Browsing by Subject "Internet of things"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Human Factors Affecting Logging Injury Incidents in Idaho and the Potential for Real-Time Location-Sharing Technology to Improve Safety
    (MDPI, 2018-10) Newman, Soren M.; Keefe, Robert F.; Brooks, Randall H.; Ahonen, Emily Q.; Wempe, Ann M.; Social and Behavioral Sciences, School of Public Health
    Human factors, including inadequate situational awareness, can contribute to fatal and near-fatal traumatic injuries in logging, which is among the most dangerous occupations in the United States. Real-time location-sharing technology may help improve situational awareness for loggers. We surveyed and interviewed professional logging contractors in Idaho to (1) characterize current perceptions of in-woods hazards and the human factors that lead to injuries; (2) understand their perspectives on using technology-based location-sharing solutions to improve safety in remote work environments; and (3) identify logging hazard scenarios that could be mitigated using location-sharing technology. We found production pressure, fatigue, and inexperience among the most-common factors contributing to logging injuries from the perspective of participants. Potential limitations of location-sharing technology identified included potential for distraction and cost. Contractors identified several situations where the technology may help improve safety, including (1) alerting workers of potential hand-faller injuries due to lack of movement; (2) helping rigging crews to maintain safe distances from yarded trees and logs during cable logging; and (3) providing a means for equipment operators to see approaching ground workers, especially in low-visibility situations.
  • Thumbnail Image
    Item
    Integrated wireless sensor system for efficient pre-fall detection
    (2015-04-13) Tiwari, Nikhil; Rizkalla, Maher E.; El-Sharkawy, Mohamed; Christopher, Lauren
    The life expectancy of humans in today's era have increased to a very large extent due to the advancement of medical science and technology. The research in medical science has largely been focused towards developing methods and medicines to cure a patient after a diagnosis of an ailment. It is crucial to maintain the quality of life and health of the patient. It is of most importance to provide a healthy life to the elderly as this particular demographic is the most severely affected by health issues, which make them vulnerable to accidents, thus lowering their independence and quality of life. Due to the old age, most of the people become weak and inefficient in carrying their weight, this increases the probability of falling when moving around. This research of iterative nature focuses on developing a device which works as a preventive measure to reduce the damage due to a fall. The research critically evaluates the best approach for the design of the Pre-Fall detection system. In this work, we develop two wearable Pre-Fall detection system with reduced hardware and practical design. One which provides the capability of logging the data on an SD card in CSV format so that the data can be analyzed, and second, capability to connect to the Internet through Wifi. In this work, data from multiple accelerometers attached at different locations of the body are analyzed in Matlab to find the optimum number of sensors and the best suitable position on the body that gives the optimum result. In this work, a strict set of considerations are followed to develop a flexible, practical and robust prototype which can be augmented with different sensors without changing the fundamental design in order to further advance the research. The performance of the system to distinguish between fall and non-fall is improved by selecting and developing the most suitable way of calculating the body orientation. The different ways of calculating the orientation of the body are scrutinized and realized to compare the performance using the hardware. To reduce the number of false positives, the system considers the magnitude and the orientation to make a decision.
  • Thumbnail Image
    Item
    Integration of UAVS with Real Time Operating Systems and Establishing a Secure Data Transmission
    (2019-08) Ravi, Niranjan; El-Sharkawy, Mohamed; King, Brian; Rizkalla, Maher
    In today’s world, the applications of Unmanned Aerial Vehicle (UAV) systems are leaping by extending their scope from military applications on to commercial and medical sectors as well. Owing to this commercialization, the need to append external hardware with UAV systems becomes inevitable. This external hardware could aid in enabling wireless data transfer between the UAV system and remote Wireless Sensor Networks (WSN) using low powered architecture like Thread, BLE (Bluetooth Low Energy). The data is being transmitted from the flight controller to the ground control station using a MAVlink (Micro Air Vehicle Link) protocol. But this radio transmission method is not secure, which may lead to data leakage problems. The ideal aim of this research is to address the issues of integrating different hardware with the flight controller of the UAV system using a light-weight protocol called UAVCAN (Unmanned Aerial Vehicle Controller Area Network). This would result in reduced wiring and would harness the problem of integrating multiple systems to UAV. At the same time, data security is addressed by deploying an encryption chip into the UAV system to encrypt the data transfer using ECC (Elliptic curve cryptography) and transmitting it to cloud platforms instead of radio transmission.
  • Thumbnail Image
    Item
    Symptom-Based COVID-19 Prognosis through AI-Based IoT: A Bioinformatics Approach
    (Hindawi, 2022-07-23) Pal, Madhumita; Parija, Smita; Mohapatra, Ranjan K.; Mishra, Snehasish; Rabaan, Ali A.; Al Mutair, Abbas; Alhumaid, Saad; Al-Tawfiq, Jaffar A.; Dhama, Kuldeep; Medicine, School of Medicine
    Objective: Internet of Things (IoT) integrates several technologies where devices learn from the experience of each other thereby reducing human-intervened likely errors. Modern technologies like IoT and machine learning enable the conventional to patient-specific approach transition in healthcare. In conventional approach, the biggest challenge faced by healthcare professionals is to predict a disease by observing the symptoms, monitoring the remote area patient, and also attending to the patient all the time after being hospitalised. IoT provides real-time data, makes decision-making smarter, and provides far superior analytics, and all these to help improve the quality of healthcare. The main objective of the work was to create an IoT-based automated system using machine learning models for symptom-based COVID-19 prognosis. Methods: Comparative analysis of predictive microbiology of COVID-19 from case symptoms using various machine learning classifiers like logistics regression, k-nearest neighbor, support vector machine, random forest, decision trees, Naïve Bayes, and gradient booster is reported here. For the sake of the validation and verification of the models, performance of each model based on the retrieved cloud-stored data was measured for accuracy. Results: From the accuracy plot, it was concluded that k-NN was more accurate (97.97%) followed by decision tree (97.79), support vector machine (97.42), logistics regression (96.50), random forest (90.66), gradient boosting classifier (87.77), and Naïve Bayes (73.50) in COVID-19 prognosis. Conclusion: The paper presents a health monitoring IoT framework having high clinical significance in real-time and remote healthcare monitoring. The findings reported here and the lessons learnt shall enable the healthcare system worldwide to counter not only this ongoing COVID but many other such global pandemics the humanity may suffer from time to come.