List:
Bhuttoa, G. M., Daudpotoa, J., & Jiskanib, I. M. (2016). Development of a wearable safety device for coal miners. International Journal of Chemical and Environmental Engineering, Volume 7, No. 4
Bi, C., Huang, J., Xing, G., Jiang, L., Liu, X., & Chen, M. (2019). Safewatch: A wearable hand motion tracking system for improving driving safety. ACM Transactions on Cyber-Physical Systems, 4(1), 1-21.
Chan, M., EstèVe, D., Fourniols, J. Y., Escriba, C., & Campo, E. (2012). Smart wearable systems: Current status and future challenges. Artificial intelligence in medicine, 56(3), 137-156.
Colley, A., Pfleging, B., Alt, F., & Häkkilä, J. (2020). Exploring public wearable display of wellness tracker data. International Journal of Human-Computer Studies, 138, 102408.
Dunn, J., Runge, R., & Snyder, M. (2018). Wearables and the medical revolution. Personalized medicine, 15(5), 429-448.
Fernández-Caramés, T. M., & Fraga-Lamas, P. (2018). Towards the Internet of smart clothing: A review on IoT wearables and garments for creating intelligent connected e-textiles. Electronics, 7(12), 405.
Ghasemzadeh, H., Fallahzadeh, R., & Jafari, R. (2016). A hardware-assisted energy-efficient processing model for activity recognition using wearables. ACM Transactions on Design Automation of Electronic Systems (TODAES), 21(4), 1-27.
Jebelli, H., Hwang, S., & Lee, S. (2018). EEG signal-processing framework to obtain high-quality brain waves from an off-the-shelf wearable EEG device. Journal of Computing in Civil Engineering, 32(1), 04017070.
JP Amorim, V., C Silva, M., & AR Oliveira, R. (2019). Software and Hardware Requirements and Trade-Offs in Operating Systems for Wearables: A Tool to Improve Devices’ Performance. Sensors, 19(8), 1904.
Lavallière, M., Burstein, A. A., Arezes, P., & Coughlin, J. F. (2016). Tackling the challenges of an aging workforce with the use of wearable technologies and the quantified-self. Dyna, 83(197), 38-43.
Martinez-Hernandez, U., Boorman, L. W., & Prescott, T. J. (2017). Multisensory wearable interface for immersion and telepresence in robotics. IEEE Sensors Journal, 17(8), 2534-2541.
Nnaji, C., Okpala, I., & Awolusi, I. (2020). Wearable Sensing Devices: Potential Impact & Current Use for Incident Prevention. Professional Safety, 65(04), 16-24.
Khan, R., & Atiqur, M. (2018). Feasibility Analysis of AI based Wearable Data-driven Solution for Safety and Health in Sweden. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239442
Annotated Bibliography 1
Bhuttoa, G. M., Daudpotoa, J., & Jiskanib, I. M. (2016). Development of a wearable safety device for coal miners. International Journal of Chemical and Environmental Engineering, Volume 7, No. 4
The article reflects on the safety of coal miners as they are always in involved in challenging work. In other words, coal mining task that involves a huge risk requires to be avoided by providing the miners with a wearable helmet. The findings exemplify that wearable helmets help to initiate early caution intelligence on temperature. It has been argued that this wearable helmet is likely to create burden for coal miners. The module is useful as it reflects on diverse sensors that are used in wearable helmets. It could be inferred that the wearable helmet has the capacity to detect any vehicle that is near to an individual. The paper is 2016 International Journal and it has been cited 2 times but effective when compared to wearable smart helmets.
Annotated Bibliography 2
Bi, C., Huang, J., Xing, G., Jiang, L., Liu, X., & Chen, M. (2019). Safewatch: A wearable hand motion tracking system for improving driving safety. ACM Transactions on Cyber-Physical Systems, 4(1), 1-21.
The article is primarily concerned with the distraction that comes on the way of the individual as they are driving. The implication of the AI in the domain of driving has been found to be promising enough in discerning the implication of the same in terms of the reducing the number of road accidents that might occur effectively in the transport scenario. The usefulness of the article illustrates that the technological advancement would be crucial enough in detecting the motion of hands that are unsafe and warn the driver in case of further accidents that might take place in the scenario. Detection of gestures using the tracking sensors and implementing to the Smart helmet would help in making transport safer for riders. The paper is 2019 ACM Transactions on Cyber-Physical Systems and it has been cited 19 times.
Annotated Bibliography 3
Chan, M., EstèVe, D., Fourniols, J. Y., Escriba, C., & Campo, E. (2012). Smart wearable systems: Current status and future challenges. Artificial intelligence in medicine, 56(3), 137-156.
The article is concerned with the researches that has been conducted both in the field of academics as well as in the area of industry in order to develop the ways in which. The findings of the article illustrate that the shift has been marked in the area concerned with the SWS or smart wearable system. The article is useful as it reflects on the future implication of the SWS that might change the scenario of the health care where the individuals will be marked by their capabilities to ensure monitoring and management of health. The implication of the device that has been used can be noted to have crucial impact in monitoring the areas that are connected to health as well as take care of the mobility and the mental health issues of the individuals both indoors as well as outdoors. Integration of this device and sensors in helmet would make sure better health of the rider in crisis situation. The paper is 2012 Artificial intelligence in medicine, and it has been cited 708 times.
Annotated Bibliography 4
Colley, A., Pfleging, B., Alt, F., & Häkkilä, J. (2020). Exploring public wearable display of wellness tracker data. International Journal of Human-Computer Studies, 138, 102408.
The purpose of the article is to examine the way wearable are able to track wellness data. This in turn has helped to reflect on outlook of the individuals regarding wearable display embedded with AI aspects. The findings reflect on the fact that business-related devices as well as applications that allows wellness and activity data are gathered as well as shared online and are made available to group of markets. The usefulness of the article is that it shows the way wearable acts as activity trackers that are adopted broadly to gather data. The limitations lie in the context of sharing data that are carried out through wearable tracker. The article has carried out a range of approaches to share wellness as well as physiological information that are to be examined. Tracking of the data gathered by sensors would play an important role in making a better smart helmet. The paper is 2020 International Journal of Human-Computer Studies and it has not been cited.
Annotated Bibliography 5
Dunn, J., Runge, R., & Snyder, M. (2018). Wearables and the medical revolution. Personalized medicine, 15(5), 429-448.
The article shows that healthcare as well as medicine fields are getting impacted largely due to wearable sensors. This in turn has been making it easier to monitor health outside the clinic as well as predict health events. The findings show that wearable devices mostly in the form of helmets will be able to revolutionize biomedicine through mobile. The article has been useful as it shows the way wearable helmets provide opportunities in order to improve healthcare. The limitations lie in the fact that the accessibility of healthcare technology is likely to be hindered due to wearable. The accuracy of wearable device has also not been reflected in a proper way in the article. The paper is 2018 Personalized medicine and it has been cited 42 times.
Annotated Bibliography 6
Fernández-Caramés, T. M., & Fraga-Lamas, P. (2018). Towards the Internet of smart clothing: A review on IoT wearables and garments for creating intelligent connected e-textiles. Electronics, 7(12), 405.
The article shows that with each passing day, technology has turned out to be ubiquitous. The focus of this article has been to show that smart clothes seek for a balance in addition to providing cyber security. The findings show that smart wearable is embedded into smart clothing that are categorized based on different parameters. Smart wearable helmets embedded with AI aspects will enable the individuals to gather information from their Smartphone regarding their heart rate, temperature, stress as well as breathing. Certainly, gathering of heart rate, temperature, stress as well as breathing will help in archiving vital information about rider if any accidents happen. The article has been limited as it does not show that whether smart wearable will be able to reduce energy consumption. The gap remains as far as fabric costs to produce wearable helmet is concerned. The paper is 2018 Electronics and it has been cited 34 times.
Annotated Bibliography 7
Ghasemzadeh, H., Fallahzadeh, R., & Jafari, R. (2016). A hardware-assisted energy-efficient processing model for activity recognition using wearables. ACM Transactions on Design Automation of Electronic Systems (TODAES), 21(4), 1-27.
The article shows the way wellness applications makes use of wearable in order to develop real-time observation. The article shows that wearable comprises of psychological data that makes communication easier. The findings reflect on the fact that practical monitoring that is limited in real-life gets easy access due to wearable. It could be argued from the article that whether wearable sensors in the form of helmets are able to detect chronic diseases or not. The limitations lie in problem complexity as well as greedy solution as the methods do not involve simple way to solve the issues. The paper is 2016 ACM Transactions on Design Automation of Electronic Systems (TODAES) and it has been cited 16 times.
Annotated Bibliography 8
Jebelli, H., Hwang, S., & Lee, S. (2018). EEG signal-processing framework to obtain high-quality brain waves from an off-the-shelf wearable EEG device. Journal of Computing in Civil Engineering, 32(1), 04017070.
The article aims to investigate brain waves that take place due to wearable helmets and the extent to which these helmets are useful. The purpose of the article is to show that whether wearable helmets are able to carry out the operations of EEG. The findings show that the employees working in construction industry feel stressed out that leads to psychological problem. The wearable helmets in this case will help to monitor the psychological status through which the bikers have been going. The article is useful as it reflects on two types of usefulness that a wearable helmet could provide. The article provides a signal-processing structure that will help to gather high quality waves through brain from real construction areas which would be helpful in transport sector for safe riding experience. The paper is 2018 Journal of Computing in Civil Engineering and it has been cited 36 times.
Annotated Bibliography 9
JP Amorim, V., C Silva, M., & AR Oliveira, R. (2019). Software and Hardware Requirements and Trade-Offs in Operating Systems for Wearables: A Tool to Improve Devices’ Performance. Sensors, 19(8), 1904.
The purpose of the article is to show that requirements that are related to wearable devices ranges from hard to soft real-time restraints. The findings put an overview on the fact that wearable helmets have turned out to be a trending technology in the electronics division. Simple as well as complicated trial products are suggested using precise hardware modules that includes inertial measurement unit. The usefulness of the article lies in the fact that it shows a powerful hardware component raises the performance of wearable helmets. It could be concluded that attractive wearable solutions have taken place due to recent advancement in hardware. The paper is 2019 Sensors and it has been cited 2 times.
Annotated Bibliography 10
Lavallière, M., Burstein, A. A., Arezes, P., & Coughlin, J. F. (2016). Tackling the challenges of an aging workforce with the use of wearable technologies and the quantified-self. Dyna, 83(197), 38-43.
The article shows that wearable helmets embedded with AI aspects are required for ageing population who are working to earn a living wage. The purpose of the article is to lay out implications to carry out interventions with wearable technology. The findings detail that ageing workforce has been turning into a considerable problem globally. In order to reduce workforce risk, it has become imperative to introduce wearable helmets to protect them. The article is useful as it shows the way wearable technology has been transforming the work environment to a large extent. The paper is 2016 Dyna and it has been cited 17 times.
Annotated Bibliography 11
Martinez-Hernandez, U., Boorman, L. W., & Prescott, T. J. (2017). Multisensory wearable interface for immersion and telepresence in robotics. IEEE Sensors Journal, 17(8), 2534-2541.
The article shows that in order to make the individuals get a feel about telepresence, the requirement to produce wearable garments in remote areas has increased. The purpose is to create wearable helmets embedded with AI features to help the individuals receive multisensory feedback that takes place from touch as well as vision. The findings show that the interface for wearable garments is developed for Microsoft Windows operating systems. The article has been useful as it shows the way individuals goes through telepresence experience. It is limited in the sense that tele-presence requires data that are based on multiple formats. It could be inferred that wearable helmets will make bikers interaction easier in remote areas. The paper is 2017 IEEE Sensors Journal and it has been cited 17 times.
Annotated Bibliography 12
Nnaji, C., Okpala, I., & Awolusi, I. (2020). Wearable Sensing Devices: Potential Impact & Current Use for Incident Prevention. Professional Safety, 65(04), 16-24.
The article provides an overview on reduction in accidents in construction industries due to introduction of wearable helmets. This in turn has reduced increased complexities thus decreasing job pressures. The findings demonstrate that the construction industry has reached saturation due to wearable that helped to prevent accidents. The article shows that demand for wearable have increased astronomically in the construction market in the form of helmets as well as wristbands. This in-built AI featured helmets has increased safety rates in construction industry to a large extent. The article has some limitations in the form of data that has been provided. In other words, the researcher completely depended on trends as well as experiences from previous cases. The paper is 2020 Professional Safety and it has not been cited.
Annotated Bibliography 13
Khan, R., & Atiqur, M. (2018). Feasibility Analysis of AI based Wearable Data-driven Solution for Safety and Health in Sweden. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239442
The article is concerned with the implication of the wearable on AI, in order to enhance the health and safety of the individuals. The aim of the article has also been directed towards finding the suitability of the usage of the technology that has been concerned as a part of the report. The major findings of the article show that in order to avoid frequent accidents of blue-collared employees, it is imperative to use wearable helmets. The article also brings under its concern the implication of the market. The article is useful as it categorizes the industries into high priority as well as medium priority laboratories thus making it clear which industry wearable helmets with in-built AI features for safety. The article shows that blue collared employees should be provided with safety as they are always involved in tricky work. As a result, the importance of AI wearable helmets should be emphasized for rider’s safety on road. The paper is 2018 Feasibility Analysis of AI based Wearable Data-driven Solution for Safety and Health in Sweden.
Self-Evaluation Report on Originality
The completion of the article leads us to realize that the research which has been done using the papers, journals, books and primo (by CSU) are developing a great knowledge of the issues and areas of the problem related to my project. I have learned many new things in this research which have cleared my thoughts towards what I want to describe from my research. The originality report shows how real is the work done. My originality report shows the 2% of similarity which are about the article names. I believe that the research which has been done for this literature should be understood and explained in our own understanding. I have applied the same thing in my annotated bibliography description. Summing up this originality report helps me to have better understanding and a clear thought about what is the plagiarism and how it can be avoided.
References
Bhuttoa, G. M., Daudpotoa, J., & Jiskanib, I. M. (2016). Development of a wearable safety device for coal miners. International Journal of Chemical and Environmental Engineering, Volume 7, No. 4
Bi, C., Huang, J., Xing, G., Jiang, L., Liu, X., & Chen, M. (2019). Safewatch: A wearable hand motion tracking system for improving driving safety. ACM Transactions on Cyber-Physical Systems, 4(1), 1-21.
Chan, M., EstèVe, D., Fourniols, J. Y., Escriba, C., & Campo, E. (2012). Smart wearable systems: Current status and future challenges. Artificial intelligence in medicine, 56(3), 137-156.
Colley, A., Pfleging, B., Alt, F., & Häkkilä, J. (2020). Exploring public wearable display of wellness tracker data. International Journal of Human-Computer Studies, 138, 102408.
Dunn, J., Runge, R., & Snyder, M. (2018). Wearables and the medical revolution. Personalized medicine, 15(5), 429-448.
Fernández-Caramés, T. M., & Fraga-Lamas, P. (2018). Towards the Internet of smart clothing: A review on IoT wearables and garments for creating intelligent connected e-textiles. Electronics, 7(12), 405.
Ghasemzadeh, H., Fallahzadeh, R., & Jafari, R. (2016). A hardware-assisted energy-efficient processing model for activity recognition using wearables. ACM Transactions on Design Automation of Electronic Systems (TODAES), 21(4), 1-27.
Jebelli, H., Hwang, S., & Lee, S. (2018). EEG signal-processing framework to obtain high-quality brain waves from an off-the-shelf wearable EEG device. Journal of Computing in Civil Engineering, 32(1), 04017070.
JP Amorim, V., C Silva, M., & AR Oliveira, R. (2019). Software and Hardware Requirements and Trade-Offs in Operating Systems for Wearables: A Tool to Improve Devices’ Performance. Sensors, 19(8), 1904.
Lavallière, M., Burstein, A. A., Arezes, P., & Coughlin, J. F. (2016). Tackling the challenges of an aging workforce with the use of wearable technologies and the quantified-self. Dyna, 83(197), 38-43.
Martinez-Hernandez, U., Boorman, L. W., & Prescott, T. J. (2017). Multisensory wearable interface for immersion and telepresence in robotics. IEEE Sensors Journal, 17(8), 2534-2541.
Nnaji, C., Okpala, I., & Awolusi, I. (2020). Wearable Sensing Devices: Potential Impact & Current Use for Incident Prevention. Professional Safety, 65(04), 16-24.
Khan, R., & Atiqur, M. (2018). Feasibility Analysis of AI based Wearable Data-driven Solution for Safety and Health in Sweden. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239442