Proceedings of the 8th International Conference on Body Area Networks 2013
DOI: 10.4108/icst.bodynets.2013.253694
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BodySim: A Multi-Domain Modeling and Simulation Framework for Body Sensor Networks Research and Design

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Cited by 5 publications
(8 citation statements)
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“…Similar efforts in related works include [16] and [15], which generate acceleration data from motion capture data using a learned model and IMU data from 2D RGB videos via pose estimation, respectively. In addition, [19] and [21] generate IMU data from simulated 3D bodies, generated either from motion capture or from a 'Human dynamics model'. We cannot directly compare our error to these methods, since they do not characterise the error in absolute terms, only in terms of the effect on the accuracy of a classifier.…”
Section: Discussionmentioning
confidence: 99%
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“…Similar efforts in related works include [16] and [15], which generate acceleration data from motion capture data using a learned model and IMU data from 2D RGB videos via pose estimation, respectively. In addition, [19] and [21] generate IMU data from simulated 3D bodies, generated either from motion capture or from a 'Human dynamics model'. We cannot directly compare our error to these methods, since they do not characterise the error in absolute terms, only in terms of the effect on the accuracy of a classifier.…”
Section: Discussionmentioning
confidence: 99%
“…There has also been some work which has looked into simulating IMU data, either from source data collected in a different modality [19], [20] or from a 3D simulation of a moving body [21]. In [20], IMU data is simulated based on motion capture data, and several activity recognition classifiers are characterised on the simulated data, showing similar performance to those trained on real data.…”
Section: Related Workmentioning
confidence: 99%
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“…For example, ref. [15][16][17] provide a simulation environment where one can model human subjects with virtual sensor models to allow experimentation and exploration in virtual space of scenarios in inertial sensing. Depending on the application, specialised tools can be used.…”
Section: Simulating Imu Data Directly From 3d Trajectories In Virtual...mentioning
confidence: 99%
“…The particularity of these models is that they focus on a very specific physiological process, while keeping their practical implementation in consideration. More generic models have also been developed: a multiparametric fall detection model using electroencephalography and electromyography [49], an electromyography-based model of user intention for artificial legs [50]; general human body 3D model [51]; or even structured human interaction models to achieve better CPS reliability [52].…”
Section: A a Cps Approach For Iomt Devicesmentioning
confidence: 99%