Robust, fast, and accurate human state - 6D pose and posture - estimation
remains a challenging problem. For real-world applications, the ability to
estimate the human state in real-time is highly desirable. In this paper, we
present BodySLAM++, a fast, efficient, and accurate human and camera state
estimation framework relying on visual-inertial data. BodySLAM++ extends an
existing visual-inertial state estimation framework, OKVIS2, to solve the dual
task of estimating camera and human states simultaneously. Our system improves
the accuracy of both human and camera state estimation with respect to baseline
methods by 26% and 12%, respectively, and achieves real-time performance at 15+
frames per second on an Intel i7-model CPU. Experiments were conducted on a
custom dataset containing both ground truth human and camera poses collected
with an indoor motion tracking system.

本文提出了一种快速、高效、准确的人体和相机状态估计框架 BodySLAM++，通过视觉惯性数据对人体状态和相机状态同时进行估计，并在 Intel i7 处理器的环境下实现了 15 + 帧每秒的实时性能，相对于基准方法，我们的系统分别提高了 26% 和 12% 的人体和相机状态估计准确率。

BodySLAM++：快速而紧密耦合的视觉惯性摄像机和人体运动跟踪

BodySLAM++: Fast and Tightly-Coupled Visual-Inertial Camera and Human  Motion Tracking

Ergonomics and human comfort are essential concerns in physical human-robot
interaction. Common practical methods in the area either fail in estimating the
correct posture due to occlusion or suffer from inaccurate ergonomics models in
performing postural optimization. We propose a novel alternative framework for
posture estimation, assessment, and optimization for ergonomically intelligent
physical human-robot interaction. We show that we can estimate human posture
solely from the trajectory of the interacting robot with median deviation of 5
deg from motion capture. We propose DULA, a differentiable ergonomics
assessment tool with 99.73% accuracy comparing to RULA. We use DULA in postural
optimization for physical human-robot interaction tasks such as co-manipulation
and teleoperation. We evaluate our framework through human and simulation
experiments.

提出了一种基于 “DULA” 的姿势评估工具的人类机器人交互的姿势估计、评估和优化的新框架。

人机工程智能化的物理人机协作：姿态估计、评估和优化

Ergonomically Intelligent Physical Human-Robot Interaction: Postural  Estimation, Assessment, and Optimization

In this paper, we present a new dataset for "distracted driver" posture
estimation. In addition, we propose a novel system that achieves 95.98% driving
posture estimation classification accuracy. The system consists of a
genetically-weighted ensemble of Convolutional Neural Networks (CNNs). We show
that a weighted ensemble of classifiers using a genetic algorithm yields in
better classification confidence. We also study the effect of different visual
elements (i.e. hands and face) in distraction detection and classification by
means of face and hand localizations. Finally, we present a thinned version of
our ensemble that could achieve a 94.29% classification accuracy and operate in
a realtime environment.

本文介绍一种新的基于卷积神经网络集成学习和遗传算法优化的 “分心驾驶” 姿势估计系统，能够高效检测和识别驾驶人员的姿态，同时降低交通事故风险。