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Open AccessReview

The Complementary Role of Gestures in Spotted Hyena (Crocuta crocuta) Communication

by Andrew J. Laurita and Stephanie A. Poindexter

Animals 2025, 15(10), 1366; https://doi.org/10.3390/ani15101366 - 9 May 2025

Viewed by 2568

Spotted hyenas live in fission–fusion social societies, requiring them to adopt a flexible multimodal communication system across variable spatial scales. However, researchers have extensively studied acoustic and olfactory signals for conspecific communication compared to visual signals, especially in wild populations. Here, we reviewed [...] Read more.

Spotted hyenas live in fission–fusion social societies, requiring them to adopt a flexible multimodal communication system across variable spatial scales. However, researchers have extensively studied acoustic and olfactory signals for conspecific communication compared to visual signals, especially in wild populations. Here, we reviewed 46 articles on the Web of Science on social communication in wild and captive spotted hyena populations to synthesize our collective knowledge of the extent to which spotted hyenas utilize sensory cues to communicate and how flexible they are between captive and wild populations. Across all articles, 54% focused on acoustic communication (n = 25), 33% on olfaction (n = 15), leaving only 13% on vision (n = 6). Most of this research studied wild populations (82%; n = 38), leaving an intriguing gap in our knowledge of captive populations and their potential for developing behavioral innovations due to their robust social cognition (i.e., modifying behavioral form and/or function observed in wild populations to better accommodate the captive performer’s environment and social needs). Improving our understanding of innovation development in this species has possible benefits for studying behavioral evolution and improving captive welfare (e.g., identifying normal vs. stereotypic behavior) in this social carnivore. Full article

(This article belongs to the Section Mammals)

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18 pages, 5057 KB

Open AccessArticle

Road Traffic Gesture Autonomous Integrity Monitoring Using Fuzzy Logic

by Kwame Owusu Ampadu and Michael Huebner

Sensors 2025, 25(1), 152; https://doi.org/10.3390/s25010152 - 30 Dec 2024

Cited by 3 | Viewed by 2027

Abstract

Occasionally, four cars arrive at the four legs of an unsignalized intersection at the same time or almost at the same time. If each lane has a stop sign, all four cars are required to stop. In such instances, gestures are used to [...] Read more.

Occasionally, four cars arrive at the four legs of an unsignalized intersection at the same time or almost at the same time. If each lane has a stop sign, all four cars are required to stop. In such instances, gestures are used to communicate approval for one vehicle to leave. Nevertheless, the autonomous vehicle lacks the ability to participate in gestural exchanges. A sophisticated in-vehicle traffic light system has therefore been developed to monitor and facilitate communication among autonomous vehicles and classic car drivers. The fuzzy logic-based system was implemented and evaluated on a self-organizing network comprising eight ESP32 microcontrollers, all operating under the same program. A single GPS sensor connects to each microcontroller that also manages three light-emitting diodes. The ESPNow broadcast feature is used. The system requires no internet service and no large-scale or long-term storage, such as the driving cloud platform, making it backward-compatible with classical vehicles. Simulations were conducted based on the order and arrival direction of vehicles at three junctions. Results have shown that autonomous vehicles at four-legged intersections can now communicate with human drivers at a much lower cost with precise position classification and lane dispersion under 30 s. Full article

(This article belongs to the Special Issue Digital Signal Processing, Computational Methods and Sensor Networks for Engineering Applications: 2nd Edition)

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19 pages, 1712 KB

Open AccessArticle

Forward Hand Gesture Spotting and Prediction Using HMM-DNN Model

by Mahmoud Elmezain, Majed M. Alwateer, Rasha El-Agamy, Elsayed Atlam and Hani M. Ibrahim

Informatics 2023, 10(1), 1; https://doi.org/10.3390/informatics10010001 - 28 Dec 2022

Cited by 5 | Viewed by 4465

Abstract

Automatic key gesture detection and recognition are difficult tasks in Human–Computer Interaction due to the need to spot the start and the end points of the gesture of interest. By integrating Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs), the present research [...] Read more.

Automatic key gesture detection and recognition are difficult tasks in Human–Computer Interaction due to the need to spot the start and the end points of the gesture of interest. By integrating Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs), the present research provides an autonomous technique that carries out hand gesture spotting and prediction simultaneously with no time delay. An HMM can be used to extract features, spot the meaning of gestures using a forward spotting mechanism with varying sliding window sizes, and then employ Deep Neural Networks to perform the recognition process. Therefore, a stochastic strategy for creating a non-gesture model using HMMs with no training data is suggested to accurately spot meaningful number gestures (0–9). The non-gesture model provides a confidence measure, which is utilized as an adaptive threshold to determine where meaningful gestures begin and stop in the input video stream. Furthermore, DNNs are extremely efficient and perform exceptionally well when it comes to real-time object detection. According to experimental results, the proposed method can successfully spot and predict significant motions with a reliability of 94.70%. Full article

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12 pages, 13025 KB

Open AccessArticle

Multi-Sensor-Based Blind-Spot Reduction Technology and a Data-Logging Method Using a Gesture Recognition Algorithm Based on Micro E-Mobility in an IoT Environment

by Hyoduck Seo, Hyeonbo Kim, Kyesan Lee and Kyujin Lee

Sensors 2022, 22(3), 1081; https://doi.org/10.3390/s22031081 - 30 Jan 2022

Cited by 5 | Viewed by 3137

Abstract

Autonomous driving is evolving through the convergence of object recognition using multiple sensors in the fourth industrial revolution. In this paper, we propose a system that utilizes data logging to control the functions of micro e-mobility vehicles (MEVs) and to build a database [...] Read more.

Autonomous driving is evolving through the convergence of object recognition using multiple sensors in the fourth industrial revolution. In this paper, we propose a system that utilizes data logging to control the functions of micro e-mobility vehicles (MEVs) and to build a database for autonomous driving with a gesture recognition algorithm for use in an IoT environment. The proposed system uses multiple sensors installed in an MEV to log driving data as the vehicle operates and to recognize objects surrounding the MEV to remove blind spots. In addition, the proposed system is capable of multi-sensor control and data logging for the MEV based on a gesture recognition algorithm, and it can provide safety information to allow the system to address blind spots or unexpected situations by recognizing the appearances or gestures of pedestrians around the MEV. The proposed system can be applied and extended in various fields, such as 5G communication, autonomous driving, and AI, which are the core technologies of the fourth industrial revolution. Full article

(This article belongs to the Section Internet of Things)

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13 pages, 1760 KB

Open AccessArticle

3D Skeletal Joints-Based Hand Gesture Spotting and Classification

by Ngoc-Hoang Nguyen, Tran-Dac-Thinh Phan, Soo-Hyung Kim, Hyung-Jeong Yang and Guee-Sang Lee

Appl. Sci. 2021, 11(10), 4689; https://doi.org/10.3390/app11104689 - 20 May 2021

Cited by 9 | Viewed by 4851

Abstract

This paper presents a novel approach to continuous dynamic hand gesture recognition. Our approach contains two main modules: gesture spotting and gesture classification. Firstly, the gesture spotting module pre-segments the video sequence with continuous gestures into isolated gestures. Secondly, the gesture classification module [...] Read more.

This paper presents a novel approach to continuous dynamic hand gesture recognition. Our approach contains two main modules: gesture spotting and gesture classification. Firstly, the gesture spotting module pre-segments the video sequence with continuous gestures into isolated gestures. Secondly, the gesture classification module identifies the segmented gestures. In the gesture spotting module, the motion of the hand palm and fingers are fed into the Bidirectional Long Short-Term Memory (Bi-LSTM) network for gesture spotting. In the gesture classification module, three residual 3D Convolution Neural Networks based on ResNet architectures (3D_ResNet) and one Long Short-Term Memory (LSTM) network are combined to efficiently utilize the multiple data channels such as RGB, Optical Flow, Depth, and 3D positions of key joints. The promising performance of our approach is obtained through experiments conducted on three public datasets—Chalearn LAP ConGD dataset, 20BN-Jester, and NVIDIA Dynamic Hand gesture Dataset. Our approach outperforms the state-of-the-art methods on the Chalearn LAP ConGD dataset. Full article

(This article belongs to the Special Issue Deep Learning-Based Action Recognition)

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14 pages, 1172 KB

Open AccessArticle

Dynamic Gesture Recognition System with Gesture Spotting Based on Self-Organizing Maps

by Hiroomi Hikawa, Yuta Ichikawa, Hidetaka Ito and Yutaka Maeda

Appl. Sci. 2021, 11(4), 1933; https://doi.org/10.3390/app11041933 - 22 Feb 2021

Cited by 5 | Viewed by 3684

Abstract

In this paper, a real-time dynamic hand gesture recognition system with gesture spotting function is proposed. In the proposed system, input video frames are converted to feature vectors, and they are used to form a posture sequence vector that represents the input gesture. [...] Read more.

In this paper, a real-time dynamic hand gesture recognition system with gesture spotting function is proposed. In the proposed system, input video frames are converted to feature vectors, and they are used to form a posture sequence vector that represents the input gesture. Then, gesture identification and gesture spotting are carried out in the self-organizing map (SOM)-Hebb classifier. The gesture spotting function detects the end of the gesture by using the vector distance between the posture sequence vector and the winner neuron’s weight vector. The proposed gesture recognition method was tested by simulation and real-time gesture recognition experiment. Results revealed that the system could recognize nine types of gesture with an accuracy of 96.6%, and it successfully outputted the recognition result at the end of gesture using the spotting result. Full article

(This article belongs to the Special Issue Applied Cognitive Sciences)

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17 pages, 4202 KB

Open AccessArticle

Fluid Intake Monitoring System Using a Wearable Inertial Sensor for Fluid Intake Management

by Hsiang-Yun Huang, Chia-Yeh Hsieh, Kai-Chun Liu, Steen Jun-Ping Hsu and Chia-Tai Chan

Sensors 2020, 20(22), 6682; https://doi.org/10.3390/s20226682 - 22 Nov 2020

Cited by 20 | Viewed by 6806

Abstract

Fluid intake is important for people to maintain body fluid homeostasis. Inadequate fluid intake leads to negative health consequences, such as headache, dizziness and urolithiasis. However, people in busy lifestyles usually forget to drink sufficient water and neglect the importance of fluid intake. [...] Read more.

Fluid intake is important for people to maintain body fluid homeostasis. Inadequate fluid intake leads to negative health consequences, such as headache, dizziness and urolithiasis. However, people in busy lifestyles usually forget to drink sufficient water and neglect the importance of fluid intake. Fluid intake management is important to assist people in adopting individual drinking behaviors. This work aims to propose a fluid intake monitoring system with a wearable inertial sensor using a hierarchical approach to detect drinking activities, recognize sip gestures and estimate fluid intake amount. Additionally, container-dependent amount estimation models are developed due to the influence of containers on fluid intake amount. The proposed fluid intake monitoring system could achieve 94.42% accuracy, 90.17% sensitivity, and 40.11% mean absolute percentage error (MAPE) for drinking detection, gesture spotting and amount estimation, respectively. Particularly, MAPE of amount estimation is improved approximately 10% compared to the typical approaches. The results have demonstrated the feasibility and the effectiveness of the proposed fluid intake monitoring system. Full article

(This article belongs to the Special Issue Wearable Inertial Sensors)

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15 pages, 2646 KB

Open AccessArticle

Real-Time Hand Gesture Spotting and Recognition Using RGB-D Camera and 3D Convolutional Neural Network

by Dinh-Son Tran, Ngoc-Huynh Ho, Hyung-Jeong Yang, Eu-Tteum Baek, Soo-Hyung Kim and Gueesang Lee

Appl. Sci. 2020, 10(2), 722; https://doi.org/10.3390/app10020722 - 20 Jan 2020

Cited by 91 | Viewed by 16897

Abstract

Using hand gestures is a natural method of interaction between humans and computers. We use gestures to express meaning and thoughts in our everyday conversations. Gesture-based interfaces are used in many applications in a variety of fields, such as smartphones, televisions (TVs), video [...] Read more.

Using hand gestures is a natural method of interaction between humans and computers. We use gestures to express meaning and thoughts in our everyday conversations. Gesture-based interfaces are used in many applications in a variety of fields, such as smartphones, televisions (TVs), video gaming, and so on. With advancements in technology, hand gesture recognition is becoming an increasingly promising and attractive technique in human–computer interaction. In this paper, we propose a novel method for fingertip detection and hand gesture recognition in real-time using an RGB-D camera and a 3D convolution neural network (3DCNN). This system can accurately and robustly extract fingertip locations and recognize gestures in real-time. We demonstrate the accurateness and robustness of the interface by evaluating hand gesture recognition across a variety of gestures. In addition, we develop a tool to manipulate computer programs to show the possibility of using hand gesture recognition. The experimental results showed that our system has a high level of accuracy of hand gesture recognition. This is thus considered to be a good approach to a gesture-based interface for human–computer interaction by hand in the future. Full article

(This article belongs to the Special Issue Big Data Analysis and Visualization)

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18 pages, 4615 KB

Open AccessArticle

Finger Gesture Spotting from Long Sequences Based on Multi-Stream Recurrent Neural Networks

by Gibran Benitez-Garcia, Muhammad Haris, Yoshiyuki Tsuda and Norimichi Ukita

Sensors 2020, 20(2), 528; https://doi.org/10.3390/s20020528 - 18 Jan 2020

Cited by 10 | Viewed by 4437

Abstract

Gesture spotting is an essential task for recognizing finger gestures used to control in-car touchless interfaces. Automated methods to achieve this task require to detect video segments where gestures are observed, to discard natural behaviors of users’ hands that may look as target [...] Read more.

Gesture spotting is an essential task for recognizing finger gestures used to control in-car touchless interfaces. Automated methods to achieve this task require to detect video segments where gestures are observed, to discard natural behaviors of users’ hands that may look as target gestures, and be able to work online. In this paper, we address these challenges with a recurrent neural architecture for online finger gesture spotting. We propose a multi-stream network merging hand and hand-location features, which help to discriminate target gestures from natural movements of the hand, since these may not happen in the same 3D spatial location. Our multi-stream recurrent neural network (RNN) recurrently learns semantic information, allowing to spot gestures online in long untrimmed video sequences. In order to validate our method, we collect a finger gesture dataset in an in-vehicle scenario of an autonomous car. 226 videos with more than 2100 continuous instances were captured with a depth sensor. On this dataset, our gesture spotting approach outperforms state-of-the-art methods with an improvement of about 10% and 15% of recall and precision, respectively. Furthermore, we demonstrated that by combining with an existing gesture classifier (a 3D Convolutional Neural Network), our proposal achieves better performance than previous hand gesture recognition methods. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation)

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21 pages, 9296 KB

Open AccessArticle

Continuous Finger Gesture Recognition Based on Flex Sensors

by Wei-Chieh Chuang, Wen-Jyi Hwang, Tsung-Ming Tai, De-Rong Huang and Yun-Jie Jhang

Sensors 2019, 19(18), 3986; https://doi.org/10.3390/s19183986 - 15 Sep 2019

Cited by 53 | Viewed by 11562

Abstract

The goal of this work is to present a novel continuous finger gesture recognition system based on flex sensors. The system is able to carry out accurate recognition of a sequence of gestures. Wireless smart gloves equipped with flex sensors were implemented for [...] Read more.

The goal of this work is to present a novel continuous finger gesture recognition system based on flex sensors. The system is able to carry out accurate recognition of a sequence of gestures. Wireless smart gloves equipped with flex sensors were implemented for the collection of the training and testing sets. Given the sensory data acquired from the smart gloves, the gated recurrent unit (GRU) algorithm was then adopted for gesture spotting. During the training process for the GRU, the movements associated with different fingers and the transitions between two successive gestures were taken into consideration. On the basis of the gesture spotting results, the maximum a posteriori (MAP) estimation was carried out for the final gesture classification. Because of the effectiveness of the proposed spotting scheme, accurate gesture recognition was achieved even for complicated transitions between successive gestures. From the experimental results, it can be observed that the proposed system is an effective alternative for robust recognition of a sequence of finger gestures. Full article

(This article belongs to the Section Internet of Things)

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19 pages, 3976 KB

Open AccessArticle

Significant Change Spotting for Periodic Human Motion Segmentation of Cleaning Tasks Using Wearable Sensors

by Kai-Chun Liu and Chia-Tai Chan

Sensors 2017, 17(1), 187; https://doi.org/10.3390/s17010187 - 19 Jan 2017

Cited by 10 | Viewed by 5724

Abstract

The proportion of the aging population is rapidly increasing around the world, which will cause stress on society and healthcare systems. In recent years, advances in technology have created new opportunities for automatic activities of daily living (ADL) monitoring to improve the quality [...] Read more.

The proportion of the aging population is rapidly increasing around the world, which will cause stress on society and healthcare systems. In recent years, advances in technology have created new opportunities for automatic activities of daily living (ADL) monitoring to improve the quality of life and provide adequate medical service for the elderly. Such automatic ADL monitoring requires reliable ADL information on a fine-grained level, especially for the status of interaction between body gestures and the environment in the real-world. In this work, we propose a significant change spotting mechanism for periodic human motion segmentation during cleaning task performance. A novel approach is proposed based on the search for a significant change of gestures, which can manage critical technical issues in activity recognition, such as continuous data segmentation, individual variance, and category ambiguity. Three typical machine learning classification algorithms are utilized for the identification of the significant change candidate, including a Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Naive Bayesian (NB) algorithm. Overall, the proposed approach achieves 96.41% in the F1-score by using the SVM classifier. The results show that the proposed approach can fulfill the requirement of fine-grained human motion segmentation for automatic ADL monitoring. Full article

(This article belongs to the Special Issue Sensors for Ambient Assisted Living, Ubiquitous and Mobile Health)

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22 pages, 4634 KB

Open AccessArticle

Head Pose Estimation on Top of Haar-Like Face Detection: A Study Using the Kinect Sensor

by Anwar Saeed, Ayoub Al-Hamadi and Ahmed Ghoneim

Sensors 2015, 15(9), 20945-20966; https://doi.org/10.3390/s150920945 - 26 Aug 2015

Cited by 26 | Viewed by 16052

Abstract

Head pose estimation is a crucial initial task for human face analysis, which is employed in several computer vision systems, such as: facial expression recognition, head gesture recognition, yawn detection, etc. In this work, we propose a frame-based approach to estimate the head [...] Read more.

Head pose estimation is a crucial initial task for human face analysis, which is employed in several computer vision systems, such as: facial expression recognition, head gesture recognition, yawn detection, etc. In this work, we propose a frame-based approach to estimate the head pose on top of the Viola and Jones (VJ) Haar-like face detector. Several appearance and depth-based feature types are employed for the pose estimation, where comparisons between them in terms of accuracy and speed are presented. It is clearly shown through this work that using the depth data, we improve the accuracy of the head pose estimation. Additionally, we can spot positive detections, faces in profile views detected by the frontal model, that are wrongly cropped due to background disturbances. We introduce a new depth-based feature descriptor that provides competitive estimation results with a lower computation time. Evaluation on a benchmark Kinect database shows that the histogram of oriented gradients and the developed depth-based features are more distinctive for the head pose estimation, where they compare favorably to the current state-of-the-art approaches. Using a concatenation of the aforementioned feature types, we achieved a head pose estimation with average errors not exceeding 5:1; 4:6; 4:2 for pitch, yaw and roll angles, respectively. Full article

(This article belongs to the Section Physical Sensors)

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