Body Pose Prediction Based on Motion Sensor Data and Recurrent Neural Network

Woźniak, Marcin; Wieczorek, Michał; Siłka, Jakub; Połap, Dawid

doi:10.1109/tii.2020.3015934

Cited by 58 publications

(23 citation statements)

References 25 publications

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“…Ambient assisted living (AAL) environments are becoming a more and more realistic opportunity due to the advances in Information and Communication technology that allow equipping environments with smart devices providing functionalities not available since recently. These smart environments may provide ubiquitous support to people, allowing to automatically control smart devices to evaluate the health state of users, to avoid dangerous situations, and to improve environmental comfort (Woźniak and Połap 2019;Lauraitis et al 2019;Ribino et al 2020;Wozniak et al 2020). Among the several applications of AAL, those in the healthcare domain are very attractive since they provide support to the elderly in their everyday life, partially relieving caregivers and/or nurses tasks.…”

Section: Introductionmentioning

confidence: 99%

Customisable assistive plans as dynamic composition of services with normed-QoS

Napoli

Ribino

Serino

2021

J Ambient Intell Human Comput

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Ambient assisted living solutions (AAL) aim to improve the quality of life, especially of elderly with cognitive and physical disabilities, by providing assistive tasks such as medication management, reminders of daily activities or medical tests. However, the loss of physical functions (such as mobility, eyesight and hearing), as well as the loss of cognitive functions (starting from amnesia to severe disabilities as dementia or Alzheimer), require appropriate solutions in order to offer personalised assistive tasks according to user needs and characteristics. In this paper, we propose a novel methodology that is based on the integration of a service-oriented approach with normative reasoning to automatically generate assistive tasks customised for different target user's profiles, and deployable in any AAL environment. The formalisation of the conceptual steps of the methodology is provided, together with a framework implementing them. The advantages of dynamic customisation obtained by decoupling the functional and not functional aspects of assistive technology are shown through a validation scenario.

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Section: Introductionmentioning

confidence: 99%

Customisable assistive plans as dynamic composition of services with normed-QoS

Napoli

Ribino

Serino

2021

J Ambient Intell Human Comput

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“…Convolutional neural networks achieved near human performance to multiple computer vision tasks and since then nearly all computer vision tasks have been re-examined from a deep learning perspective [26][27][28]. Recently, significant progress has been made due to new paradigms and improved network architectures.…”

Section: Race Detection Using Convolutional Neural Networkmentioning

confidence: 99%

Recognizing Human Races through Machine Learning—A Multi-Network, Multi-Features Study

2021

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The human face holds a privileged position in multi-disciplinary research as it conveys much information—demographical attributes (age, race, gender, ethnicity), social signals, emotion expression, and so forth. Studies have shown that due to the distribution of ethnicity/race in training datasets, biometric algorithms suffer from “cross race effect”—their performance is better on subjects closer to the “country of origin” of the algorithm. The contributions of this paper are two-fold: (a) first, we gathered, annotated and made public a large-scale database of (over 175,000) facial images by automatically crawling the Internet for celebrities’ images belonging to various ethnicity/races, and (b) we trained and compared four state of the art convolutional neural networks on the problem of race and ethnicity classification. To the best of our knowledge, this is the largest, data-balanced, publicly-available face database annotated with race and ethnicity information. We also studied the impact of various face traits and image characteristics on the race/ethnicity deep learning classification methods and compared the obtained results with the ones extracted from psychological studies and anthropomorphic studies. Extensive tests were performed in order to determine the facial features to which the networks are sensitive to. These tests and a recognition rate of 96.64% on the problem of human race classification demonstrate the effectiveness of the proposed solution.

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“…The major applications of the proposed FIT machine are face detection [ 9 ], security [ 10 ], malware threat detection [ 11 ], and body pose estimation [ 12 ] as well as collecting the face emotions. The face detection could detect any face from lost children to criminals as the FIT machine can collect faces for storage of the dataset, and later use them for model training.…”

Section: Introductionmentioning

confidence: 99%

The Extensive Usage of the Facial Image Threshing Machine for Facial Emotion Recognition Performance

Kim

Poulose

Han

2021

Sensors

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Facial emotion recognition (FER) systems play a significant role in identifying driver emotions. Accurate facial emotion recognition of drivers in autonomous vehicles reduces road rage. However, training even the advanced FER model without proper datasets causes poor performance in real-time testing. FER system performance is heavily affected by the quality of datasets than the quality of the algorithms. To improve FER system performance for autonomous vehicles, we propose a facial image threshing (FIT) machine that uses advanced features of pre-trained facial recognition and training from the Xception algorithm. The FIT machine involved removing irrelevant facial images, collecting facial images, correcting misplacing face data, and merging original datasets on a massive scale, in addition to the data-augmentation technique. The final FER results of the proposed method improved the validation accuracy by 16.95% over the conventional approach with the FER 2013 dataset. The confusion matrix evaluation based on the unseen private dataset shows a 5% improvement over the original approach with the FER 2013 dataset to confirm the real-time testing.

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Body Pose Prediction Based on Motion Sensor Data and Recurrent Neural Network

Cited by 58 publications

References 25 publications

Customisable assistive plans as dynamic composition of services with normed-QoS

Customisable assistive plans as dynamic composition of services with normed-QoS

Recognizing Human Races through Machine Learning—A Multi-Network, Multi-Features Study

The Extensive Usage of the Facial Image Threshing Machine for Facial Emotion Recognition Performance

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