Providing a cushion for wireless healthcare application development

Hu, Yu; Stoelting, A; Wang, Ying; Zou, Yi; Sarrafzadeh, Majid

doi:10.1109/mpot.2009.934698

Cited by 25 publications

(8 citation statements)

References 6 publications

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“…As in the previous case, however, this study did not provide the accuracy of the classification approach and it is therefore difficult to interpret the quality of the reported seating posture classification. Hu and coworkers [27] presented their PoSeat , a smart seat cushion with the aim of preventing chronic back pain. In order to keep the costs low, they experimented with multiple placement schemes with 16 pressure sensors.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Approaches for Classifying Sitting Posture Based on Force and Acceleration Sensors

Zemp

Tanadini

Plüss

et al. 2016

BioMed Research International

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Occupational musculoskeletal disorders, particularly chronic low back pain (LBP), are ubiquitous due to prolonged static sitting or nonergonomic sitting positions. Therefore, the aim of this study was to develop an instrumented chair with force and acceleration sensors to determine the accuracy of automatically identifying the user's sitting position by applying five different machine learning methods (Support Vector Machines, Multinomial Regression, Boosting, Neural Networks, and Random Forest). Forty-one subjects were requested to sit four times in seven different prescribed sitting positions (total 1148 samples). Sixteen force sensor values and the backrest angle were used as the explanatory variables (features) for the classification. The different classification methods were compared by means of a Leave-One-Out cross-validation approach. The best performance was achieved using the Random Forest classification algorithm, producing a mean classification accuracy of 90.9% for subjects with which the algorithm was not familiar. The classification accuracy varied between 81% and 98% for the seven different sitting positions. The present study showed the possibility of accurately classifying different sitting positions by means of the introduced instrumented office chair combined with machine learning analyses. The use of such novel approaches for the accurate assessment of chair usage could offer insights into the relationships between sitting position, sitting behaviour, and the occurrence of musculoskeletal disorders.

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Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Approaches for Classifying Sitting Posture Based on Force and Acceleration Sensors

Zemp

Tanadini

Plüss

et al. 2016

BioMed Research International

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“…Nevertheless, despite all the previous works underlining the importance of keeping a correct posture while sitting for long hours, they focused on classifying the sitting posture and eventually providing feedback on the assumed sitting position without raising an alert to invite the user to correct their posture. In 2010, Hu et al [ 41 ] proposed a wireless system for posture monitoring that provided a visual warning in case an inappropriate position was detected for a prolonged time. The system acquires pressure data from an accelerometer, two pressure sensors in the seat, and four pressure sensors in the backrest of an office chair (the number of sensors is limited by the microcontroller capabilities) and is managed by a mobile application that analyzes the sitting posture using a machine learning-based algorithm and sends the stored posture readings to a remote web server.…”

Section: Related Workmentioning

confidence: 99%

IoT System for Real-Time Posture Asymmetry Detection

Mura

Gregorio

Lamberti

et al. 2023

Sensors

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The rise of the Internet of Things (IoT) has enabled the development of measurement systems dedicated to preventing health issues and monitoring conditions in smart homes and workplaces. IoT systems can support monitoring people doing computer-based work and avoid the insurgence of common musculoskeletal disorders related to the persistence of incorrect sitting postures during work hours. This work proposes a low-cost IoT measurement system for monitoring the sitting posture symmetry and generating a visual alert to warn the worker when an asymmetric position is detected. The system employs four force sensing resistors (FSR) embedded in a cushion and a microcontroller-based read-out circuit for monitoring the pressure exerted on the chair seat. Java-based software performs the real-time monitoring of the sensors’ measurements and implements an uncertainty-driven asymmetry detection algorithm. The shifts from a symmetric to an asymmetric posture and vice versa generate and close a pop-up warning message, respectively. In this way, the user is promptly notified when an asymmetric posture is detected and invited to adjust the sitting position. Every position shift is recorded in a web database for further analysis of the sitting behavior.

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“…In [36] the PoSeat system, a seat cushion consisting of 16 force sensors and an algorithm based on Vector Support Machines as posture classifier, was presented. The authors stated that it was capable of classifying five different positions, but the accuracy of the system was not disclosed.…”

Section: Related Workmentioning

confidence: 99%

IoT Device for Sitting Posture Classification Using Artificial Neural Networks

et al. 2021

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Nowadays, the percentage of time that the population spends sitting has increased substantially due to the use of computers as the main tool for work or leisure and the increase in jobs with a high office workload. As a consequence, it is common to suffer musculoskeletal pain, mainly in the back, which can lead to both temporary and chronic damage. This pain is related to holding a posture during a prolonged period of sitting, usually in front of a computer. This work presents a IoT posture monitoring system while sitting. The system consists of a device equipped with Force Sensitive Resistors (FSR) that, placed on a chair seat, detects the points where the user exerts pressure when sitting. The system is complemented with a Machine Learning model based on Artificial Neural Networks, which was trained to recognize the neutral correct posture as well as the six most frequent postures that involve risk of damage to the locomotor system. In this study, data was collected from 12 participants for each of the seven positions considered, using the developed sensing device. Several neural network models were trained and evaluated in order to improve the classification effectiveness. Hold-Out technique was used to guide the training and evaluation process. The results achieved a mean accuracy of 81% by means of a model consisting of two hidden layers of 128 neurons each. These results demonstrate that is feasible to distinguish different sitting postures using few sensors allocated in the surface of a seat, which implies lower costs and less complexity of the system.

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Providing a cushion for wireless healthcare application development

Cited by 25 publications

References 6 publications

Application of Machine Learning Approaches for Classifying Sitting Posture Based on Force and Acceleration Sensors

Application of Machine Learning Approaches for Classifying Sitting Posture Based on Force and Acceleration Sensors

IoT System for Real-Time Posture Asymmetry Detection

IoT Device for Sitting Posture Classification Using Artificial Neural Networks

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