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A comprehensive survey on support vector machine classification: Applications, challenges and trends

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Advances In Kernel Methods Support Vector Learning

Background: With the rise of mobile medicine, the development of new technologies such as smart sensing, and the popularization of personalized health concepts, the field of smart wearable devices has developed rapidly in recent years. Among them, medical wearable devices have become one of the most promising fields. These intelligent devices not only assist people in pursuing a healthier lifestyle but also provide a constant stream of health care data for disease diagnosis and treatment by actively recording physiological parameters and tracking metabolic status. Therefore, wearable medical devices have the potential to become a mainstay of the future mobile medical market.
Objective: Although previous reviews have discussed consumer trends in wearable electronics and the application of wearable technology in recreational and sporting activities, data on broad clinical usefulness are lacking. We aimed to review the current application of wearable devices in health care while highlighting shortcomings for further research. In addition to daily health and safety monitoring, the focus of our work was mainly on the use of wearable devices in clinical practice.
Methods: We conducted a narrative review of the use of wearable devices in health care settings by searching papers in PubMed, EMBASE, Scopus, and the Cochrane Library published since October 2015. Potentially relevant papers were then compared to determine their relevance and reviewed independently for inclusion.
Results: A total of 82 relevant papers drawn from 960 papers on the subject of wearable devices in health care settings were qualitatively analyzed, and the information was synthesized. Our review shows that the wearable medical devices developed so far have been designed for use on all parts of the human body, including the head, limbs, and torso. These devices can be classified into 4 application areas: (1) health and safety monitoring, (2) chronic disease management, (3) disease diagnosis and treatment, and (4) rehabilitation. However, the wearable medical device industry currently faces several important limitations that prevent further use of wearable technology in medical practice, such as difficulties in achieving user-friendly solutions, security and privacy concerns, the lack of industry standards, and various technical bottlenecks.
Conclusions: We predict that with the development of science and technology and the popularization of personalized health concepts, wearable devices will play a greater role in the field of health care and become better integrated into people’s daily lives. However, more research is needed to explore further applications of wearable devices in the medical field. We hope that this review can provide a useful reference for the development of wearable medical devices.

/pdf/wearable-health-devices-in-health-care-narrative-systematic-2f964x4hzh.pdf

Wearable Health Devices in Health Care: Narrative Systematic Review

The increasing ageing global population is causing an upsurge in ailments related to old age, primarily dementia and Alzheimer’s disease, frailty, Parkinson’s, and cardiovascular disease, but also a general need for general eldercare as well as active and healthy ageing. In turn, there is a need for constant monitoring and assistance, intervention, and support, causing a considerable financial and human burden on individuals and their caregivers. Interconnected sensing technology, such as IoT wearables and devices, present a promising solution for objective, reliable, and remote monitoring, assessment, and support through ambient assisted living. This paper presents a review of such solutions including both earlier review studies and individual case studies, rapidly evolving in the last decade. In doing so, it examines and categorizes them according to common aspects of interest such as health focus, from specific ailments to general eldercare; IoT technologies, from wearables to smart home sensors; aims, from assessment to fall detection and indoor positioning to intervention; and experimental evaluation participants duration and outcome measures, from acceptability to accuracy. Statistics drawn from this categorization aim to outline the current state-of-the-art, as well as trends and effective practices for the future of effective, accessible, and acceptable eldercare with technology.

IoT Wearable Sensors and Devices in Elderly Care: A Literature Review.

This review aims to present current advancements in wearable technologies and IoT-based applications to support independent living. The secondary aim was to investigate the barriers and challenges of wearable sensors and Internet-of-Things (IoT) monitoring solutions for older adults. For this work, we considered falls and activity of daily life (ADLs) for the ageing population (older adults). A total of 327 articles were screened, and 14 articles were selected for this review. This review considered recent studies published between 2015 and 2019. The research articles were selected based on the inclusion and exclusion criteria, and studies that support or present a vision to provide advancement to the current space of ADLs, independent living and supporting the ageing population. Most studies focused on the system aspects of wearable sensors and IoT monitoring solutions including advanced sensors, wireless data collection, communication platform and usability. Moderate to low usability/ user-friendly approach is reported in most of the studies. Other issues found were inaccurate sensors, battery/ power issues, restricting the users within the monitoring area/ space and lack of interoperability. The advancement of wearable technology and the possibilities of using advanced IoT technology to assist older adults with their ADLs and independent living is the subject of many recent research and investigation.

A Systematic Review of Wearable Sensors and IoT-Based Monitoring Applications for Older Adults – a Focus on Ageing Population and Independent Living

A system on chip for melanoma detection using FPGA-based SVM classifier

The Support Vector Machine (SVM) is a common machine learning tool that is widely used because of its high classification accuracy. Implementing SVM for embedded real-time applications is very challenging because of the intensive computations required. This increases the attractiveness of implementing SVM on hardware platforms for reaching high performance computing with low cost and power consumption. This paper provides the first comprehensive survey of current literature (2010-2015) of different hardware implementations of SVM classifier on Field-Programmable Gate Array (FPGA). A classification of existing techniques is presented, along with a critical analysis and discussion. A challenging trade-off between meeting embedded real-time systems constraints and high classification accuracy has been observed. Finally, some key future research directions are suggested.

Hardware Implementations of SVM on FPGA: A State-of-the-Art Review of Current Practice

Support vector machine (SVM) is a robust machine learning model with high classification accuracy. SVM is widely utilized for online classification in various real-time embedded applications. However, implementing SVM classification algorithm for an embedded system is challenging due to intensive and complicated computations required. Several works attempted to optimize performance and cost by implementing SVM in hardware, especially on field-programmable gate array (FPGA) as it is a promising platform for meeting challenging embedded systems constraints. This article presents a comprehensive survey of hardware architectures used for implementing SVM on FPGA over the period 2010–2019. We performed a critical analysis and comparison of existing works with in-depth discussions around limitations, challenges, and research gaps. We concluded that the primary research gap is overcoming the challenging trade-off between meeting critical embedded systems constraints and achieving efficient and precise classification. Finally, some future research directions are proposed, aiming to address such research gaps.

https://link.springer.com/content/pdf/10.1007/s42979-020-00128-9.pdf

FPGA Implementations of SVM Classifiers: A Review

Support Vector Machine (SVM) is a common classifier used for efficient classification with high accuracy. SVM shows high accuracy for classifying melanoma (skin cancer) clinical images within computer-aided diagnosis systems used by skin cancer specialists to detect melanoma early and save lives. We aim to develop a medical low-cost handheld device that runs a real-time embedded SVM-based diagnosis system for use in primary care for early detection of melanoma. In this paper, an optimized SVM classifier is implemented onto a recent FPGA platform using the latest design methodology to be embedded into the proposed device for realizing online efficient melanoma detection on a single system on chip/device. The hardware implementation results demonstrate a high classification accuracy of 97.9% and a significant acceleration factor of 26 from equivalent software implementation on an embedded processor, with 34% of resources utilization and 2 watts for power consumption. Consequently, the implemented system meets crucial embedded systems constraints of high performance and low cost, resources utilization and power consumption, while achieving high classification accuracy.

Shereen Afifi

Papers

A Systematic Review of Wearable Sensors and IoT-Based Monitoring Applications for Older Adults – a Focus on Ageing Population and Independent Living

A system on chip for melanoma detection using FPGA-based SVM classifier

Hardware Implementations of SVM on FPGA: A State-of-the-Art Review of Current Practice

FPGA Implementations of SVM Classifiers: A Review

SVM classifier on chip for melanoma detection