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

Patellofemoral joint (PFJ) issues affect one in four people, with 20%
experiencing chronic knee pain despite treatment. Poor outcomes and pain after
knee replacement surgery are often linked to patellar mal-tracking. Traditional
imaging methods like CT and MRI face challenges, including cost and metal
artefacts, and there's currently no ideal way to observe joint motion without
issues such as soft tissue artefacts or radiation exposure. A new system to
monitor joint motion could significantly improve understanding of PFJ dynamics,
aiding in better patient care and outcomes. Combining 2D ultrasound with motion
tracking for 3D reconstruction of the joint using semantic segmentation and
position registration can be a solution. However, the need for expensive
external infrastructure to estimate the trajectories of the scanner remains the
main limitation to implementing 3D bone reconstruction from handheld ultrasound
scanning clinically. We proposed the Visual-Inertial Odometry (VIO) and the
deep learning-based inertial-only odometry methods as alternatives to motion
capture for tracking a handheld ultrasound scanner. The 3D reconstruction
generated by these methods has demonstrated potential for assessing the PFJ and
for further measurements from free-hand ultrasound scans. The results show that
the VIO method performs as well as the motion capture method, with average
reconstruction errors of 1.25 mm and 1.21 mm, respectively. The VIO method is
the first infrastructure-free method for 3D reconstruction of bone from
wireless handheld ultrasound scanning with an accuracy comparable to methods
that require external infrastructure.

通过结合二维超声波和运动跟踪，利用语义分割和位置配准进行三维重建，作者提出了一种用于监测关节运动的新方法，该方法能够在不需要昂贵的外部基础设施的情况下评估骨骼的三维重建准确性。

使用视觉惯性和深度惯性测量髌骨跟踪的 3D 手持式超声

3D Freehand Ultrasound using Visual Inertial and Deep Inertial Odometry  for Measuring Patellar Tracking

Mobility impairment caused by limb loss is a significant challenge faced by
millions of individuals worldwide. The development of advanced assistive
technologies, such as prosthetic devices, has the potential to greatly improve
the quality of life for amputee patients. A critical component in the design of
such technologies is the accurate prediction of reference joint motion for the
missing limb. However, this task is hindered by the scarcity of joint motion
data available for amputee patients, in contrast to the substantial quantity of
data from able-bodied subjects. To overcome this, we leverage deep learning's
reprogramming property to repurpose well-trained models for a new goal without
altering the model parameters. With only data-level manipulation, we adapt
models originally designed for able-bodied people to forecast joint motion in
amputees. The findings in this study have significant implications for
advancing assistive tech and amputee mobility.

通过深度学习模型将常规关节运动数据用于截肢者，以预测缺失肢体的参考关节运动，为提高助身技术和截肢者的生活质量提供了有重要意义的发现。

通过模型重新编程提升肢体缺失个体的联动运动预测

Enhancing Joint Motion Prediction for Individuals with Limb Loss Through  Model Reprogramming

Muscle forces and joint kinematics estimated with musculoskeletal (MSK)
modeling techniques offer useful metrics describing movement quality.
Model-based computational MSK models can interpret the dynamic interaction
between the neural drive to muscles, muscle dynamics, body and joint
kinematics, and kinetics. Still, such a set of solutions suffers from high
computational time and muscle recruitment problems, especially in complex
modeling. In recent years, data-driven methods have emerged as a promising
alternative due to the benefits of flexibility and adaptability. However, a
large amount of labeled training data is not easy to be acquired. This paper
proposes a physics-informed deep learning method based on MSK modeling to
predict joint motion and muscle forces. The MSK model is embedded into the
neural network as an ordinary differential equation (ODE) loss function with
physiological parameters of muscle activation dynamics and muscle contraction
dynamics to be identified. These parameters are automatically estimated during
the training process which guides the prediction of muscle forces combined with
the MSK forward dynamics model. Experimental validations on two groups of data,
including one benchmark dataset and one self-collected dataset from six healthy
subjects, are performed. The results demonstrate that the proposed deep
learning method can effectively identify subject-specific MSK physiological
parameters and the trained physics-informed forward-dynamics surrogate yields
accurate motion and muscle forces predictions.

该研究论文提出了一种基于肌骨建模的受物理学约束的深度学习方法，用于预测关节运动和肌肉力量，并证明该方法能有效地识别个体特定的肌骨生理参数，并且训练的受物理学约束的前向动力学模型能够准确地预测运动和肌肉力量。