Related papers: GEM: Gear-based Environment-Integrated Mobility fo…
Adhering to current HRI paradigms, all of the sensors, visualisation and legibility of actions and motions are borne by the robot or its working cell. This necessarily makes robots more complex or confines them into specialised, structured…
Internet of Things (IoT) employs a large number of spatially distributed wireless sen-sors to monitor physical environments, e.g., temperature, humidity, and air pressure, have found wide applications including environmental monitoring,…
The transition to electric transportation is a key enabler for intelligent and sustainable cities; however, inadequate charging infrastructure remains a major barrier to large-scale electric vehicle (EV) adoption. This paper presents a…
To enable machines to understand the way humans interact with the physical world in daily life, 3D interaction signals should be captured in natural settings, allowing people to engage with multiple objects in a range of sequential and…
The ever-increasing mobile data demands have posed significant challenges in the current radio access networks, while the emerging computation-heavy Internet of things (IoT) applications with varied requirements demand more flexibility and…
Remote monitoring of motor functions is a powerful approach for health assessment, especially among the elderly population or among subjects affected by pathologies that negatively impact their walking capabilities. This is further…
Human decision-making heavily relies on active sensing, a well-documented cognitive behaviour for evidence gathering to accommodate ever-changing environments. However, its operational mechanism in the real world remains non-trivial.…
Understanding individual-level human mobility is critical for a wide range of applications. As such, real-world trajectory datasets provide valuable insights into actual movement behaviors and patterns of life but are often constrained by…
Tracking maneuvering targets requires estimators that are both responsive and robust. Interacting Multiple Model (IMM) filters are a standard tracking approach, but fusing models via Gaussian mixtures can lag during maneuvers. Recent…
A recently approved ten-year extension of the BESIII experiment (IHEP, Beijing) motivated an upgrade program for both the accelerator and the detector. In particular, the current inner drift chamber is suffering from aging and the proposal…
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…
With the advent of the Internet of Things (IoT), the ever growing number of connected devices observed in recent years and foreseen for the next decade suggests that more and more data will have to be transmitted over a network, before…
Precise 6-DoF simultaneous localization and mapping (SLAM) from onboard sensors is critical for wearable devices capturing egocentric data, which exhibits specific challenges, such as a wider diversity of motions and viewpoints, prevalent…
Wearable sensors such as Inertial Measurement Units (IMUs) are often used to assess the performance of human exercise. Common approaches use handcrafted features based on domain expertise or automatically extracted features using time…
Robust multisensor fusion of multi-modal measurements such as IMUs, wheel encoders, cameras, LiDARs, and GPS holds great potential due to its innate ability to improve resilience to sensor failures and measurement outliers, thereby enabling…
We have recently proposed a scheme to use the channel equalization blocks of telecommunication systems to sense changes in an environment. We call this communication-sensing, CommSense for short. After some initial positive results we tried…
Home Energy Management Systems (HEMS) are being actively developed for both individual houses and communities to support demand response in on-grid operation, and ensure resilience during off-grid scenarios. However, most simulators used…
In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observations in many domains. Further, in many cases the target entities for analysis are actually signals on…
Sensor-based human activity recognition (HAR) is a paramount technology in the Internet of Things services. HAR using representation learning, which automatically learns a feature representation from raw data, is the mainstream method…
The RGB complementary metal-oxidesemiconductor (CMOS) sensor works within the visible light spectrum. Therefore it is very sensitive to environmental light conditions. On the contrary, a long-wave infrared (LWIR) sensor operating in 8-14…