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Motion capture from sparse inertial sensors has shown great potential compared to image-based approaches since occlusions do not lead to a reduced tracking quality and the recording space is not restricted to be within the viewing frustum…
Precise, consistent, and reliable positioning is crucial for a multitude of uses. In order to achieve high precision global positioning services, multi-sensor fusion techniques, such as the Global Navigation Satellite System (GNSS)/Inertial…
Falls represent a significant cause of injury among the elderly population. Extensive research has been devoted to the utilization of wearable IMU sensors in conjunction with machine learning techniques for fall detection. To address the…
Indoor human positioning has become increasingly important for applications such as health monitoring, breath monitoring, human identification, safety and rescue operations, and security surveillance. However, achieving robust indoor human…
Stride length estimation using inertial measurement unit (IMU) sensors is getting popular recently as one representative gait parameter for health care and sports training. The traditional estimation method requires some explicit…
We propose a framework for tightly-coupled lidar inertial odometry via smoothing and mapping, LIO-SAM, that achieves highly accurate, real-time mobile robot trajectory estimation and map-building. LIO-SAM formulates lidar-inertial odometry…
This paper presents the large and diverse dataset for development of smartphone-based pedestrian navigation algorithms. This dataset consists of about 1200 sets of inertial measurements from sensors of several smartphones. The measurements…
The ubiquitous availability of wearable sensors is responsible for driving the Internet-of-Things but is also making an impact on sport sciences and precision medicine. While human activity recognition from smartphone data or other types of…
This paper proposes a method for tight fusion of visual, depth and inertial data in order to extend robotic capabilities for navigation in GPS-denied, poorly illuminated, and texture-less environments. Visual and depth information are fused…
Human bodily movements convey critical insights into action intentions and cognitive processes, yet existing multimodal systems primarily focused on understanding human motion via language, vision, and audio, which struggle to capture the…
Sedentary behavior poses a major public health risk, being strongly linked to obesity, cardiovascular disease, and other chronic conditions. Accurately estimating sitting time is therefore critical for monitoring and improving individual…
This paper presents a novel approach to vehicle positioning that operates without reliance on the global navigation satellite system (GNSS). Traditional GNSS approaches are vulnerable to interference in certain environments, rendering them…
In this paper, we study state estimation of multi-visual-inertial systems (MVIS) and develop sensor fusion algorithms to optimally fuse an arbitrary number of asynchronous inertial measurement units (IMUs) or gyroscopes and global and(or)…
Consider indoor positioning systems (IPS) in production halls where objects equipped with sensors send their current position. Beside its large volume, the analyzation of the resulting raw data is challenging due to the susceptibility…
Video and wearable sensor data provide complementary information about human movement. Video provides a holistic understanding of the entire body in the world while wearable sensors provide high-resolution measurements of specific body…
Although Global Navigation Satellite Systems (GNSS) provide a general solution for bike tracking outdoors, there still exist complex riding environments where only inertial navigation systems work, such as urban canyons. Despite decades of…
To improve the accuracy and robustness of the inertial navigation systems (INS) for wheeled robots without adding additional component cost, we propose Wheel-INS, a complete dead reckoning solution based on a wheel-mounted…
Compared with visual signals, Inertial Measurement Units (IMUs) placed on human limbs can capture accurate motion signals while being robust to lighting variation and occlusion. While these characteristics are intuitively valuable to help…
Inertial Measurement Unit (IMU)-based Human Activity Recognition (HAR) aims to interpret and classify user behaviors from temporal motion signals. Recently, deep learning frameworks have advanced this task by learning and extracting…
Wearable inertial measurement units (IMUs) provide a cost-effective approach to assessing human movement in clinical and everyday environments. However, developing the associated classification models for robust assessment of…