FinePose

Rohal, Shubham; Shriram, Shreya; Nguyen, VP; Pan, Shijia

doi:10.1145/3539489.3539590

Cited by 1 publication

(3 citation statements)

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“…During that period, individuals often assume a posture characterized by the head’s protraction and spine flexion [ 26 ]. Therefore, three approaches have been devised to classify forward head posture: capturing how forward the head is, measuring the curvature of the spine, and measuring muscle stiffness levels [ 10 , 25 , 27 ]. According to Lee et al [ 25 ], a vision-based detection system was formed using a commercial depth camera.…”

Section: Related Workmentioning

confidence: 99%

“…The development of embedded systems has allowed the use of wearables for numerous applications, namely in tracking and monitoring an individual’s health. Therefore, a wearable device can be used to diagnose and detect various posture problems and monitor the progression of a disorder over time [ 10 , 11 ]. One such approach involves the use of inertial measurement units (IMUs), which can identify how forward the head is and how effective the wearable device is in mitigating the disorder [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, a wearable device can be used to diagnose and detect various posture problems and monitor the progression of a disorder over time [ 10 , 11 ]. One such approach involves the use of inertial measurement units (IMUs), which can identify how forward the head is and how effective the wearable device is in mitigating the disorder [ 10 , 11 ]. Therefore, it is essential to retrofit a conventional back brace which can observe the vital signs of UCS and monitor posture problems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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