<scp>Multi‐feature</scp> gait analysis approach using deep learning in constraint‐free environment

Sethi, Dimple; Prakash, Chandra; Bharti, Sourabh

doi:10.1111/exsy.13274

Cited by 2 publications

(2 citation statements)

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“…The ensemble learning method, with the use of multiple learning algorithms, can enable the multi-stage network to produce better predictions than a simpler network trained with only one learning algorithm. Sethi et al [32] developed an ensemble model based on CNN and LSTM for gait analysis. The inputs are skeleton and landmark data, which must be estimated first from the image.…”

Section: Deep Learning Modelmentioning

confidence: 99%

Gender Recognition Based on Gradual and Ensemble Learning from Multi-View Gait Energy Images and Poses

Leung,

Chan

2023

Sensors

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Image-based gender classification is very useful in many applications, such as intelligent surveillance, micromarketing, etc. One common approach is to adopt a machine learning algorithm to recognize the gender class of the captured subject based on spatio-temporal gait features extracted from the image. The image input can be generated from the video of the walking cycle, e.g., gait energy image (GEI). Recognition accuracy depends on the similarity of intra-class GEIs, as well as the dissimilarity of inter-class GEIs. However, we observe that, at some viewing angles, the GEIs of both gender classes are very similar. Moreover, the GEI does not exhibit a clear appearance of posture. We postulate that distinctive postures of the walking cycle can provide additional and valuable information for gender classification. This paper proposes a gender classification framework that exploits multiple inputs of the GEI and the characteristic poses of the walking cycle. The proposed framework is a cascade network that is capable of gradually learning the gait features from images acquired in multiple views. The cascade network contains a feature extractor and gender classifier. The multi-stream feature extractor network is trained to extract features from the multiple input images. Features are then fed to the classifier network, which is trained with ensemble learning. We evaluate and compare the performance of our proposed framework with state-of-the-art gait-based gender classification methods on benchmark datasets. The proposed framework outperforms other methods that only utilize a single input of the GEI or pose.

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Section: Deep Learning Modelmentioning

confidence: 99%

Gender Recognition Based on Gradual and Ensemble Learning from Multi-View Gait Energy Images and Poses

Leung,

Chan

2023

Sensors

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“…In the next paper, the authors Dimple Sethi et al (2023) have proposed a gait analysis in a markerless environment and divided into three stages: First, a markerless gait database using videos from MNIT RAMAN LABORA-TORY in Jaipur has been prepared. Second, the skeletal landmark data to generate kinematic gait characteristics comparable to gold standard marker-based techniques have been adapted.…”

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confidence: 99%

Special issue on International conference on computing and communication networks (ICCCN2022)

Gupta

2023

Expert Systems

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The past years have seen a flurry of activity in distributed computing and its related fields. Indeed, developments in computer communications and networks enabled the deployment of exciting new areas, including internet of things, vehicular networks, collaborative big data analysis and so on. The design and implementation of energy efficient future generation communication and networking technologies fostered the development of mobile, pervasive and large-scale computing technologies.The International Conference on Computing and Communications Networks (ICCCN 2022) aims to serve as a forum for exchanging the latest findings and experiences ranging from theoretical research to practical system development in all aspects of computing and networking. All submitted papers must have a substantial knowledge engineering component like AI/machine learning, etc. to be in scope of this special issue, otherwise the paper will get rejected straightaway. This special issue brings selected papers presented at the ICCCN 2022 conference. From around 45 submitted articles, only 12 papers were selected based on the reviews. Each paper was reviewed by at least two reviewers and went through at least two rounds of reviews. The brief contributions of these papers are discussed below.In the first paper of this special issue, the authors Minni Jain et al. ( 2023) have proposed the first approach for code-mixed Hindi-English social media text that comprises language identification, detection and correction of non-word (out of vocabulary) errors as well as real-word errors occurring simultaneously. A fuzzy graph between different words of the suggestive lists is generated using various semantic relations in Hindi WordNet. Word embeddings and fuzzy graph-based centrality measures are used to find the correct word. Several experiments are performed on different social media datasets taken from Instagram, Twitter, YouTube comments, Blogs and WhatsApp. The experimental results demonstrate that the proposed system corrects out-of-vocabulary words as well as real-word errors with a maximum recall of 0.90 and 0.67, respectively, for Dev_Hindi and 0.87 and 0.66, respectively, for Rom_Hindi. The authors Ritu Bibyan et al. (2023) in their study have proposed a prediction model based on LDA to study the content aspect and emotion

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Multi‐feature gait analysis approach using deep learning in constraint‐free environment

Cited by 2 publications

References 60 publications

Gender Recognition Based on Gradual and Ensemble Learning from Multi-View Gait Energy Images and Poses

Gender Recognition Based on Gradual and Ensemble Learning from Multi-View Gait Energy Images and Poses

Special issue on International conference on computing and communication networks (ICCCN2022)

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