Search Results (34)

Unlike traditional vision-based camera tracking, human indoor localization and activity trajectory recognition also employ other methods such as infrared tracking, acoustic localization, and locators. These methods have significant environmental limitations or dependency on specialized equipment. Currently, WiFi-based human sensing is a novel and important method for human activity recognition. However, most WiFi-based activity recognition methods have limitations, such as using WiFi fingerprints to identify human activities. They either require extensive sample collection and training, are constrained by a fixed environmental layout, or rely on the precise positioning of transmitters (TXs) and receivers (RXs) within the space. If the positions are uncertain, or change, the sensing performance becomes unstable. To address the dependency of current WiFi indoor human activity trajectory reconstruction on the TX-RX position, we propose WiPIHT, a stable system for tracking indoor human activity trajectories using a small number of commercial WiFi devices. This system does not require additional hardware to be carried or locators to be attached, enabling passive, real-time, and accurate tracking and trajectory reconstruction of indoor human activities. WiPIHT is based on an innovative CSI channel analysis method, analyzing its autocorrelation function to extract location-independent real-time movement speed features of the human body. It also incorporates Fresnel zone and motion velocity direction decomposition to extract movement direction change patterns independent of the relative position between the TX-RX and the human body. By combining real-time speed and direction curve features, the system derives the shape of the human movement trajectory. Experiments demonstrate that, compared to existing methods, our system can accurately reconstruct activity trajectory shapes even without knowing the initial positions of the TX or the human body. Additionally, our system shows significant advantages in tracking accuracy, real-time performance, equipment, and cost. Full article

To address the challenges of accurate indoor positioning in complex environments, this paper proposes a two-stage indoor positioning method, ResT-IMU, which integrates the ResNet and Transformer architectures. The method initially processes the IMU data using Kalman filtering, followed by the application of windowing to the data. Residual networks are then employed to extract motion features by learning the residual mapping of the input data, which enhances the model’s ability to capture motion changes and predict instantaneous velocity. Subsequently, the self-attention mechanism of the Transformer is utilized to capture the temporal features of the IMU data, thereby refining the estimation of movement direction in conjunction with the velocity predictions. Finally, a fully connected layer outputs the predicted velocity and direction, which are used to calculate the trajectory. During training, the RMSE loss is used to optimize velocity prediction, while the cosine similarity loss is employed for direction prediction. Theexperimental results demonstrate that ResT-IMU achieves velocity prediction errors of 0.0182 m/s on the iIMU-TD dataset and 0.014 m/s on the RoNIN dataset. Compared with the ResNet model, ResT-IMU achieves reductions of 0.19 m in ATE and 0.05 m in RTE on the RoNIN dataset. Compared with the IMUNet model, ResT-IMU achieves reductions of 0.61 m in ATE and 0.02 m in RTE on the iIMU-TD dataset and reductions of 0.32 m in ATE and 0.33 m in RTE on the RoNIN dataset. Compared with the ResMixer model, ResT-IMU achieves reductions of 0.13 m in ATE and 0.02 m in RTE on the RoNIN dataset. These improvements indicate that ResT-IMU offers superior accuracy and robustness in trajectory prediction. Full article

Micro drones are becoming more popular in many areas, because they are small and fast enough to fly in tight and complex spaces. But they still have some significant problems. Their batteries drain fast, they cannot carry much weight, and their sensors and computers are limited. These problems affect their flying performance and stability, which is very important for their missions. In this study, we evaluated the accuracy of mini drones in indoor environments. During hovering, the drones showed an average deviation of 77.9 cm, with a standard deviation of 26.4 cm, indicating moderate stability while stationary. In simple forward flights over 3 m, the average deviation increased to 92.6 cm, which showed slight drop in accuracy during movement. For more complex flight paths, such as L-shaped and square trajectories, the deviations increased to 141 cm and 245 cm, respectively. Full article

Millimeter-wave (mmWave) radar is increasingly used in smart environments for human detection due to its rich sensing capabilities and sensitivity to subtle movements. However, indoor multipath propagation causes severe ghost target issues, reducing radar reliability. To address this, we propose a trajectory-based ghost suppression method that integrates multi-target tracking with point cloud deep learning. Our approach consists of four key steps: (1) point cloud pre-segmentation, (2) inter-frame trajectory tracking, (3) trajectory feature aggregation, and (4) feature broadcasting, effectively combining spatiotemporal information with point-level features. Experiments on an indoor dataset demonstrate its superior performance compared to existing methods, achieving 93.5% accuracy and 98.2% AUROC. Ablation studies demonstrate the importance of each component, particularly the complementary benefits of pre-segmentation and trajectory processing. Full article

Pipelines in landslide-prone areas are highly susceptible to damage or rupture under soil movement, posing severe threats to social stability and national security. However, research on pipeline failure mechanisms across different landslide types remains insufficient. Therefore, this study employs large-scale indoor model tests to investigate the interaction mechanisms between pipelines and soil (pipeline–soil interaction) in translational landslide zones through comparative experiments. The results indicate that: (1) The failure process of translational landslides is characterized by initial sliding at the slope crest under loading, which progressively drives the lower soil mass, ultimately resulting in global slope instability. The sliding mass displacement exhibits a top-to-bottom reduction pattern. (2) Pipelines traversing slopes laterally significantly enhance slope stability by providing measurable anti-sliding resistance. (3) Pipeline displacement under sliding mass action occurs in the downslope direction, yet its trajectory deviates from the sliding mass movement. (4) Strain analysis reveals that the pipeline experiences peak strain in the middle region of the sliding mass and at the sliding-non-sliding interface, with the middle region being the primary location for initial yielding and fracture. This study advances the understanding of pipeline-sliding mass interaction mechanisms in translational landslides and offers critical insights for improving pipeline safety and reliability. Full article

The lack of a testing framework for various indoor positioning technologies brings huge challenges to the systematic and fair evaluation of positioning systems, which greatly hinders the development and industrialization of indoor positioning technology. In order to solve this problem, this article refers to international standards, such as ISO/IEC 18305, and uses the China Electronics Standardization Institute’s rich experience in indoor positioning technology research and testing to build a universal positioning performance testing and evaluation framework. First, this paper introduces the experimental environment in detail from the aspects of the coordinate system definition, test point selection, building type definition, motion mode definition, and motion trajectory setting. Then, this paper comprehensively measures performance evaluation indicators from dimensions such as the accuracy index, relative accuracy, startup time, fault tolerance, power consumption, size, and cost. Finally, this paper elaborates on the testing methods and processes of positioning precision, accuracy, relative accuracy, floor identification, indoor–outdoor distinction, latency, relative accuracy, success rate, and movement speed tests. Full article

The recent growth in e-commerce has significantly increased the demand for indoor delivery solutions, highlighting challenges in last-mile delivery. This study presents a time-interval-based collision detection method for Four-Wheel Independent Steering (4WIS) mobile robots operating in human-shared indoor environments, where traditional path following algorithms often create unpredictable movements. By integrating kinematic-based robot trajectory calculation with LiDAR-based human detection and Kalman filter-based prediction, our system enables more natural robot–human interactions. Experimental results demonstrate that our parallel driving mode achieves superior human detection performance compared to conventional Ackermann steering, particularly during cornering and high-speed operations. The proposed method’s effectiveness is validated through comprehensive experiments in realistic indoor scenarios, showing its potential for improving the efficiency and safety of indoor autonomous navigation systems. Full article

Understanding the feeding behavior of Portunidae crabs with different baits can improve bait selection and is crucial for improving the effectiveness of crab fishing gear. This study, conducted in indoor experimental tanks, used trajectory tracking software and two types of natural baits (mackerel (Scomber australasicus) and squid (Uroteuthis chinensis)) to understand the behavior of Portunus sanguinolentus and Charybdis natator. Spatial distribution results showed that P. sanguinolentus was frequently present in the starting area (S1) and bait area (S3) in the control and treatment groups. However, C. natator was frequently present and concentrated in the S1 area compared to the middle areas S2 and S3, and only in the mackerel treatments were they observed to move to the S3 areas. The spatial distribution results indicate that P. sanguinolentus shows a stronger willingness to explore its surroundings, while C. natator is generally in a stationary, wait-and-see state. The swimming speeds of P. sanguinolentus and C. natator showed different trends. P. sanguinolentus showed continuous movement with no fixed speed when no bait was present in the control groups. However, when treated with mackerel and squid, the average swimming speed of P. sanguinolentus was faster (>5 cm/s) in the first 10 min and showed a more stable movement speed when searching for the baits. C. natator showed a stationary or low movement speed when no bait was present in the control groups. However, when C. natator perceived the presence of the baits in the treatment groups, their movement speed increased in the first 10 min. In addition, there was no significant difference between male and female crabs of P. sanguinolentus and C. natator in movement speed in the control and treatment groups. Compared to C. natator, P. sanguinolentus might be more sensitive to natural baits, as shown by its movement from S1 to S3. The results indicate that the species of Portunidae crabs show different bait selections. Natural baits (mackerel and squid) are recommended for catching P. sanguinolentus in crab fisheries. Full article

The flexibility and versatility associated with autonomous mobile robots (AMR) have facilitated their integration into different types of industries and tasks. However, as the main objective of their implementation on the factory floor is to optimize processes and, consequently, the time associated with them, it is necessary to take into account the environment and congestion to which they are subjected. Localization, on the shop floor and in real time, is an important requirement to optimize the AMRs’ trajectory management, thus avoiding livelocks and deadlocks during their movements in partnership with manual forklift operators and logistic trains. Threeof the most commonly used localization techniques in indoor environments (time of flight, angle of arrival, and time difference of arrival), as well as two of the most commonly used indoor localization methods in the industry (ultra-wideband, and ultrasound), are presented and compared in this paper. Furthermore, it identifies and compares three industrial indoor localization solutions: Qorvo, Eliko Kio, and Marvelmind, implemented in an industrial mobile platform, which is the main contribution of this paper. These solutions can be applied to both AMRs and other mobile platforms, such as forklifts and logistic trains. In terms of results, the Marvelmind system, which uses an ultrasound method, was the best solution. Full article

This study proposes a solution for Active Simultaneous Localization and Mapping (Active SLAM) of robots in unknown indoor environments using a combination of Deep Deterministic Policy Gradient (DDPG) path planning and the Cartographer algorithm. To enhance the convergence speed of the DDPG network and minimize collisions with obstacles, we devised a unique reward function that integrates exploration and exploitation strategies. The exploration strategy allows the robot to achieve the shortest running time and movement trajectory, enabling efficient traversal of unmapped environments. Moreover, the exploitation strategy introduces active closed loops to enhance map accuracy. We conducted experiments using the simulation platform Gazebo to validate our proposed model. The experimental results demonstrate that our model surpasses other Active SLAM methods in exploring and mapping unknown environments, achieving significant grid completeness of 98.7%. Full article

The COVID-19 pandemic has underscored the necessity for rapid contact tracing as a means to effectively suppress the spread of infectious diseases. Existing contact tracing methods leverage location-based or distance-based detection to identify contact with a confirmed patient. Existing contact tracing methods have encountered challenges in practical applications, stemming from the tendency to classify even casual contacts, which carry a low risk of infection, as close contacts. This issue arises because the transmission characteristics of the virus have not been fully considered. This study addresses the above problem by proposing IntelliTrace, an intelligent method that introduces methodological innovations prioritizing shared environmental context over physical proximity. This approach more accurately assesses potential transmission events by considering the transmission characteristics of the virus, with a special focus on COVID-19. In this study, we present space-based indoor Wi-Fi contact tracing using machine learning for indoor environments and trajectory-based outdoor GPS contact tracing for outdoor environments. For an indoor environment, a contact is detected based on whether users are in the same space with the confirmed case. For an outdoor environment, we detect contact through judgments based on the companion statuses of people, such as the same movements in their trajectories. The datasets obtained from 28 participants who installed the smartphone application during a one-month experiment in a campus space were utilized to train and validate the performance of the proposed exposure-detection method. As a result of the experiment, IntelliTrace exhibited an F1 score performance of 86.84% in indoor environments and 94.94% in outdoor environments. Full article

Human movement anomalies in indoor spaces commonly involve urgent situations, such as security threats, accidents, and fires. This paper proposes a two-phase framework for detecting indoor human trajectory anomalies based on density-based spatial clustering of applications with noise (DBSCAN). The first phase of the framework groups datasets into clusters. In the second phase, the abnormality of a new trajectory is checked. A new metric called the longest common sub-sequence using indoor walking distance and semantic label (LCSS_IS) is proposed to calculate the similarity between trajectories, extending from the longest common sub-sequence (LCSS). Moreover, a DBSCAN cluster validity index (DCVI) is proposed to improve the trajectory clustering performance. The DCVI is used to choose the epsilon parameter for DBSCAN. The proposed method is evaluated using two real trajectory datasets: MIT Badge and sCREEN. The experimental results show that the proposed method effectively detects human trajectory anomalies in indoor spaces. With the MIT Badge dataset, the proposed method achieves 89.03% in terms of F1-score for hypothesized anomalies and above 93% for all synthesized anomalies. In the sCREEN dataset, the proposed method also achieves impressive results in F1-score on synthesized anomalies: 89.92% for rare location visit anomalies (τ = 0.5) and 93.63% for other anomalies. Full article

Due to the prevalence of COVID-19, providing safe environments and reducing the risks of virus exposure play pivotal roles in our daily lives. Contact tracing is a well-established and widely-used approach to track and suppress the spread of viruses. Most digital contact tracing systems can detect direct face-to-face contact based on estimated proximity, without quantifying the exposed virus concentration. In particular, they rarely allow for quantitative analysis of indirect environmental exposure due to virus survival time in the air and constant airborne transmission. In this work, we propose an indoor spatiotemporal contact awareness framework (iSTCA), which explicitly considers the self-containing quantitative contact analytics approach with spatiotemporal information to provide accurate awareness of the virus quanta concentration in different origins at various times. Smartphone-based pedestrian dead reckoning (PDR) is employed to precisely detect the locations and trajectories for distance estimation and time assessment without the need to deploy extra infrastructure. The PDR technique we employ calibrates the accumulative error by identifying spatial landmarks automatically. We utilized a custom deep learning model composed of bidirectional long short-term memory (Bi-LSTM) and multi-head convolutional neural networks (CNNs) for extracting the local correlation and long-term dependency to recognize landmarks. By considering the spatial distance and time difference in an integrated manner, we can quantify the virus quanta concentration of the entire indoor environment at any time with all contributed virus particles. We conducted an extensive experiment based on practical scenarios to evaluate the performance of the proposed system, showing that the average positioning error is reduced to less than 0.7 m with high confidence and demonstrating the validity of our system for the virus quanta concentration quantification involving virus movement in a complex indoor environment. Full article

This research aims at contributing to a seamless, integrated technology intelligent living system for solitary older adults at home. The capacitive intimate sensing module, that can be easily pasted to the existing home space element surfaces, daily objects, or home furniture, such as a wall, door, stairs, a chair, cabinet, table, sofa, etc, is developed in this research. This 30 × 30 cm sensing module can actively sense people’s physical behaviors and body movements in spaces. The signals acquired from the sensing modules in indoor spaces will then integrate into the controller system through the IoT application and logically define the behavior classification. From the preliminary analysis of observing the 80-year-old elderly subject’s daily activities, the movement trajectory of the ‘Move–Stop’ pattern is found. There will be a touch (T) and a touchless (TL) relationship between the body and the space elements or objects. The touchless or non-contact intimate relationship also can be divided into two types: 1. the body ‘Passes by’ (P) the spatial elements or objects, and 2. the body ‘Stays’ (S) in front of the object and performs activities. This research pasted eight sensing modules on nine objects in six spaces. Finally, the specific actions and life pattern can be recognized and analyzed through the developed IoT spatial behavior smart system and provide the customized intelligent application function for the elderly. Full article

With the prevalence of smartphones and the maturation of indoor positioning techniques, predicting the movement of a large number of customers in indoor environments has become a promising and challenging line of research in recent years. While most of the current predicting approaches that take advantage of mathematical methods perform well in outdoor settings, they exhibit poor performance in indoor environments. To solve this problem, in this study, a sequential similarity-based prediction approach which combines the spatial and semantic contexts into a unified framework is proposed. We first present a revised Longest Common Sub-Sequence (LCSS) algorithm to compute the spatial similarity of the indoor trajectories, and then a novel algorithm considering the indoor semantic R-tree is proposed to compute the semantic similarities; after this, a unified algorithm is considered to group the trajectories, and then the clustered trajectories are used to train the prediction models. Extensive performance evaluations were carried out on a real-world dataset collected from a large shopping mall to validate the performance of our proposed method. The results show that our approach markedly outperforms the baseline methods and can be used in real-world scenarios. Full article