Human Motion Enhancement via Tobit Kalman Filter-Assisted Autoencoder

Lannan, Nate; Zhou, Le; Fan, Guoliang

doi:10.1109/access.2022.3157605

Cited by 5 publications

(2 citation statements)

References 57 publications

(140 reference statements)

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“…Further, to validate the performance of the proposed IoT-based recognition system, we have given a comparison in Table 6 with other state-of-the-art methodologies presented in the literature. It is evident from the table that our proposed system outperformed the others in terms of accuracy for Opportunity++ [ 59 , 60 ] and Berkeley-MHAD datasets [ 61 , 62 , 63 ].…”

Section: Dataset Experimental Setup and Resultsmentioning

confidence: 99%

Intelligent ADL Recognition via IoT-Based Multimodal Deep Learning Framework

Javeed,

Mudawi,

Alazeb

et al. 2023

Sensors

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Smart home monitoring systems via internet of things (IoT) are required for taking care of elders at home. They provide the flexibility of monitoring elders remotely for their families and caregivers. Activities of daily living are an efficient way to effectively monitor elderly people at home and patients at caregiving facilities. The monitoring of such actions depends largely on IoT-based devices, either wireless or installed at different places. This paper proposes an effective and robust layered architecture using multisensory devices to recognize the activities of daily living from anywhere. Multimodality refers to the sensory devices of multiple types working together to achieve the objective of remote monitoring. Therefore, the proposed multimodal-based approach includes IoT devices, such as wearable inertial sensors and videos recorded during daily routines, fused together. The data from these multi-sensors have to be processed through a pre-processing layer through different stages, such as data filtration, segmentation, landmark detection, and 2D stick model. In next layer called the features processing, we have extracted, fused, and optimized different features from multimodal sensors. The final layer, called classification, has been utilized to recognize the activities of daily living via a deep learning technique known as convolutional neural network. It is observed from the proposed IoT-based multimodal layered system’s results that an acceptable mean accuracy rate of 84.14% has been achieved.

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Section: Dataset Experimental Setup and Resultsmentioning

confidence: 99%

Intelligent ADL Recognition via IoT-Based Multimodal Deep Learning Framework

Javeed,

Mudawi,

Alazeb

et al. 2023

Sensors

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“…As a results, they do not need in advance the process and measurement covariance matrices and the transition function. Literature examples that combine state observer and neural network are also [140], [141], [142], [143].…”

Section: State Observer and Neural Networkmentioning

confidence: 99%

Denoising and Completion Filters for Human Motion Software: a Survey with Code

Martini,

Calanca,

Bombieri

2024

Preprint

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Software platforms used for human motion analysis are increasingly being utilized in various fields, ranging from Healthcare to Industry 5.0. However, the inherent inaccuracy of these platforms often results in noisy descriptions of human poses or time periods during which the information is even missing. As a consequence, data filtering for denoising or completion is a common yet fundamental step before data analysis. Over the years, different techniques have been proposed, from generalpurpose solutions based on low-pass filters to more advanced and embedded approaches based on state observers rather than deep learning. This survey presents the current state-of-the-art filtering solutions for denoising and completing data generated by software platforms for human motion analysis, with a focus on 3D positional data extrapolated through marker-based or markerless motion capture systems. It proposes a concise taxonomy based on the filter technology and application assumptions. For each class, it summarizes the basic concepts and reports application feedback collected from literature. The survey also includes implementation codes or links to the authors’ original codes, allowing readers to easily reproduce all the algorithms in different experimental settings (https://github.com/PARCO-LAB/mocap-refinement).

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