An Intelligent Cost-Efficient System to Prevent the Improper Posture Hazards in Offices Using Machine Learning Algorithms

Arshad, Jehangir; Asim, Hafiza Mahnoor; Ashraf, Muhammad; Jaffery, Mujtaba Hussain; Zaidi, Shabih H.; Amentie, Melkamu Deressa

doi:10.1155/2022/7957148

Cited by 7 publications

(2 citation statements)

References 32 publications

(47 reference statements)

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“…Similarly, left-leaning and right-leaning postures are determined by left and right sensors, as shown in Fig. 2 in [30].…”

Section: Figure 1: Configuration Of Sensors On a Chairmentioning

confidence: 99%

Force Sensitive Resistors-Based Real-Time Posture Detection System Using Machine Learning Algorithms

Javaid,

Abbas,

Arshad

et al. 2023

CMC

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To detect the improper sitting posture of a person sitting on a chair, a posture detection system using machine learning classification has been proposed in this work. The addressed problem correlates to the third Sustainable Development Goal (SDG), ensuring healthy lives and promoting well-being for all ages, as specified by the World Health Organization (WHO). An improper sitting position can be fatal if one sits for a long time in the wrong position, and it can be dangerous for ulcers and lower spine discomfort. This novel study includes a practical implementation of a cushion consisting of a grid of 3 × 3 force-sensitive resistors (FSR) embedded to read the pressure of the person sitting on it. Additionally, the Body Mass Index (BMI) has been included to increase the resilience of the system across individual physical variances and to identify the incorrect postures (backward, front, left, and right-leaning) based on the five machine learning algorithms: ensemble boosted trees, ensemble bagged trees, ensemble subspace K-Nearest Neighbors (KNN), ensemble subspace discriminant, and ensemble RUSBoosted trees. The proposed arrangement is novel as existing works have only provided simulations without practical implementation, whereas we have implemented the proposed design in Simulink. The results validate the proposed sensor placements, and the machine learning (ML) model reaches a maximum accuracy of 99.99%, which considerably outperforms the existing works. The proposed concept is valuable as it makes it easier for people in workplaces or even at individual household levels to work for long periods without suffering from severe harmful effects from poor posture.

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“…Similarly, left-leaning and right-leaning postures are determined by left and right sensors, as shown in Fig. 2 in [30].…”

Section: Figure 1: Configuration Of Sensors On a Chairmentioning

confidence: 99%

Force Sensitive Resistors-Based Real-Time Posture Detection System Using Machine Learning Algorithms

Javaid,

Abbas,

Arshad

et al. 2023

CMC

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“…Because each study used different arrangements of sensors, the number of postures that their setup could detect also varied. For example, Arshad et al (2022) used force-sensitive resistor (FSR) sensors along with ultrasonic sensors to detect four postures, namely leaning right, left, forward, and back. Bourahmoune et al (2022) detected 15 postures using an arrangement of 9 pressure sensors, and Roh et al (2018) detected six postures with four load cells positioned on the corners of a seat.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning Based Automatic Ergonomic Assessment Using Webcam Data

Lu,

Hochgraf

2023

J Stud Res

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Primarily due to increasing computer use, people are spending more and more time sitting in front of a desk every day. However, prolonged sitting has been associated with tiredness, hypertension, and pain in areas like the lower back or shoulders. These symptoms arise for a variety of reasons, but musculoskeletal disorders in particular are largely associated with poor postures. The adverse results caused by poor postures can be controlled with proper training and monitoring. This study attempts to provide automatic ergonomic assessment using only webcam data. Since laptops, desktops, and phones are now widely available and equipped with built-in cameras, this solution is accessible and convenient for most people. More importantly, automatic posture assessment may help to prevent conditions associated with poor posture by giving reminders whenever improper posture occurs. To create our model, we make use of Mediapipe, which provides a solution to identifying keypoint locations from an image. By training our MLP classifier on this key-point data, we achieved a 96.96% test F1 score, indicating that our system serves as a convenient way to assess posture while maintaining high performance. To illustrate our results, we perform a final video classification by overlaying the model’s pre-dictions on each frame.

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AutoPose: Pose Estimation for Prevention of Musculoskeletal Disorders Using LSTM

Bassino-Riglos,

Mosqueira-Chacon,

Ugarte

2023

Communications in Computer and Information Science

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An Intelligent Cost-Efficient System to Prevent the Improper Posture Hazards in Offices Using Machine Learning Algorithms

Cited by 7 publications

References 32 publications

Force Sensitive Resistors-Based Real-Time Posture Detection System Using Machine Learning Algorithms

Force Sensitive Resistors-Based Real-Time Posture Detection System Using Machine Learning Algorithms

Deep-Learning Based Automatic Ergonomic Assessment Using Webcam Data

AutoPose: Pose Estimation for Prevention of Musculoskeletal Disorders Using LSTM

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