Evaluation of Full-Body Gestures Performed by Individuals with Down Syndrome: Proposal for Designing User Interfaces for All Based on Kinect Sensor

Rio, Marta del; Martín-Gutiérrez, Jorge

doi:10.3390/s20143930

Cited by 3 publications

(4 citation statements)

References 43 publications

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“…The experiments proved to elicit functional performances, social behaviors, and emotional responses. The authors concluded that further empirical research is needed in this area as the experiment was limited to health care [19], [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Particle Physics Simulator for Scientific Education using Augmented Reality

Hyder¹,

Baloch²,

Saad³

et al. 2021

IJACSA

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In this era of fourth industrial revolution, young learners need to be equipped with 21 st century skills, such as critical thinking, creativity, communication, collaboration, innovation and problem solving. Augmented Reality (AR) based learning systems are an effective tool to embed these skills. This paper presents a detailed review of latest research on an AR-based learning systems. Furthermore, an AR-based learning system is proposed to demonstrate the particle physics experiments i.e. proton-proton collision and Higgs field. The proposed learning system algorithms are developed using particle system of unity 3D software. Then, Microsoft Kinect sensor is interfaced with unity 3D to create an immersive experience. Then, the qualitative analysis of the proposed system and latest AR-based learning systems is presented. Finally, the quantitative analysis of the proposed system is conducted. Overall, the results suggest that 85% of the participants recommended the proposed learning system.

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Section: Literature Reviewmentioning

confidence: 99%

Particle Physics Simulator for Scientific Education using Augmented Reality

Hyder¹,

Baloch²,

Saad³

et al. 2021

IJACSA

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“…Gesture recognition is a topic in computer science and language technology [ 1 ]. As an extremely efficient non-verbal interaction method, gesture interaction will provide strong technical support for excellence in emerging fields such as smart medical devices, assistive devices for the disabled, smart homes, and smart military operations [ 2 , 3 ]. Most of the current major research work on gesture recognition is focused on machine-vision-based recognition methods, which can pose many limitations in practical applications.…”

Section: Introductionmentioning

confidence: 99%

“…Based on its built-in algorithm, it can automatically identify and track the dynamic skeletal structure of the human body and apply it to the hand to research human gestures. Researchers have two methods of gesture recognition using Kinect: (1) recognition based on the dynamic skeleton of the human body [10]; (2) recognition based on spatial depth sensing [5]. In the first approach, Ren et al [11] obtained the skeleton data of 25 joint points of the human body by Kinect, obtained their coordinates in 3D space in real-time, and investigated the importance of each joint bone in dynamic gesture expression.…”

Section: Introductionmentioning

confidence: 99%

MEMS Devices-Based Hand Gesture Recognition via Wearable Computing

Miao

Gao

et al. 2023

Micromachines

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Gesture recognition has found widespread applications in various fields, such as virtual reality, medical diagnosis, and robot interaction. The existing mainstream gesture-recognition methods are primarily divided into two categories: inertial-sensor-based and camera-vision-based methods. However, optical detection still has limitations such as reflection and occlusion. In this paper, we investigate static and dynamic gesture-recognition methods based on miniature inertial sensors. Hand-gesture data are obtained through a data glove and preprocessed using Butterworth low-pass filtering and normalization algorithms. Magnetometer correction is performed using ellipsoidal fitting methods. An auxiliary segmentation algorithm is employed to segment the gesture data, and a gesture dataset is constructed. For static gesture recognition, we focus on four machine learning algorithms, namely support vector machine (SVM), backpropagation neural network (BP), decision tree (DT), and random forest (RF). We evaluate the model prediction performance through cross-validation comparison. For dynamic gesture recognition, we investigate the recognition of 10 dynamic gestures using Hidden Markov Models (HMM) and Attention-Biased Mechanisms for Bidirectional Long- and Short-Term Memory Neural Network Models (Attention-BiLSTM). We analyze the differences in accuracy for complex dynamic gesture recognition with different feature datasets and compare them with the prediction results of the traditional long- and short-term memory neural network model (LSTM). Experimental results demonstrate that the random forest algorithm achieves the highest recognition accuracy and shortest recognition time for static gestures. Moreover, the addition of the attention mechanism significantly improves the recognition accuracy of the LSTM model for dynamic gestures, with a prediction accuracy of 98.3%, based on the original six-axis dataset.

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“…Today they find new applications within the Industry 4.0 program. In particular, the Microsoft Kinect family has found applications in many research areas, including human-computer interaction [36][37][38], occupational health [39][40][41], physiotherapy [42][43][44][45], and daily life safety [46]. Furthermore, recently Microsoft released the Azure Kinect DK (also known as k4a) [47].…”

mentioning

confidence: 99%

A Body Tracking-Based Low-Cost Solution for Monitoring Workers’ Hygiene Best Practices during Pandemics

Manghisi

Fiorentino

Boccaccio

et al. 2020

Sensors

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Since its beginning at the end of 2019, the pandemic spread of the severe acute respiratory syndrome coronavirus 2 (Sars-CoV-2) caused more than one million deaths in only nine months. The threat of emerging and re-emerging infectious diseases exists as an imminent threat to human health. It is essential to implement adequate hygiene best practices to break the contagion chain and enhance society preparedness for such critical scenarios and understand the relevance of each disease transmission route. As the unconscious hand–face contact gesture constitutes a potential pathway of contagion, in this paper, the authors present a prototype system based on low-cost depth sensors able to monitor in real-time the attitude towards such a habit. The system records people’s behavior to enhance their awareness by providing real-time warnings, providing for statistical reports for designing proper hygiene solutions, and better understanding the role of such route of contagion. A preliminary validation study measured an overall accuracy of 91%. A Cohen’s Kappa equal to 0.876 supports rejecting the hypothesis that such accuracy is accidental. Low-cost body tracking technologies can effectively support monitoring compliance with hygiene best practices and training people in real-time. By collecting data and analyzing them with respect to people categories and contagion statistics, it could be possible to understand the importance of this contagion pathway and identify for which people category such a behavioral attitude constitutes a significant risk.

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Evaluation of Full-Body Gestures Performed by Individuals with Down Syndrome: Proposal for Designing User Interfaces for All Based on Kinect Sensor

Cited by 3 publications

References 43 publications

Particle Physics Simulator for Scientific Education using Augmented Reality

Particle Physics Simulator for Scientific Education using Augmented Reality

MEMS Devices-Based Hand Gesture Recognition via Wearable Computing

A Body Tracking-Based Low-Cost Solution for Monitoring Workers’ Hygiene Best Practices during Pandemics

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