Posture Detection Framework Using the Internet of Wearable Things

Basmaji, Tasnim; Yaghi, Maha; Zia, Huma; Qamhieh, Lana; Abueida, Doaa; Abueida, Maha; Ghazal, Mohammed

doi:10.1109/icmi55296.2022.9873656

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…The model was able to detect correct poses with 96% accuracy. [35] Each of these systems utilizes an IMU sensor to classify hunchback postures. Jiang et al [32] demonstrated that a vest design can be used to detect hunchback posture.…”

Section: Slouched and Hunchback Posturesmentioning

confidence: 99%

IoT-Based Solution for Detecting and Monitoring Upper Crossed Syndrome

Shaheen,

Kazim,

Eltawil

et al. 2023

Sensors

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A sedentary lifestyle has caused adults to spend more than 6 h seated, which has led to inactivity and spinal issues. This context underscores the growing sedentary behavior, exemplified by extended sitting hours among adults and university students. Such inactivity triggers various health problems and spinal disorders, notably Upper Crossed Syndrome (UCS) and its association with thoracic kyphosis, which can cause severe spinal curvature and related complications. Traditional detection involves clinical assessments and corrective exercises; however, this work proposes a multi-layered system for a back brace to detect, monitor, and potentially prevent the main signs of UCS. Building and using a framework that detects and monitors signs of UCS has facilitated patient–doctor interaction, automated the detection process for improved patient–physician coordination, and helped improve patients’ spines over time. The smart wearable brace includes inertial measurement unit (IMU) sensors targeting hunched-back postures. The IMU sensors capture postural readings, which are then used for classification. Multiple classifiers were used where the long short-term memory (LSTM) model had the highest accuracy of 99.3%. Using the classifier helped detect and monitor UCS over time. Integrating the wearable device with a mobile interface enables real-time data visualization and immediate feedback for users to correct and mitigate UCS-related issues.

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Section: Slouched and Hunchback Posturesmentioning

confidence: 99%