Temporal 3D human pose estimation from monocular videos is a challenging task
in human-centered computer vision due to the depth ambiguity of 2D-to-3D
lifting. To improve accuracy and address occlusion issues, inertial sensor has
been introduced to provide complementary source of information. However, it
remains challenging to integrate heterogeneous sensor data for producing
physically rational 3D human poses. In this paper, we propose a novel
framework, Real-time Optimization and Fusion (RTOF), to address this issue. We
first incorporate sparse inertial orientations into a parametric human skeleton
to refine 3D poses in kinematics. The poses are then optimized by energy
functions built on both visual and inertial observations to reduce the temporal
jitters. Our framework outputs smooth and biomechanically plausible human
motion. Comprehensive experiments with ablation studies demonstrate its
rationality and efficiency. On Total Capture dataset, the pose estimation error
is significantly decreased compared to the baseline method.

我们提出了一个新的框架（Real-time Optimization and Fusion，RTOF），通过将稀疏惯性方向信息与参数化的人体骨骼结构相结合，优化并融合视觉和惯性观测数据，从而实现对时态三维人体姿势的准确估计和干扰问题的解决，提供平滑及生物力学合理的人体动作估计结果，并通过全面实验与消融研究验证了其合理性和效率。

单目视频和稀疏惯性测量单元的混合三维人体姿态估计

Hybrid 3D Human Pose Estimation with Monocular Video and Sparse IMUs

A two-wheeled self-balancing robot is an example of an inverse pendulum and
is an inherently non-linear, unstable system. The fundamental concept of the
proposed framework "Epersist" is to overcome the challenge of counterbalancing
an initially unstable system by delivering robust control mechanisms,
Proportional Integral Derivative(PID), and Reinforcement Learning (RL).
Moreover, the micro-controller NodeMCUESP32 and inertial sensor in the Epersist
employ fewer computational procedures to give accurate instruction regarding
the spin of wheels to the motor driver, which helps control the wheels and
balance the robot. This framework also consists of the mathematical model of
the PID controller and a novel self-trained advantage actor-critic algorithm as
the RL agent. After several experiments, control variable calibrations are made
as the benchmark values to attain the angle of static equilibrium. This
"Epersist" framework proposes PID and RL-assisted functional prototypes and
simulations for better utility.

提出一个名为 Epersist 的框架，它采用 PID 和强化学习辅助的功能原型和仿真，以克服反摆问题，并提供了使用惯性传感器来控制机器人轮子旋转的精确指令。