BodySim: A Multi-Domain Modeling and Simulation Framework for Body Sensor Networks Research and Design

Asare, Philip; Dickerson, Robert; Wu, Xia; Lach, John; Stankovic, John A.

doi:10.4108/icst.bodynets.2013.253694

Cited by 5 publications

(8 citation statements)

References 7 publications

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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%

“…Bodysim [21] takes a different approach in that the IMU data is modelled based on a 3D model of a human body (a 'human structure model') which is initialised using a Microsoft Kinect. This is combined with a 'human Dynamics Model' and a 'human to system properties relationship model' which can be used to generate readings from sensors placed at a configurable position on the simulated body.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Mapping Vicon Motion Tracking to 6-Axis IMU Data for Wearable Activity Recognition

Pellatt

Dewar

Philippides

et al. 2020

Smart Innovation, Systems and Technologies

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There are several large datasets available captured from motion tracking systems which could be useful to train wearable human activity recognition (HAR) systems, if only their spatial data could be mapped into the equivalent inertial measurement unit (IMU) data that would be sensed on body. In this paper we describe a mapping from 3D Vicon motion tracking data to data collected from a BlueSense on-body IMU. We characterise the error incurred in order to discern the extent to which it is possible to generate useful training data for a wearable activity recognition system from data collected with a motion capture system. We analyze this by mapping Vicon motion tracking data to rotational velocity and linear acceleration at the head, and compare this to actual gyroscope and accelerometer data collected by an IMU mounted on the head. In a 15 minute dataset comprising three static activities -sitting, standing and lying down -we find that 95% of the reconstructed gyroscope data is within an error of [-7.25;+7.46] • −1 , while 95% of the reconstructed accelerometer data was contained within [-96.1;+72.9] • . However, when we introduce more movement by including data collected while walking this increases to [-19.0;+18.2] • −1 for the gyroscope and [-208;+186] • for the accelerometer. We conclude that generating accurate IMU data from motion capture datasets is possible and could be useful in providing larger volumes

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Mapping Vicon Motion Tracking to 6-Axis IMU Data for Wearable Activity Recognition

Pellatt

Dewar

Philippides

et al. 2020

Smart Innovation, Systems and Technologies

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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%

Translating Videos into Synthetic Training Data for Wearable Sensor-Based Activity Recognition Systems Using Residual Deep Convolutional Networks

2021

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Human activity recognition (HAR) using wearable sensors has benefited much less from recent advances in Deep Learning than fields such as computer vision and natural language processing. This is, to a large extent, due to the lack of large scale (as compared to computer vision) repositories of labeled training data for sensor-based HAR tasks. Thus, for example, ImageNet has images for around 100,000 categories (based on WordNet) with on average 1000 images per category (therefore up to 100,000,000 samples). The Kinetics-700 video activity data set has 650,000 video clips covering 700 different human activities (in total over 1800 h). By contrast, the total length of all sensor-based HAR data sets in the popular UCI machine learning repository is less than 63 h, with around 38 of those consisting of simple mode of locomotion activities like walking, standing or cycling. In our research we aim to facilitate the use of online videos, which exist in ample quantities for most activities and are much easier to label than sensor data, to simulate labeled wearable motion sensor data. In previous work we already demonstrated some preliminary results in this direction, focusing on very simple, activity specific simulation models and a single sensor modality (acceleration norm). In this paper, we show how we can train a regression model on generic motions for both accelerometer and gyro signals and then apply it to videos of the target activities to generate synthetic Inertial Measurement Units (IMU) data (acceleration and gyro norms) that can be used to train and/or improve HAR models. We demonstrate that systems trained on simulated data generated by our regression model can come to within around 10% of the mean F1 score of a system trained on real sensor data. Furthermore, we show that by either including a small amount of real sensor data for model calibration or simply leveraging the fact that (in general) we can easily generate much more simulated data from video than we can collect its real version, the advantage of the latter can eventually be equalized.

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“…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%

Internet of Medical Things: A Review of Recent Contributions Dealing With Cyber-Physical Systems in Medicine

Gatouillat

Badr

Massot

et al. 2018

IEEE Internet Things J.

312

140

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The Internet of Medical Things designates the interconnection of communication-enabled medical-grade devices and their integration to wider-scale health networks in order to improve patients' health. However, because of the critical nature of health-related systems, the Internet of Medical Things still faces numerous challenges, more particularly in terms of reliability, safety and security. In this paper, we present a comprehensive literature review of recent contributions focused on improving the Internet of Medical Things through the use of formal methodologies provided by the cyber-physical systems community. We describe the practical application of the democratization of medical devices for both patients and health-care providers. We also identify unexplored research directions and potential trends to solve uncharted research problems.

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BodySim: A Multi-Domain Modeling and Simulation Framework for Body Sensor Networks Research and Design

Cited by 5 publications

References 7 publications

Mapping Vicon Motion Tracking to 6-Axis IMU Data for Wearable Activity Recognition

Mapping Vicon Motion Tracking to 6-Axis IMU Data for Wearable Activity Recognition

Translating Videos into Synthetic Training Data for Wearable Sensor-Based Activity Recognition Systems Using Residual Deep Convolutional Networks

Internet of Medical Things: A Review of Recent Contributions Dealing With Cyber-Physical Systems in Medicine

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