The performance of physical workers is significantly influenced by the
quantity of their motions. However, monitoring and assessing these motions is
challenging due to the complexities of motion sensing, tracking, and
quantification. Recent advancements have utilized in-situ video analysis for
real-time observation of worker behaviors, enabling data-driven quantification
of motion amounts. Nevertheless, there are limitations to monitoring worker
movements using video data. This paper introduces a novel framework based on
computer vision to track and quantify the motion of workers' upper and lower
limbs, issuing alerts when the motion reaches critical thresholds. Using joint
position data from posture estimation, the framework employs Hotelling's T$^2$
statistic to quantify and monitor motion amounts, integrating computer vision
tools to address challenges in automated worker training and enhance
exploratory research in this field. We collected data of participants
performing lifting and moving tasks with large boxes and small wooden cubes, to
simulate macro and micro assembly tasks respectively. It was found that the
correlation between workers' joint motion amount and the Hotelling's T$^2$
statistic was approximately 35% greater for micro tasks compared to macro
tasks, highlighting the framework's ability to identify fine-grained motion
differences. This study demonstrates the effectiveness of the proposed system
in real-time applications across various industry settings. It provides a tool
for enhancing worker safety and productivity through precision motion analysis
and proactive ergonomic adjustments.

基于计算机视觉的新框架可追踪和量化工人上下肢的运动，并在达到临界阈值时发出警报，通过精确动作分析和主动人体工程学调整，提供了一种增强工人安全和生产力的工具。

计算机视觉辅助工人视频分析的运动量定量化

Computer-Vision-Enabled Worker Video Analysis for Motion Amount  Quantification

We present IMU2CLIP, a novel pre-training approach to align Inertial
Measurement Unit (IMU) motion sensor recordings with video and text, by
projecting them into the joint representation space of Contrastive
Language-Image Pre-training (CLIP). The proposed approach allows IMU2CLIP to
translate human motions (as measured by IMU sensors) into their corresponding
textual descriptions and videos -- while preserving the transitivity across
these modalities.
We explore several new IMU-based applications that IMU2CLIP enables, such as
motion-based media retrieval and natural language reasoning tasks with motion
data. In addition, we show that IMU2CLIP can significantly improve the
downstream performance when fine-tuned for each application (e.g. activity
recognition), demonstrating the universal usage of IMU2CLIP as a new
pre-trained resource. Our code will be made publicly available.

IMU2CLIP 是一种新型的预训练方法，用于将惯性测量单元（IMU）运动传感器记录与视频和文本对齐，从而在对比度语言 - 图像预训练 (CLIP) 的联合表示空间中将其投影，该方法允许 IMU2CLIP 将人体动作转化为相应的文本描述和视频，并保持这些模态之间的传递性，用于多种应用，包括基于动作的媒体检索和自然语言推理任务以及活动识别等，并展示了 IMU2CLIP 在每种应用的定制预训练可大大提高下游性能，彰显了其作为预训练资源的通用用途。