Smart Chair for Monitoring of Sitting Behavior

Huang, Mengjie; Gibson, I. M.; Yang, Rui

doi:10.18502/keg.v2i2.626

Cited by 38 publications

(31 citation statements)

References 7 publications

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“…However, the task of accurate activity recognition is challenging because human activity is complex and highly diverse [29] and numerous approaches have been investigated [33][34][35][36] including a variety of smart chair [37,38] and cushion [39,40] options. Despite the challenges, the objectives of activity recognition in relation to smart floor architectures have all been achieved to varying degrees.…”

Section: Background and Related Workmentioning

confidence: 99%

Activity recognition evaluation via machine learning

Rameka¹,

Connor²,

Kruse³

2019

ICST Transactions on Ambient Systems

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With the proliferation of relatively cheap Internet of Things (IoT) devices, smart environments have been highlighted as an example of how the IoT can make our lives easier. Each of these 'things' produces data which can work in unison to react to its users. Machine learning makes use of this data to make inferences about our habits and activities, such as our buying preferences or likely commute destinations. However, this level of human inclusion within the IoT relies on indirect inferences from the usage of these devices or services. Activity recognition is already a widely researched area and could provide a more direct way of including humans within this system. This research explores the feasibility of using a cost effective, unobtrusive, single modality ground-based sensor matrix to track subtle pressure changes to predict user activity, in an effort to assess its ability to act as an intermediary interface between humans and digital systems such as the IoT.

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Section: Background and Related Workmentioning

confidence: 99%

Activity recognition evaluation via machine learning

Rameka¹,

Connor²,

Kruse³

2019

ICST Transactions on Ambient Systems

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show abstract

“…Most of the available technologies to monitor posture are based on pressure maps obtained by the incorporation of electronic sensors at different points of the chair [5][6][7][8][9][10]. Nevertheless, electronic sensors exhibit limitations, one of the biggest being the use of electrical signals at the point of measurement, which makes them susceptible to catastrophic failure if in contact with liquids (in case of an accident or even cleaning), increases the risk of short circuit and/or electric fire, and even user safety.…”

Section: Introductionmentioning

confidence: 99%

Instrumented Office Chair With Low-Cost Plastic Optical Fiber Sensors for Posture Control and Work Conditions Optimization

et al. 2022

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People spend more and more time sitting and this habit has been shown to cause spine pathologies. Thus, the scientific community and the industry have become interested in instrumented chairs development to detect incorrect user postures. In this work we present the development and implementation of invisibles and non-intrusive plastic optical fiber sensor cells to monitor the posture and evaluate the ergonomic behavior of a seated person. The low-cost plastic optical fiber (POF) based sensing devices developed in this work, were implemented in an office chair, to evaluate the workers posture throughout their work day. In addition to the sensors, Android and PC software applications were developed, to provide real time feedback and alerts to the user whenever an inadequate posture is detected, or the seating position is the same for a long time. The proposed approach was evaluated in a study involving six users, and results show that it can detect the user's position with 96.6% accuracy.

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“…This conventional approach conceptualizes sitting activity as a person's static characteristics in a single day. Conversely, some studies that have tried to obtain a sitting posture using pressure sensors placed under a chair, armrests, and chair backrests (Cheng et al, 2013;Griffiths et al, 2014;Huang et al, 2017), merely describe the sitting posture, without providing insight into the nature of sitting behaviors. Such approaches to characterize sitting behavior overlook the fact that sitting is a highly complex process.…”

Section: Introductionmentioning

confidence: 99%

Low Back Pain Exacerbation Is Predictable Through Motif Identification in Center of Pressure Time Series Recorded During Dynamic Sitting

et al. 2021

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Background: Low back pain (LBP) is a common health problem — sitting on a chair for a prolonged time is considered a significant risk factor. Furthermore, the level of LBP may vary at different times of the day. However, the role of the time-sequence property of sitting behavior in relation to LBP has not been considered. During the dynamic sitting, small changes, such as slight or big sways, have been identified. Therefore, it is possible to identify the motif consisting of such changes, which may be associated with the incidence, exacerbation, or improvement of LBP.Method: Office chairs installed with pressure sensors were provided to a total of 22 office workers (age = 43.4 ± 8.3 years) in Japan. Pressure sensors data were collected during working days and hours (from morning to evening). The participants were asked to answer subjective levels of pain including LBP. Center of pressure (COP) was calculated from the load level, the changes in COP were analyzed by applying the Toeplitz inverse covariance-based clustering (TICC) analysis, COP changes were categorized into several states. Based on the states, common motifs were identified as a recurring sitting behavior pattern combination of different states by motif-aware state assignment (MASA). Finally, the identified motif was tested as a feature to infer the changing levels of LBP within a day. Changes in the levels of LBP from morning to evening were categorized as exacerbated, did not change, or improved based on the survey questions. Here, we present a novel approach based on social spider algorithm (SSA) and probabilistic neural network (PNN) for the prediction of LBP. The specificity and sensitivity of the LBP inference were compared among ten different models, including SSA-PNN.Result: There exists a common motif, consisting of stable sitting and slight sway. When LBP level improved toward the evening, the frequency of motif appearance was higher than when LBP was exacerbated (p < 0.05) or the level did not change. The performance of the SSA-PNN optimization was better than that of the other algorithms. Accuracy, precision, recall, and F1-score were 59.20, 72.46, 40.94, and 63.24%, respectively.Conclusion: A lower frequency of a common motif of the COP dynamic changes characterized by stable sitting and slight sway was found to be associated with the exacerbation of LBP in the evening. LBP exacerbation is predictable by AI-based analysis of COP changes during the sitting behavior of the office workers.

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Smart Chair for Monitoring of Sitting Behavior

Cited by 38 publications

References 7 publications

Activity recognition evaluation via machine learning

Activity recognition evaluation via machine learning

Instrumented Office Chair With Low-Cost Plastic Optical Fiber Sensors for Posture Control and Work Conditions Optimization

Low Back Pain Exacerbation Is Predictable Through Motif Identification in Center of Pressure Time Series Recorded During Dynamic Sitting

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