Experiments of Posture Estimation on Vehicles Using Wearable Acceleration Sensors

Yamato, Yoji

doi:10.1109/bigdatasecurity.2017.8

Cited by 23 publications

(5 citation statements)

References 27 publications

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“…Some of the proposed solutions relied on the use of wearable sensors [22,23,24,25], while others exploited non-invasive approaches in which the sensors were applied on the environment (e.g., chairs [26,27,28,29,30,31,32,33]). In this way, participants were not aware of being monitored, thus allowing the reproduction of real-life conditions.…”

Section: Introductionmentioning

confidence: 99%

A Sitting Posture Monitoring Instrument to Assess Different Levels of Cognitive Engagement

Bibbo

Carli

Conforto

et al. 2019

Sensors

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An office chair for analyzing the seated posture variation during the performance of a stress-level test is presented in this work. To meet this aim, we placed a set of textile pressure sensors both on the backrest and on the seat of the chair. The position of the sensors was selected for maximizing the detection of variations of user’s posture. The effectiveness of the designed system was evaluated through an experiment where increasing stress levels were obtained by administering a Stroop test. The collected results had been analyzed by considering three different time intervals based on the difficulty level of the test (low, medium, and high). A transition analysis conducted on postures assumed during the test showed that participants reached a different posture at the end of the test, when the cognitive engagement increased, with respect to the beginning. This evidence highlighted the presence of movement presumably due to the increased cognitive engagement. Overall, the performed analysis showed the proposed monitoring system could be used to identify body posture variations related to different levels of engagement of a seated user while performing cognitive tasks.

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Section: Introductionmentioning

confidence: 99%

A Sitting Posture Monitoring Instrument to Assess Different Levels of Cognitive Engagement

Bibbo

Carli

Conforto

et al. 2019

Sensors

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“…Users also can coordinate outer systems using service coordination technologies such as [61][62][63][64][65][66]. We also consider to use GPU/ FPGA servers to IoT applications [67][68][69][70][71][72][73][74].…”

Section: Related Workmentioning

confidence: 99%

Server Selection, Configuration and Reconfiguration Technology for IaaS Cloud with Multiple Server Types

Yamato

2017

J Netw Syst Manage

Self Cite

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We propose a server selection, configuration, reconfiguration and automatic performance verification technology to meet user functional and performance requirements on various types of cloud compute servers. Various servers mean there are not only virtual machines on normal CPU servers but also container or baremetal servers on strong graphic processing unit (GPU) servers or field programmable gate arrays (FPGAs) with a configuration that accelerates specified computation. Early cloud systems are composed of many PC-like servers, and virtual machines on these severs use distributed processing technology to achieve high computational performance. However, recent cloud systems change to make the best use of advances in hardware power. It is well known that baremetal and container performances are better than virtual machines performances. And dedicated processing servers, such as strong GPU servers for graphics processing, and FPGA servers for specified computation, have increased. Our objective for this study was to enable cloud providers to provision compute resources on appropriate hardware based on user requirements, so that users can benefit from high performance of their applications easily. Our proposed technology select appropriate servers for user compute resources from various types of hardware, such as GPUs and FPGAs, or set appropriate configurations or reconfigurations of FPGAs to use hardware power. Furthermore, our technology automatically verifies the performances of provisioned systems. We measured provisioning and automatic performance verification times to show the effectiveness of our technology.

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“…In the literature, several studies have been conducted to identify and analyze users’ seated postures. Some studies proposed the use of wearable sensors [ 13 , 14 ]. However, wearing a sensorized device differs from typical real-life conditions, as when users constantly felt that they were being monitored, their postures would drastically vary from their normal postures.…”

Section: Introductionmentioning

confidence: 99%

Implementing Machine Learning Algorithms to Classify Postures and Forecast Motions When Using a Dynamic Chair

Farhani

Zhou

Danielson³

et al. 2022

Sensors

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Many modern jobs require long periods of sitting on a chair that may result in serious health complications. Dynamic chairs are proposed as alternatives to the traditional sitting chairs; however, previous studies have suggested that most users are not aware of their postures and do not take advantage of the increased range of motion offered by the dynamic chairs. Building a system that identifies users’ postures in real time, as well as forecasts the next few postures, can bring awareness to the sitting behavior of each user. In this study, machine learning algorithms have been implemented to automatically classify users’ postures and forecast their next motions. The random forest, gradient decision tree, and support vector machine algorithms were used to classify postures. The evaluation of the trained classifiers indicated that they could successfully identify users’ postures with an accuracy above 90%. The algorithm can provide users with an accurate report of their sitting habits. A 1D-convolutional-LSTM network has also been implemented to forecast users’ future postures based on their previous motions, the model can forecast a user’s motions with high accuracy (97%). The ability of the algorithm to forecast future postures could be used to suggest alternative postures as needed.

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Experiments of Posture Estimation on Vehicles Using Wearable Acceleration Sensors

Cited by 23 publications

References 27 publications

A Sitting Posture Monitoring Instrument to Assess Different Levels of Cognitive Engagement

A Sitting Posture Monitoring Instrument to Assess Different Levels of Cognitive Engagement

Server Selection, Configuration and Reconfiguration Technology for IaaS Cloud with Multiple Server Types

Implementing Machine Learning Algorithms to Classify Postures and Forecast Motions When Using a Dynamic Chair

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