Smart carpet for imaging of objects' footprint by photonic guided-path tomography

Cantoral-Ceballos, Jose A.; Nurgiyatna, Nurgiyatna; Scully, Patricia; Ozanyan, Krikor B.

doi:10.1109/afrcon.2011.6071986

Cited by 7 publications

(6 citation statements)

References 23 publications

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“…Cantoral-Ceballos et al [95] used a principally different approach to floor sensing: instead of point measurements, they used a distributed Plastic Optical Fiber (POF) sensor layer sandwiched unobtrusively between the top pile layer of a commercial carpet and deformable underlay, implementing Guided-Path Tomography [96] (iMAGiMAT, see figure 6). With frame rates of 256 Hz and spatial sampling adequate for inverting the data into footstep image frames, it was possible to capture in substantial detail the dynamics of an uninterrupted sequence of at least 4 footfalls at a time.…”

Section: Floor Sensorsmentioning

confidence: 99%

Deep Learning for Monitoring of Human Gait: A Review

2019

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The essential human gait parameters are briefly reviewed, followed by a detailed review of the state of the art in deep learning for the human gait analysis. The modalities for capturing the gait data are grouped according to the sensing technology: video sequences, wearable sensors, and floor sensors, as well as the publicly available datasets. The established artificial neural network architectures for deep learning are reviewed for each group, and their performance are compared with particular emphasis on the spatiotemporal character of gait data and the motivation for multi-sensor, multi-modality fusion. It is shown that by most of the essential metrics, deep learning convolutional neural networks typically outperform shallow learning models. In the light of the discussed character of gait data, this is attributed to the possibility to extract the gait features automatically in deep learning as opposed to the shallow learning from the handcrafted gait features.

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Section: Floor Sensorsmentioning

confidence: 99%

Deep Learning for Monitoring of Human Gait: A Review

2019

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“…This approach also allows, at the imaging stage, to exclude all static objects on the carpet (such as furniture) reconstructing only the dynamic footprint images by data inversion, in addition it accounts for any residual deformation present in the mat. Some pilot PGPT results with light (2 to 5 kg) household objects on a 0.8 m × 0.8 m PGPT imaging mat have been presented earlier [21]; however that pilot was not suitable for real-time footprint imaging. Based on the combination of the background given in Section III with the PGPT concept presented throughout this Section, we integrated a complete "intelligent carpet" system capable of mapping in real time the deformation induced by an average-weight person walking on it.…”

Section: Photonic Guided-path Tomographymentioning

confidence: 99%

“…In addition to sinogram recovery for further applying the inverse Radon transform, this approach allows fast and computationally efficient "center-of-mass" reconstruction [47] suitable for applications where the dynamics of the SUT is of greater interest than its internal distribution and the details of its contours. In hardware implementations, the utilization of the Parallel Center-of-Mass Algorithm (PCoMA) [21], [48] 1 allows a drastic reduction of the time for data inversion, down to microseconds, independent of the number of SUTs in the imaging frame.…”

Section: Imagingmentioning

confidence: 99%

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Intelligent Carpet System, Based on Photonic Guided-Path Tomography, for Gait and Balance Monitoring in Home Environments

et al. 2015

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We report on the photonic variant of the previously introduced guided-path tomography (GPT), by demonstrating a system for footstep imaging using plastic optical fiber (POF) sensors. The 1 m × 2 m sensor head is manufactured by attaching 80 POF sensors on a standard commercial carpet underlay. The sensing principle relies on the sensitivity of POF to bending, quantified by measuring light transmission. The photonic GPT system, comprising the sensor head with processing hardware and software, covered by a mass-production general-purpose carpet top, successfully performs footstep imaging and correctly displays the position and footfall of a person walking on the carpet in real time. We also present the implementation of fast footprint center of mass calculations, suitable for recording gait and footfall. A split-screen movie, showing the frame-by-frame camera-captured action next to the reproduced footprints, can be downloaded at http://ieeexplore.ieee.org Index Terms-Footstep imaging, gait, "intelligent carpet," parallel center of mass algorithm (PCoMA), photonic guidedpath tomography (PGPT), plastic optical fiber (POF).

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“…This work addresses in a novel way a number of challenges, identified in literature and by own observations, pertaining to the physical sensing layer, as well as the data processing. Signals are recorded with an original floor sensor system, specially designed, and built for optimal spatiotemporal sampling with multiple plastic optical fiber (POF) distributed sensors [17], [18], [19], [20]. Multiple sensor fusion is achieved by deep learning with convolutional neural networks (CNN) used to classify subjects' gait.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Analysis by Deep Learning of Gait Signatures From Floor Sensors

2021

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The recognition of gait pattern variation is of high importance to various industrial and commercial applications, including security, sport, virtual reality, gaming, robotics, medical rehabilitation, mental illness diagnosis, space exploration, and others. The purpose of this paper is to study the nature of gait variability in more detail, by identifying gait intervals responsible for gait pattern variations in individuals, as well as between individuals, using cognitive demanding tasks. This work uses deep learning methods for sensor fusion of 116 plastic optical fiber (POF) distributed sensors for gait recognition. The floor sensor system captures spatiotemporal samples due to varying ground reaction force (GRF) in multiples of up to 4 uninterrupted steps on a continuous 2x1 m area. We demonstrate classifications of gait signatures, achieving up to 100% F1-score with Convolutional Neural Networks (CNN), in the context of gait recognition of 21 subjects, with imposters and clients. Classifications under cognitive load, induced by 4 different dual tasks, manifested lower F1scores. Layer-Wise Relevance Propagation (LRP) methods are employed to decompose a trained neural network prediction to relevant standard events in the gait cycle, by generating a "heat map" over the input used for classification. This allows valuable insight into which parts of the gait spatiotemporal signal have the heaviest influence on the gait classification and consequently, which gait events, such as heel strike or toe-off, are mostly affected by cognitive load.

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Smart carpet for imaging of objects' footprint by photonic guided-path tomography

Cited by 7 publications

References 23 publications

Deep Learning for Monitoring of Human Gait: A Review

Deep Learning for Monitoring of Human Gait: A Review

Intelligent Carpet System, Based on Photonic Guided-Path Tomography, for Gait and Balance Monitoring in Home Environments

Spatiotemporal Analysis by Deep Learning of Gait Signatures From Floor Sensors

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