A Practical Design and Implementation of a Low Cost Platform for Remote Monitoring of Lower Limb Health of Amputees in the Developing World

Mathur, Neha; Paul, Greig; Irvine, James; Abuhelala, Mohamed; Buis, Arjan; Glesk, Ivan

doi:10.1109/access.2016.2622163

Cited by 26 publications

(11 citation statements)

References 25 publications

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“…There are many recent studies for post-operative cases in which researchers proposed edge-based rehabilitation systems [1] for monitoring health complications or infections after treatment. One such study [57] proposes a system for monitoring the health of an orthotic for an amputated limb by tracking patient's gait and temperature. They employed edge nodes in the form of smartphones to collect and transfer health data to fog nodes, where ML-based techniques were implemented for feature extraction classification.…”

Section: B Edge Based Iot Systems For Rehabilitationmentioning

confidence: 99%

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Edge Intelligence and Internet of Things in Healthcare: A Survey

2021

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Section: B Edge Based Iot Systems For Rehabilitationmentioning

confidence: 99%

“…It has enabled humans to control robots using their brains without doing any action. Hence ML based IoT healthcare systems are being used to help patients with severe disabilities to lead a normal life [57].…”

Section: Machine Learning and Iot In Healthcare Frameworkmentioning

confidence: 99%

Edge Intelligence and Internet of Things in Healthcare: A Survey

2021

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“…The use of protective IoMT solutions is effective also in postoperative situations where health tracking is aimed at detecting infections or complications in rehabilitation systems [3] . Mathur et al [32] propose a solution to predict the health status of the residual limb in lower limb amputee by monitoring temperature and gait; here, a combination of edge devices (including an Android mobile) is used to capture and send data to a fog station that performs ML based prediction. In the same direction, Villeneuve et al [52] contribute with a study on the estimation of simplified human limb kinematics based on measurements from two lowpower accelerometers placed on the forearm.…”

Section: Rehabilitation Systemsmentioning

confidence: 99%

Trends in IoT based solutions for health care: Moving AI to the edge

Greco

Percannella

Ritrovato

et al. 2020

Pattern Recognition Letters

243

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In recent times, we assist to an ever growing diffusion of smart medical sensors and Internet of things devices that are heavily changing the way healthcare is approached worldwide. In this context, a combination of Cloud and IoT architectures is often exploited to make smart healthcare systems capable of supporting near realtime applications when processing and performing Artificial Intelligence on the huge amount of data produced by wearable sensor networks. Anyway, the response time and the availability of cloud based systems, together with security and privacy, still represent critical issues that prevents Internet of Medical Things (IoMT) devices and architectures from being a reliable and effective solution to the aim. Lately, there is a growing interest towards architectures and approaches that exploit Edge and Fog computing as an answer to compensate the weaknesses of the cloud. In this paper, we propose a short review about the general use of IoT solutions in health care, starting from early health monitoring solutions from wearable sensors up to a discussion about the latest trends in fog/edge computing for smart health.

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“…Sukor et al [21] developed algorithms to assess quality of pulse oximetry and blood pressure signals that acquired from a home environment. Mathur et al [22] presents a full mobile sensor platform that is constituted by readily purchased consumer components to monitor the health of patients with prosthetic lower limbs. The platform is also designed to be able to operate standalone without Internet connectivity.…”

Section: Self-care Technologymentioning

confidence: 99%

Surgical Wounds Assessment System for Self-Care

Chen

Hsu

Hung

et al. 2020

IEEE Trans. Syst. Man Cybern, Syst.

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The importance of effective surgical wound care cannot never be underestimated. Poorly managing surgical wounds may cause many serious complications. Thus, it raises the necessity to develop a patient-friendly self-care system which can help both patients and medical professionals to ensure the state of the surgical wounds without any special medical equipment. In this paper, a surgical wound assessment system for self-care is proposed. The proposed system is designed to enable patients capture surgical wound images of themselves by using a mobile device and upload these images for analysis. Combining image-processing and machine-learning techniques, the proposed method is composed of four phases. First, images are segmented into superpixels where each superpixel contains the pixels in the similar color distribution. Second, these superpixels corresponding to the skin are identified and the area of connected skin superpixels is derived. Third, surgical wounds will be extracted from this area based on the observation of the texture difference between skin and wounds. Lastly, state and symptoms of surgical wound will be assessed. Extensive experimental results are conducted. With the proposed method, more than 90% state assessment results are correct and more than 91% symptom assessment results consistent with the actual diagnosis. Moreover, case studies are provided to show the advantage and limitation of this system. These results show that this system could perform well in the practical self-care scenario.Index Terms-Artificial intelligence (AI), classification, health care service systems.

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A Practical Design and Implementation of a Low Cost Platform for Remote Monitoring of Lower Limb Health of Amputees in the Developing World

Cited by 26 publications

References 25 publications

Edge Intelligence and Internet of Things in Healthcare: A Survey

Edge Intelligence and Internet of Things in Healthcare: A Survey

Trends in IoT based solutions for health care: Moving AI to the edge

Surgical Wounds Assessment System for Self-Care

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