scholarly journals Sensor Fusion-Based Cooperative Trail Following for Autonomous Multi-Robot System

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 823 ◽  
Author(s):  
Mingyang Geng ◽  
Shuqi Liu ◽  
Zhaoxia Wu
Keyword(s):  
Sensor Fusion ◽  
Sensor Data ◽  
Great Success ◽  
Trail Following ◽  
Robust Performance ◽  
Robot System ◽  
Threshold Mechanism ◽  
Local View ◽  
In The Wild

Autonomously following a man-made trail in the wild is a challenging problem for robotic systems. Recently, deep learning-based approaches have cast the trail following problem as an image classification task and have achieved great success in the vision-based trail-following problem. However, the existing research only focuses on the trail-following task with a single-robot system. In contrast, many robotic tasks in reality, such as search and rescue, are conducted by a group of robots. While these robots are grouped to move in the wild, they can cooperate to lead to a more robust performance and perform the trail-following task in a better manner. Concretely, each robot can periodically exchange the vision data with other robots and make decisions based both on its local view and the information from others. This paper proposes a sensor fusion-based cooperative trail-following method, which enables a group of robots to implement the trail-following task by fusing the sensor data of each robot. Our method allows each robot to face the same direction from different altitudes to fuse the vision data feature on the collective level and then take action respectively. Besides, considering the quality of service requirement of the robotic software, our method limits the condition to implementing the sensor data fusion process by using the “threshold” mechanism. Qualitative and quantitative experiments on the real-world dataset have shown that our method can significantly promote the recognition accuracy and lead to a more robust performance compared with the single-robot system.

Measuring patient engagement among cancer patients in eHealth and mHealth interventions on symptom management: an integrated review (Preprint)

2020 ◽  
Author(s):  
Juqing Zhao ◽  
Pei Chen ◽  
Guangming Wan
Keyword(s):  
Cancer Patients ◽  
Patient Engagement ◽  
Symptom Management ◽  
Subjective Experience ◽  
Participation Rate ◽  
Sensor Data ◽  
Quality Of Data ◽  
Mhealth Intervention

BACKGROUND There has been an increase number of eHealth and mHealth interventions aimed to support symptoms among cancer survivors. However, patient engagement has not been guaranteed and standardized in these interventions. OBJECTIVE The objective of this review was to address how patient engagement has been defined and measured in eHealth and mHealth interventions designed to improve symptoms and quality of life for cancer patients. METHODS Searches were performed in MEDLINE, PsychINFO, Web of Science, and Google Scholar to identify eHealth and mHealth interventions designed specifically to improve symptom management for cancer patients. Definition and measurement of engagement and engagement related outcomes of each intervention were synthesized. This integrated review was conducted using Critical Interpretive Synthesis to ensure the quality of data synthesis. RESULTS A total of 792 intervention studies were identified through the searches; 10 research papers met the inclusion criteria. Most of them (6/10) were randomized trial, 2 were one group trail, 1 was qualitative design, and 1 paper used mixed method. Majority of identified papers defined patient engagement as the usage of an eHealth and mHealth intervention by using different variables (e.g., usage time, log in times, participation rate). Engagement has also been described as subjective experience about the interaction with the intervention. The measurement of engagement is in accordance with the definition of engagement and can be categorized as objective and subjective measures. Among identified papers, 5 used system usage data, 2 used self-reported questionnaire, 1 used sensor data and 3 used qualitative method. Almost all studies reported engagement at a moment to moment level, but there is a lack of measurement of engagement for the long term. CONCLUSIONS There have been calls to develop standard definition and measurement of patient engagement in eHealth and mHealth interventions. Besides, it is important to provide cancer patients with more tailored and engaging eHealth and mHealth interventions for long term engagement.

scholarly journals How Good Is the STW Sensor? An Account from a Larger Shipping Company

2021 ◽  
Vol 9 (5) ◽  
pp. 465
Author(s):  
Angelos Ikonomakis ◽  
Ulrik Dam Nielsen ◽  
Klaus Kähler Holst ◽  
Jesper Dietz ◽  
Roberto Galeazzi
Keyword(s):  
High Frequency ◽  
Additional Data ◽  
Empirical Method ◽  
Influential Factor ◽  
Sensor Data ◽  
Sea State ◽  
Shipping Company ◽  
Frequency Sensor ◽  
Semi Empirical

This paper examines the statistical properties and the quality of the speed through water (STW) measurement based on data extracted from almost 200 container ships of Maersk Line’s fleet for 3 years of operation. The analysis uses high-frequency sensor data along with additional data sources derived from external providers. The interest of the study has its background in the accuracy of STW measurement as the most important parameter in the assessment of a ship’s performance analysis. The paper contains a thorough analysis of the measurements assumed to be related with the STW error, along with a descriptive decomposition of the main variables by sea region including sea state, vessel class, vessel IMO number and manufacturer of the speed-log installed in each ship. The paper suggests a semi-empirical method using a threshold to identify potential error in a ship’s STW measurement. The study revealed that the sea region is the most influential factor for the STW accuracy and that 26% of the ships of the dataset’s fleet warrant further investigation.

scholarly journals Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

2021 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Parag Narkhede ◽  
Rahee Walambe ◽  
Shruti Mandaokar ◽  
Pulkit Chandel ◽  
Ketan Kotecha ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Network Architecture ◽  
Cost Effective ◽  
Sensor Data ◽  
Dense Layer ◽  
Gaseous Emissions ◽  
Multiple Sensors ◽  
Single Output ◽  
Novel Approach ◽  
Single Sensor

With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor.

Low Cost Sensor Data Fusion in Omnidirectional Mobile Robot Feedback System to Improve the Navigation Accuracy

2014 ◽  
Vol 607 ◽  
pp. 791-794 ◽  
Author(s):  
Wei Kang Tey ◽  
Che Fai Yeong ◽  
Yip Loon Seow ◽  
Eileen Lee Ming Su ◽  
Swee Ho Tang
Keyword(s):  
Mobile Robot ◽  
Sensor Fusion ◽  
Low Cost ◽  
Feedback System ◽  
Sensor Data ◽  
Research Setting ◽  
Fusion Technique ◽  
Omnidirectional Mobile Robot ◽  
The Cost ◽  
Navigation Accuracy

Omnidirectional mobile robot has gained popularity among researchers. However, omnidirectional mobile robot is rarely been applied in industry field especially in the factory which is relatively more dynamic than normal research setting condition. Hence, it is very important to have a stable yet reliable feedback system to allow a more efficient and better performance controller on the robot. In order to ensure the reliability of the robot, many of the researchers use high cost solution in the feedback of the robot. For example, there are researchers use global camera as feedback. This solution has increases the cost of the robot setup fee to a relatively high amount. The setup system is also hard to modify and lack of flexibility. In this paper, a novel sensor fusion technique is proposed and the result is discussed.

scholarly journals Performance metrics in wireless sensor networks :a survey and outlook

2018 ◽  
Vol 7 (2.26) ◽  
pp. 25
Author(s):  
E Ramya ◽  
R Gobinath
Keyword(s):  
Wireless Sensor Networks ◽  
Quality Of Service ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Performance Metrics ◽  
Resource Constraints ◽  
Sensor Data ◽  
Wireless Sensor

Data mining plays an important role in analysis of data in modern sensor networks. A sensor network is greatly constrained by the various challenges facing a modern Wireless Sensor Network. This survey paper focuses on basic idea about the algorithms and measurements taken by the Researchers in the area of Wireless Sensor Network with Health Care. This survey also catego-ries various constraints in Wireless Body Area Sensor Networks data and finds the best suitable techniques for analysing the Sensor Data. Due to resource constraints and dynamic topology, the quality of service is facing a challenging issue in Wireless Sensor Networks. In this paper, we review the quality of service parameters with respect to protocols, algorithms and Simulations. 

scholarly journals Sensor Fusion for Recognition of Activities of Daily Living

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4029 ◽  
Author(s):  
Jiaxuan Wu ◽  
Yunfei Feng ◽  
Peng Sun
Keyword(s):  
Time Series ◽  
Sensor Fusion ◽  
Daily Living ◽  
Single Point ◽  
Recognition System ◽  
Sensor Data ◽  
Multiple Sensors ◽  
Life Patterns ◽  
Functional Capabilities ◽  
Correction Technique

Activity of daily living (ADL) is a significant predictor of the independence and functional capabilities of an individual. Measurements of ADLs help to indicate one’s health status and capabilities of quality living. Recently, the most common ways to capture ADL data are far from automation, including a costly 24/7 observation by a designated caregiver, self-reporting by the user laboriously, or filling out a written ADL survey. Fortunately, ubiquitous sensors exist in our surroundings and on electronic devices in the Internet of Things (IoT) era. We proposed the ADL Recognition System that utilizes the sensor data from a single point of contact, such as smartphones, and conducts time-series sensor fusion processing. Raw data is collected from the ADL Recorder App constantly running on a user’s smartphone with multiple embedded sensors, including the microphone, Wi-Fi scan module, heading orientation of the device, light proximity, step detector, accelerometer, gyroscope, magnetometer, etc. Key technologies in this research cover audio processing, Wi-Fi indoor positioning, proximity sensing localization, and time-series sensor data fusion. By merging the information of multiple sensors, with a time-series error correction technique, the ADL Recognition System is able to accurately profile a person’s ADLs and discover his life patterns. This paper is particularly concerned with the care for the older adults who live independently.

scholarly journals Challenges and opportunities for monitoring wild Nile crocodiles with scute mark-recapture photography

Koedoe ◽  
2018 ◽  
Vol 60 (1) ◽  
Author(s):  
Bernard W.T. Coetzee ◽  
Sam M. Ferreira ◽  
Kristine Maciejewski
Keyword(s):  
Conservation Status ◽  
Field Conditions ◽  
High Turnover ◽  
Mark Recapture ◽  
Non Invasive ◽  
Open Population ◽  
Challenges And Opportunities ◽  
In Captivity ◽  
In The Wild

The global conservation status of Nile crocodiles (Crocodylus niloticus) was last assessed in 1996. The species presents particular difficulty in monitoring because it can be cryptic, require expertise to handle, and caudal tail tags and transmitters are often lost. Some studies advocate mark-recapture techniques based on photograph identification of the unique scute markings of crocodile tails as a non-invasive means of monitoring their populations. Researchers developed this method with crocodiles in captivity. In this study, we test the technique under field conditions by monitoring crocodiles from 2015 to 2017 in the Sunset Dam in the Kruger National Park. Using a Cormack-Jolly-Seber open population model, we found that the dam may host 15–30 individuals, but that there is a high turnover of individuals and much uncertainty in model outputs. The dam’s population thus has high rates of immigration and emigration. The method proved challenging under field conditions, as there was bias in identifying scute markings consistently. The efficient use of the method requires an exceptional quality of photographic equipment. Animal crypsis, however, remains an issue. In this study, we discuss how to improve the mark-recapture photography methodology, especially to adapt the technique for citizen science initiatives.Conservation implications: Using scute mark-recapture photography presents challenges under field conditions. These challenges require innovative, practical and analytical solutions to successfully use the technique before monitoring programmes, aimed at ensuring the persistence of crocodiles in the wild, can be implemented.

scholarly journals Loneliness and social interaction dynamics: An ambulatory assessment study in a student population

2021 ◽  
Author(s):  
Timon Elmer ◽  
Gerine M. A. Lodder
Keyword(s):  
Social Interaction ◽  
Social Interactions ◽  
Social Relationships ◽  
Sensor Data ◽  
Ambulatory Assessment ◽  
Survival Models ◽  
Qualitative And Quantitative ◽  
Fine Grained ◽  
The Mean

Loneliness is the feeling associated with a perceived lack of qualitative and quantitative aspects of social relationships. Loneliness is thus evidently intwined with individuals’ social behaviors in day-to-day life. Yet, little is known about the bidirectional pathways between loneliness and social interactions in daily life. In this study, we thus investigate (a) how loneliness predicts the frequency and duration of social interactions and (b) how frequency and duration of social interactions predict changes in loneliness. We examine these questions using fine-grained ambulatory-assessed sensor data of student’s social behavior covering 10 weeks (N_participants = 45, N_observations = 74,645). Before (T1) and after (T2) the ambulatory assessment phase, participants completed the UCLA loneliness scale, covering subscales on intimate, relational, and collective loneliness. Using multistate survival models, we show that T1 loneliness subscales are not significantly associated with differences in social interaction frequency and duration– only relational loneliness predicted shorter social interaction encounters. In predicting changes in loneliness subscales (T1-T2), only the mean duration of social interactions was negatively associated with collective loneliness. Thus, effects of loneliness on the structure of social interactions may be small or limited to specific forms of loneliness, implying that the quality of interactions may be more important.

scholarly journals Implications of properties and quality of indoor sensor data for building machine learning applications: Two case studies in smart campuses

2021 ◽  
pp. 108529
Author(s):  
Miia Lillstrang ◽  
Markus Harju ◽  
Guillermo del Campo ◽  
Gonzalo Calderon ◽  
Juha Röning ◽  
...  
Keyword(s):  
Machine Learning ◽  
Case Studies ◽  
Sensor Data ◽  
Machine Learning Applications

scholarly journals Comparative studies of some white wine cultivars in the sub-region of Belgrade and Nis

2004 ◽  
Vol 49 (1) ◽  
pp. 49-57
Author(s):  
Branislava Sivcev ◽  
Dragoslav Cvetkovic ◽  
Nevena Petrovic ◽  
Ivana Popadic
Keyword(s):  
Total Yield ◽  
Grape Juice ◽  
Experimental Period ◽  
White Wine ◽  
High Yield ◽  
Great Success ◽  
Air Temperatures ◽  
Grape Quality ◽  
Climatic Characteristics

In two wine-growing areas with different climatic characteristics 12 cultivars intended for the production of white wines were studied. The climatic characteristics include: mean annual air temperatures, mean vegetation air temperatures, heliothermal coefficient, hydrothermal coefficient and active temperatures sum from the moment of the growth of shoots to their full maturity for each studied cultivar. Elements of buds fruitfulness (6 features in total), yield, cluster mass, sugar quantity and grape quality were observed in both localities. In the vineyards of Grocka and Kutina high yielding varieties Ugni blanc and Dimyat can be grown with great success. Italian Riezling produced higher yields and better quality of unfermented grape juice in the vineyards of Grocka in comparison with the vineyards of Kutina. Pinot blanc in both localities was characterized by high yield, but the quality of unfermented grape juice was better in the vineyards of Grocka. Variety Rkaciteli produced high yield and good quality of unfermented grape juice in the experimental period in the vineyards of Kutina.

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