning, etc.). The collected data includes images, ground-truth human body keypoint coordinates, and handling categories.

Data Preprocessing:
Image frames are uniformly resized and subjected to pixel normalization, and the RGB mean value is extracted as a simplified input representation. Keypoint data is standardized to adapt to individuals of different heights, and all coordinate values are converted to a relative coordinate system. Handling categories are converted into integer encodings (0-4 correspond to standard lifting, bent-over handling, single-leg support, backward leaning, and others, respectively) for classification task training.

Model Construction:
A multi-task learning model based on convolutional neural networks is adopted to simultaneously predict keypoint positions and handling categories. The keypoint detection module uses a convolutional regression network, and the classification module uses a fully connected neural network to predict handling categories.

Core Calculation Formulas are as follows:
Feature extraction process: $F = ext{FeatureEncoder}(I)$. Where $I$ is the original image, $F$ is the extracted feature tensor, and $ ext{FeatureEncoder}()$ is the feature extraction network.

Multi-task output process: $P_{kp} = ext{KeypointRegressor}(F)$, $P_{cls} = ext{PostureClassifier}(F)$. Where $P_{kp}$ is the predicted keypoint positions, $P_{cls}$ is the probability output of handling categories, $ ext{KeypointRegressor}$ is the keypoint detection network, and $ ext{PostureClassifier}$ is the posture classification network.

The total loss function is defined as: $L_{ ext{total}} = L_{ ext{keypoint}} + alpha imes L_{ ext{classification}}$. Where $L_{ ext{keypoint}}$ is the average Euclidean distance loss for keypoint positions, $L_{ ext{classification}}$ is the cross-entropy loss for posture categories, and $alpha$ is the task weight factor used to balance the importance of the two tasks.

The goal of model training is to simultaneously minimize the prediction error of keypoint positions and the error of handling categories, ultimately achieving high-precision recognition of handling movements and real-time risk alerts in actual scenarios. The evaluation metrics are average detection accuracy and average keypoint prediction error.