Information set based features for the speed invariant gait recognition

Medikonda, Jeevan; Hanmandlu, M.; Ketan, Panigrahi Bijaya

doi:10.1049/iet-bmt.2016.0136

Cited by 10 publications

(6 citation statements)

References 42 publications

(80 reference statements)

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“…Therefore, we are not being able to replace all of ANN's layers with Infor-set layers; otherwise, there would be no room for learning. In the future, we will study the Generalised Entropy Function (GEF) 12 , which has the potential to replace the layers of CNN since its parameters not only collect information from the source but can also be trained to capture the relationship between input and output. Furthermore, compared to conventional architectures, the number of parameters that must be learned will be substantially reduced.…”

Section: Discussionmentioning

confidence: 99%

Information-Set Deep Learning Architectures for Improving Classification of Noisy Patterns

Bhardwaj

Wang

et al. 2022

Preprint

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Deep learning models have been widely used in many supervised learning applications. These models, however, suffer from the problem of overfitting due to various types of uncertainty with deteriorating performance when facing data biases, class imbalance, or noise propagation. To address the critical problem of the absence of robust deep learning models, we propose Information-Set Deep learning (ISDL) architectures with four variants by integrating information set theory and deep learning principle. We describe the ISDL architectures, learning algorithms, and analytic workflows. We evaluate and compare the performance of the ISDL models and standard architectures using a noise-corrupted benchmark dataset. The experimental results show that the ISDL models can efficiently handle noise-dominated uncertainty and outperform peer architectures.

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Section: Discussionmentioning

confidence: 99%

Information-Set Deep Learning Architectures for Improving Classification of Noisy Patterns

Bhardwaj

Wang

et al. 2022

Preprint

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“…As mentioned above [33][34][35][36], the use of spatiotemporal feature has influence on recognition accuracy [38]. Inspired by this, to obtain a video data-driven method for automatic detection of NSSI behaviours, we proposed NssiDetection.…”

Section: Detection Algorithm Of Nssi Based On Spatiotemporal Features...mentioning

confidence: 99%

“…Medikonda et al. [34] and Zou et al. [35] have successfully achieved user behavioural biometric identification and gait recognition by using spatiotemporal feature extraction.…”

Section: Introductionmentioning

confidence: 99%

“…Medikonda et al [34] and Zou et al [35] have successfully achieved user behavioural biometric identification and gait recognition by using spatiotemporal feature extraction. Tran et al [36] have proposed a simple yet efficient method that employs 3D convolutional neural networks (C3D) trained on a large video dataset, which has demonstrated superior performance on various benchmarks.…”

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confidence: 99%

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Detection of non‐suicidal self‐injury based on spatiotemporal features of indoor activities

Yang

Luo

et al. 2023

IET Biometrics

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Non-suicide self-injury (NSSI) can be dangerous and difficult for guardians or caregivers to detect in time. NSSI refers to when people hurt themselves even though they have no wish to cause critical or long-lasting hurt. To timely identify and effectively prevent NSSI in order to reduce the suicide rates of patients with a potential suicide risk, the detection of NSSI based on the spatiotemporal features of indoor activities is proposed. Firstly, an NSSI behaviour dataset is provided, and it includes four categories that can be used for scientific research on NSSI evaluation. Secondly, an NSSI detection algorithm based on the spatiotemporal features of indoor activities (NssiDetection) is proposed. NssiDetection calculates the human bounding box by using an object detection model and employs a behaviour detection model to extract the temporal and spatial features of NSSI behaviour. Thirdly, the optimal combination schemes of NssiDetection is investigated by checking its performance with different behaviour detection methods and training strategies. Lastly, a case study is performed by implementing an NSSI behaviour detection prototype system. The prototype system has a recognition accuracy of 84.18% for NSSI actions with new backgrounds, persons, or camera angles. K E Y W O R D Sbehavioural sciences computing, computer vision, convolutional neural nets, feature extraction, object detection | INTRODUCTIONNon-suicidal self-injury (NSSI) [1] refers to the direct and repetitive intentional destruction of one's body in the absence of suicidal intent, and its typical behaviours include cutting, severe scratching, burning, biting, climbing and beating. More than 20% of adolescents and over 5% of adults commit self-injury [2,3]. NSSI is one of the emotional outlets for patients with depression, borderline personality disorder and other common mental diseases [4]. Particularly, patients with depression, accounting for 3.8% of the global population [5], are likely to commit NSSI according to the Global Health Data Exchange Platform.Usually, suicidal intent is absent in NSSI, but NSSI behaviours are meant for self-punishment or signal personal pain to the outside world, especially to important persons. NSSI is associated with potential suicide attempts, and this association implies that NSSI behaviours and their related psychology may already be on the same danger trajectory [6]. In addition, evidence shows that over time, raw emotions, such as anxiety, anger and sadness, along with additional feelings of guilt and shame will continue to exist and may even worsen [7].In conclusion, suicide and NSSI are not the same, but NSSI can be dangerous. Guardians or caregivers may have difficulty in determining when a person is hurting himself evenThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Swing phase starts when toe leaves the ground and ends just before HS. Double-Support is the phase when both legs touch the ground[2].…”

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confidence: 99%

Wearable wireless ultrasonic sensors for human gait analysis

Ashhar¹

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Boon for the continuous support of my PhD study and research, for his patience, motivation, enthusiasm, and immense knowledge. Without his guidance and persistent help, this research would not have been possible. I would also like to thank Dr. Mohammad Omar Khyam, Dr. Md Noor-A-Rahim and Dr. Keng He Kong for guiding me throughout my PhD and suggesting novel ideas for implementation. I would like to thank all the people who helped me while collecting data and all subjects who willingly volunteered for the experiments. This project would not have been successful without their passionate participation. I would like to thank Mr. Long Tien Siew and Ms. Leow-How Seok Lai for their support to use the laboratories and facilities in NTU. I also thank my friends Rijil Thomas, Qi Yongbin and Aamani Budhota for all the help. Finally, I must express my very profound gratitude to my parents and to my friends for providing me with unfailing support and continuous encouragement throughout my years of study. This accomplishment would not have been possible without them. Thank you. xi "Imagination is more important than knowledge. Knowledge is limited. Imagination encircles the world."

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Information set based features for the speed invariant gait recognition

Cited by 10 publications

References 42 publications

Information-Set Deep Learning Architectures for Improving Classification of Noisy Patterns

Information-Set Deep Learning Architectures for Improving Classification of Noisy Patterns

Detection of non‐suicidal self‐injury based on spatiotemporal features of indoor activities

Wearable wireless ultrasonic sensors for human gait analysis

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