IMUTube

Kwon, Hyeokhyen; Tong, Catherine; Haresamudram, Harish; Gao, Yan; Abowd, Gregory D.; Lane, Nicholas D.; Plötz, Thomas

doi:10.1145/3411841

Cited by 92 publications

(14 citation statements)

References 29 publications

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“…Using e.g. a feature extraction tool as seen in [20] could be used to create such larger datasets. Further, we will analyse how 1-layered LSTMs learn compared to 2-layered ones within the setting of HAR by replicating a similar analysis as performed by Karparthy et al [17].…”

Section: Discussionmentioning

confidence: 99%

Improving Deep Learning for HAR with Shallow LSTMs

Bock

Hölzemann

Moeller

et al. 2021

2021 International Symposium on Wearable Computers

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Recent studies in Human Activity Recognition (HAR) have shown that Deep Learning methods are able to outperform classical Machine Learning algorithms. One popular Deep Learning architecture in HAR is the DeepConvLSTM. In this paper we propose to alter the DeepConvLSTM architecture to employ a 1-layered instead of a 2-layered LSTM. We validate our architecture change on 5 publicly available HAR datasets by comparing the predictive performance with and without the change employing varying hidden units within the LSTM layer(s). Results show that across all datasets, our architecture consistently improves on the original one: Recognition performance increases up to 11.7% for the F1-score, and our architecture significantly decreases the amount of learnable parameters. This improvement over DeepConvLSTM decreases training time by as much as 48%. Our results stand in contrast to the belief that one needs at least a 2-layered LSTM when dealing with sequential data. Based on our results we argue that said claim might not be applicable to sensor-based HAR. CCS CONCEPTS• Computing methodologies → Neural networks; • Humancentered computing → Ubiquitous and mobile computing design and evaluation methods.

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Section: Discussionmentioning

confidence: 99%

Improving Deep Learning for HAR with Shallow LSTMs

Bock

Hölzemann

Moeller

et al. 2021

2021 International Symposium on Wearable Computers

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“…Zhang and Alshurafa (2020) proposed two deep generative cross-modal architectures for synthesising accelerometer data from video sequences. Kwon et al (2020Kwon et al ( , 2021 presented IMUTube, an automated processing pipeline for human activity recognition (HAR) that integrates existing computer vision and signal processing techniques to convert video of human activity into virtual streams of IMU data. A similar approach was chosen by Lämsä et al (2022), who used neural networks with VIDEO2IMU to generate IMU signals and features from monocular videos of human activities.…”

Section: Data Simulation and Synthesismentioning

confidence: 99%

Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation

Uhlenberg

Derungs

Amft

2023

Front. Bioeng. Biotechnol.

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We propose a co-simulation framework comprising biomechanical human body models and wearable inertial sensor models to analyse gait events dynamically, depending on inertial sensor type, sensor positioning, and processing algorithms. A total of 960 inertial sensors were virtually attached to the lower extremities of a validated biomechanical model and shoe model. Walking of hemiparetic patients was simulated using motion capture data (kinematic simulation). Accelerations and angular velocities were synthesised according to the inertial sensor models. A comprehensive error analysis of detected gait events versus reference gait events of each simulated sensor position across all segments was performed. For gait event detection, we considered 1-, 2-, and 4-phase gait models. Results of hemiparetic patients showed superior gait event estimation performance for a sensor fusion of angular velocity and acceleration data with lower nMAEs (9%) across all sensor positions compared to error estimation with acceleration data only. Depending on algorithm choice and parameterisation, gait event detection performance increased up to 65%. Our results suggest that user personalisation of IMU placement should be pursued as a first priority for gait phase detection, while sensor position variation may be a secondary adaptation target. When comparing rotatory and translatory error components per body segment, larger interquartile ranges of rotatory errors were observed for all phase models i.e., repositioning the sensor around the body segment axis was more harmful than along the limb axis for gait phase detection. The proposed co-simulation framework is suitable for evaluating different sensor modalities, as well as gait event detection algorithms for different gait phase models. The results of our analysis open a new path for utilising biomechanical human digital twins in wearable system design and performance estimation before physical device prototypes are deployed.

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“…Cross-modality data translation generates synthetic data in the target modality from real data in the source modality. The studies [18,21,35] generate synthetic IMU data from videos of human activities. The work [2] generates mmWave radar data from videos.…”

Section: Multimentioning

confidence: 99%

Interpersonal Distance Tracking with mmWave Radar and IMUs

Dai¹,

Shuai²,

Tan³

et al. 2023

Preprint

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Tracking interpersonal distances is essential for real-time social distancing management and ex-post contact tracing to prevent spreads of contagious diseases. Bluetooth neighbor discovery has been employed for such purposes in combating COVID-19, but does not provide satisfactory spatiotemporal resolutions. This paper presents ImmTrack, a system that uses a millimeter wave radar and exploits the inertial measurement data from user-carried smartphones or wearables to track interpersonal distances. By matching the movement traces reconstructed from the radar and inertial data, the pseudo identities of the inertial data can be transferred to the radar sensing results in the global coordinate system. The re-identified, radar-sensed movement trajectories are then used to track interpersonal distances. In a broader sense, ImmTrack is the first system that fuses data from millimeter wave radar and inertial measurement units for simultaneous user tracking and re-identification. Evaluation with up to 27 people in various indoor/outdoor environments shows ImmTrack's decimeters-seconds spatiotemporal accuracy in contact tracing, which is similar to that of the privacyintrusive camera surveillance and significantly outperforms the Bluetooth neighbor discovery approach. CCS CONCEPTS• Human-centered computing → Ambient intelligence; Mobile computing; • Computer systems organization → Sensor networks.

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IMUTube

Cited by 92 publications

References 29 publications

Improving Deep Learning for HAR with Shallow LSTMs

Improving Deep Learning for HAR with Shallow LSTMs

Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation

Interpersonal Distance Tracking with mmWave Radar and IMUs

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