A review of wearable sensors based monitoring with daily physical activity to manage type 2 diabetes

AlShorman, Omar; Alshorman, Buthaynah; Alkahtani, Fahed

doi:10.11591/ijece.v11i1.pp646-653

Cited by 17 publications

(9 citation statements)

References 93 publications

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“…Recently, several studies have been conducted for RHM for diabetic patients based on mHealth technologies. These studies have focused on several related topics including diabetic management and control [15], [16], diabetes prevention [17], diabetes intervention program [18], diabetes self-efficacy [19], continuous glucose monitoring [20], glycemic control improvement [21], diabetic patients treatment [22], diabetes prediction system [23], diabetes care improvement [24], continuous and remote monitoring system [25], insulin dose management [26], and carbohydrate measurement [27]. However, Tables 1, 2, and 3 show existing mHealth studies for managing, diagnosing, tracking, detecting, and predicting diabetic mellitus, respectively.…”

Section: Related Workmentioning

confidence: 99%

Mobile health monitoring based studies for diabetes mellitus: a review

AlShorman

Masadeh

Alshorman³

2021

Bulletin EEI

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Diabetes as a chronic disease is considered to be a serious problem not only for diabetic patients but also for caregivers, families and countries. Hazardously, as an example, 16% of the Middle East population died every year because of diabetes as it is reported by World Health Organization (WHO). Therefore, it is crucial to utilize the recent advances and technologies to find the best instrument for diabetes monitoring and management. Recently, mobile health (mHealth) technologies have a vital role in the healthcare industrial world. Undoubtedly, mHealth technologies are used to manage, track, monitor, diagnose, and prevent chronic diseases including, diabetes. Certainly, the main advantages of mHealth include a real-time and continuous monitoring with high reliability, accessibility, and availability. In addition to that, mHealth is considered to be a fast, accurate, simple, cheap, comfortable, and safe technology. Hence, the proposed study aims to review existing mHealth studies for managing, diagnosing, tracking, detecting, and predicting diabetic mellitus. Moreover, challenges and future trends of this emerging topic are also discussed.

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Section: Related Workmentioning

confidence: 99%

Mobile health monitoring based studies for diabetes mellitus: a review

AlShorman

Masadeh

Alshorman³

2021

Bulletin EEI

Self Cite

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“…The prospects of our proposed methods could be applied in health-related solutions. For example, diabetes patient's wearable sensors [32] could be fused and sensor-body positions may affect the proper recognition of health conditions.…”

Section: Discussionmentioning

confidence: 99%

Leveraging Sensor Fusion and Sensor-Body Position for Activity Recognition for Wearable Mobile Technologies

Алам

Das

Tasjid

et al. 2021

Int. J. Interact. Mob. Technol.

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Smart devices like smartphones and smartwatches have made this world smarter. These wearable devices are created through complex research methodologies to make them more usable and interactive with its user. Various interactive mobile applications such as augmented reality (AR), virtual reality (VR) or mixed reality (MR) applications solely depend on the in-built sensors of the smart devices. A lot of facilities can be taken from these devices with sensors such as accelerometer and gyroscope. Different physical activities such as walking, jogging, sitting, etc., can be important for analysis like health state prediction and duration of exercise by using those sensors based on artificial intelligence. In this paper, we have implemented machine learning and deep learning algorithms to detect and recognize eight activities namely, walking, jogging, standing, walking upstairs, walking downstairs, sitting, sitting-in-a-car and cycling; with a maximum of 99.3% accuracy. A few activities are almost similar in action, such as sitting and sitting-in-a-car, but difficult to distinguish; which makes it more challenging to predict tasks. In this paper, we have hypothesized that with more sensors (sensor fusion) and data collection points (sensor-body positions) a wide range of activities can be recognized and the recognition accuracies can be increased. Finally, we showed that the combination of all the sensors data of both pocket/waist and wrist can be used to recognize a wide range of activities accurately. The possibility of using the proposed methodologies for futuristic mobile technologies is quite significant. The adaptation of most recent deep learning algorithms such as convolutional neural network (CNN) and bi-directional Long Short Time Memory (Bi-LSTM) demonstrated high credibility of the methods presented as experimentation.

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“…Furthermore, the digital signal processing (DSP) technique is always applied to extract the features of the signals and use it with related MLs [63]- [68]. In medical imaging applications, machine learning techniques suffer from several limitations due to the variety of possible shapes, locations, and intensity inhomogeneity [69]- [75]. These techniques relied heavily on manual functions developed by doctors and neuroradiologists in the field [76].…”

Section: Segmentation Techniquesmentioning

confidence: 99%

Brain stroke computed tomography images analysis using image processing: A Review

Ali

Abdullah

Saad

et al. 2021

IJ-AI

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Stroke is the second-leading cause of death globally; therefore, it needs immediate treatment to prevent the brain from damage. Neuroimaging technique for stroke detection such as computed tomography (CT) has been widely used for emergency setting that can provide precise information on an obvious difference between white and gray matter. CT is the comprehensively utilized medical imaging technology for bone, soft tissue, and blood vessels imaging. A fully automatic segmentation became a significant contribution to help neuroradiologists achieve fast and accurate interpretation based on the region of interest (ROI). This review paper aims to identify, critically appraise, and summarize the evidence of the relevant studies needed by researchers. Systematic literature review (SLR) is the most efficient way to obtain reliable and valid conclusions as well as to reduce mistakes. Throughout the entire review process, it has been observed that the segmentation techniques such as fuzzy C-mean, thresholding, region growing, k-means, and watershed segmentation techniques were regularly used by researchers to segment CT scan images. This review is also impactful in identifying the best automated segmentation technique to evaluate brain stroke and is expected to contribute new information in the area of stroke research.

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A review of wearable sensors based monitoring with daily physical activity to manage type 2 diabetes

Cited by 17 publications

References 93 publications

Mobile health monitoring based studies for diabetes mellitus: a review

Mobile health monitoring based studies for diabetes mellitus: a review

Leveraging Sensor Fusion and Sensor-Body Position for Activity Recognition for Wearable Mobile Technologies

Brain stroke computed tomography images analysis using image processing: A Review

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