Advancements in Healthcare Using Wearable Technology

Rajendran, Sindhu; Chaudhari, Surabhi; Giridhar, Swathi

doi:10.1007/978-3-030-68723-6_5

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…As among IoT components, wearables currently have the most flexible and adaptive role for supporting real-time applications. So, they have been widely used for sportive activities, travelling and healthcare solutions [11]. Recently, as a trendy area, health tourism seems to have great potential for applications of wearables.…”

Section: Introductionmentioning

confidence: 99%

Wearables for Health Tourism: Perspectives and Model Suggestion

Kose,

Marmolejo-Saucedo,

Rodriguez-Aguilar

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Internet of Things (IoT) has been taking wide place in our daily lives. Among different solution ways in terms of IoT, wearables take a remarkable role because of their compact structures and the mobility. By using wearables, it is very easy to sense a person’s movements and gather characteristic data, which may be processed for desired outcomes if intelligent inferencing. As associated with this, wearables can be effectively used for health tourism operations. As wearables already proved their capabilities for healthcare-oriented applications, the perspective may be directed to health tourism purposes. In this way, positive contributions may be done in the context of not only patients’ well-being but also other actors such as health staff and tourism agencies.OBJECTIVES: Objective of this paper is to evaluate the potential of wearables in health tourism applications, provide a model suggestion, and evaluate it in the view of different actors enrolling in health tourism ecosystems. Within this objective, research targets were directed to the usage ways of wearables in health tourism, ensuring model structures as meeting with the digital transformation advantages, and gather some findings thanks to feedback by patients, health staff, and agencies.METHODS: The research firstly included some views on what is health tourism, how the IoT, mobile solutions as well as wearables may be included in the ecosystem. Following to that, the research ensured a model suggestion considering wearables and their connections to health tourism actors. Finally, the potentials of wearables and the model suggestion was evaluated by gathering feedback from potential / active health tourists, health staff, and agency staff. RESULTS: The research revealed that the recent advancements in wearables and the role of digital transformation affects health tourism. In this context, there is a great potential to track and manage states of all actors in a health tourism eco system. Thanks to data processing and digital systems, it is effective to rise fast and practical software applications for health tourism. In detail, this may be structured in a model where typical IoT and wearable interactions can be connected to sensors, databases, and the related users. According to the surveys done with potential / active health tourists, health staff, and agency staff, such a model has great effect to advance the health tourism.CONCLUSION: The research study shows positive perspectives for both present and future potentials of wearable and health tourism relation. It is remarkable that rapid advancements in IoT can trigger health tourism and the future of health tourism may be established over advanced applications including data and user-oriented relations.

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Section: Introductionmentioning

confidence: 99%

Wearables for Health Tourism: Perspectives and Model Suggestion

Kose,

Marmolejo-Saucedo,

Rodriguez-Aguilar

et al. 2024

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

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“…AI technologies, such as machine learning and computer vision, can analyse accident data to identify patterns and trends. Wearable technologies, such as smart helmets and vests, can monitor vital signs of emergency responders in real-time [10]. Autonomous vehicles can transport injured persons quickly and safely to medical facilities [11].…”

Section: Introductionmentioning

confidence: 99%

Drones for Road Accident Management: A Systematic Review

Gohari,

Ahmad,

Rahim

et al. 2023

IEEE Access

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The aim of this study is to conduct a systematic review of the academic literature regarding applications of drones in road accident management. It is attempted to address key research questions regarding its status, applications, solutions, challenges, and opportunities. It is the first scholarly study specifically devoted to this topic that we are aware of. A systematic search of Web of Science and Scopus databases yielded 26 relevant articles out of 147 initially identified records. The findings indicate that the emphasis in earlier studies has predominantly been on the accident investigation and analysis, with a specific focus on 3D accident scene reconstruction. This review also shows a growing trend in adopting emerging technologies like drones and integrating them with industry 4.0 technologies for efficient road accident management. Despite the existence of different solution methodologies and frameworks, there remains a dearth of studies examining their suitability for complex accident cases, real-life scenarios, and large-scale accidents. The paper ends with a discussion of the challenges faced by current road accident management systems, the importance of adopting advanced technologies, the prospects of drone research in road accident management, and drone-enabled data analysis for different types of road accidents.

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“…Therefore, this type of method is suitable for fixed scenes. Behavior recognition is more suitable, not suitable for long-term, continuous long jump take-off records [9][10][11]. Marcu [12] proposed a local sparse classification method and a distributed sparse method for behavior recognition.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Digital Long Jump Take‐off Wearable Sensor Monitoring System

et al. 2021

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The wearable sensor monitoring system builds a long jump take-off recognition network model based on different digital feature extraction methods (one-dimensional digital feature extraction method, two-dimensional digital feature extraction method, and feature extraction method combining one-dimensional digitization and recursion). Experimental verification and analysis are performed on the processed sample data, and the identification effects, advantages, and disadvantages of the four methods are obtained. First, the sensor behavior movement collection software is designed based on the Android system, and the collection time and frequency are specified at the same time. In addition, for the problem of multisensor behavior recognition, an effective result fusion method is proposed. In a multisensor behavior recognition system, constructing a parallel processing architecture is conducive to improving the rate of behavior recognition. To maintain or increase the rate of behavior recognition, the result fusion method plays a vital role. Finally, this paper analyzes the process of multitask behavior recognition and constructs a residual model that can effectively integrate multitask results and fully mine data information. The experimental results show that, for the monitoring of exercise volume, we use step count statistics to extract feature values that can distinguish activity types based on human motion characteristics. This paper proposes a sample autonomous learning method to find the optimal sample training set and avoid occurrence of overfitting problems. In the recognition of 11 types of long jump take-offs, the average accuracy rate reached 98.7%. The average replacement method is used to count the number of steps, which provides a data reference for the user’s daily exercise volume.

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Advancements in Healthcare Using Wearable Technology

Cited by 6 publications

References 24 publications

Wearables for Health Tourism: Perspectives and Model Suggestion

Wearables for Health Tourism: Perspectives and Model Suggestion

Drones for Road Accident Management: A Systematic Review

Analysis of Digital Long Jump Take‐off Wearable Sensor Monitoring System

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