Transportation mode recognition (TMR) is a critical component of human
activity recognition (HAR) that focuses on understanding and identifying how
people move within transportation systems. It is commonly based on leveraging
inertial, location, or both types of signals, captured by modern smartphone
devices. Each type has benefits (such as increased effectiveness) and drawbacks
(such as increased battery consumption) depending on the transportation mode
(TM). Combining the two types is challenging as they exhibit significant
differences such as very different sampling rates. This paper focuses on the
TMR task and proposes an approach for combining the two types of signals in an
effective and robust classifier. Our network includes two sub-networks for
processing acceleration and location signals separately, using different window
sizes for each signal. The two sub-networks are designed to also embed the two
types of signals into the same space so that we can then apply an
attention-based multiple-instance learning classifier to recognize TM. We use
very low sampling rates for both signal types to reduce battery consumption. We
evaluate the proposed methodology on a publicly available dataset and compare
against other well known algorithms.

该研究论文聚焦于交通方式识别，并提出了一种将惯性信号和位置信号结合的有效和稳健的分类器，以识别交通方式。

基于低速加速度和定位信号的交通方式识别与注意力多实例学习网络

Transportation mode recognition based on low-rate acceleration and  location signals with an attention-based multiple-instance learning network

Either RGB images or inertial signals have been used for the task of motion
capture (mocap), but combining them together is a new and interesting topic. We
believe that the combination is complementary and able to solve the inherent
difficulties of using one modality input, including occlusions, extreme
lighting/texture, and out-of-view for visual mocap and global drifts for
inertial mocap. To this end, we propose a method that fuses monocular images
and sparse IMUs for real-time human motion capture. Our method contains a dual
coordinate strategy to fully explore the IMU signals with different goals in
motion capture. To be specific, besides one branch transforming the IMU signals
to the camera coordinate system to combine with the image information, there is
another branch to learn from the IMU signals in the body root coordinate system
to better estimate body poses. Furthermore, a hidden state feedback mechanism
is proposed for both two branches to compensate for their own drawbacks in
extreme input cases. Thus our method can easily switch between the two kinds of
signals or combine them in different cases to achieve a robust mocap. %The two
divided parts can help each other for better mocap results under different
conditions. Quantitative and qualitative results demonstrate that by delicately
designing the fusion method, our technique significantly outperforms the
state-of-the-art vision, IMU, and combined methods on both global orientation
and local pose estimation. Our codes are available for research at
this https URL

将 RGB 图像与惯性信号相结合的实时人体运动捕捉方法通过双坐标策略利用不同目标的 IMU 信号进行完全探索，并引入了隐藏状态反馈机制以应对极端输入情况。定量和定性结果表明，通过精心设计的融合方法，我们的技术在全局方向和局部姿势估计上明显优于最先进的视觉，IMU 和融合方法。