Hand Gesture Recognition and Real-time Game Control Based on A Wearable Band with 6-axis Sensors

Li, Yande; Wang, Taiqian; Khan, Aamir; Li, Lian; Li, Caihong; Yang, Yi; Liu, Li

doi:10.1109/ijcnn.2018.8489743

Cited by 16 publications

(9 citation statements)

References 17 publications

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“…The main question regarding the further application of our system addresses the size of the gesture vocabulary and the number of samples in the dataset. Although our 10 gestures used in this study is a high number compared with recent research [5,10,18,29], and involving 5 participants to include gesture diversity, our experiments are evaluated only on a total of 500 sequences. We obtained high performance for the LSTM both in F1 score and accuracy, the latter comparable with our ESN framework.…”

Section: Discussionmentioning

confidence: 99%

“…In the context of HCI, wearables are also used in the gaming domain because the sensor input directly measured from the subject allows real-time processing. In this regard, Li et al [18] presented a system to control the Jump&Go Fly Bird game. Gyroscope and accelerometer data were collected from a wristband and gestures manually segmented using video information to mark the start and end phase of a gesture.…”

Section: Related Workmentioning

confidence: 99%

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Echo State Networks and Long Short-Term Memory for Continuous Gesture Recognition: a Comparative Study

et al. 2020

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Recent developments of sensors that allow tracking of human movements and gestures enable rapid progress of applications in domains like medical rehabilitation or robotic control. Especially the inertial measurement unit (IMU) is an excellent device for real-time scenarios as it rapidly delivers data input. Therefore, a computational model must be able to learn gesture sequences in a fast yet robust way. We recently introduced an echo state network (ESN) framework for continuous gesture recognition (Tietz et al., 2019) including novel approaches for gesture spotting, i.e., the automatic detection of the start and end phase of a gesture. Although our results showed good classification performance, we identified significant factors which also negatively impact the performance like subgestures and gesture variability. To address these issues, we include experiments with Long Short-Term Memory (LSTM) networks, which is a state-of-the-art model for sequence processing, to compare the obtained results with our framework and to evaluate their robustness regarding pitfalls in the recognition process. In this study, we analyze the two conceptually different approaches processing continuous, variable-length gesture sequences, which shows interesting results comparing the distinct gesture accomplishments. In addition, our results demonstrate that our ESN framework achieves comparably good performance as the LSTM network but has significantly lower training times. We conclude from the present work that ESNs are viable models for continuous gesture recognition delivering reasonable performance for applications requiring real-time performance as in robotic or rehabilitation tasks. From our discussion of this comparative study, we suggest prospective improvements on both the experimental and network architecture level.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Echo State Networks and Long Short-Term Memory for Continuous Gesture Recognition: a Comparative Study

et al. 2020

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“…En ambos casos, la comprensión del gesto es muy amplia y aplica para diferentes partes del cuerpo: estos faciales [33], con las manos [5], [34], [35], con los dedos [4], [5], [20], o de cuerpo entero [8], [36]. En algunos casos, la complejidad técnica de reconocer la secuencia de acciones que definen un gesto ha llevado a que se utilicen objetos que restrinjan la interacción [11], [18].…”

Section: El Gesto Como Medio De Interacciónunclassified

“…Algunas de las cuestiones que deben ser respondidas en estas etapas son: ¿qué ventana de tiempo es adecuada para preprocesar un conjunto de datos crudos?, ¿cómo se filtran los datos crudos?, ¿de qué manera se pueden comprimir los datos crudos para mantener la información más relevante, sin demandar demasiado espacio de memoria?, ¿se preprocesa el gesto luego de Para el aprendizaje de máquina y la etapa de clasificación o regresión, se han utilizado diferentes técnicas, entre las que destacan: Modelos Ocultos de Markov (HMM) [15], [33], [34], [47], Redes Neuronales [34], Freeman Chain Code [44], Máquinas de Vectores de Soporte [48]- [50], evolución neuronal [35] y aprendizaje profundo [51], [52].…”

Section: Reconocimiento De Gestosunclassified

Interacciones basadas en gestos: revisión crítica

Cortés-Rico¹,

Piedrahita-Solórzano²

2019

TecnoL.

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Este artículo presenta una revisión crítica de la interacción humano-computador (HCI) basada en gestos. El gesto, como una forma de comunicación no verbal, ha sido de interés para el área de HCI en la búsqueda de alternativas de interacción entre el humano y la máquina, a través del cuerpo como agente que percibe y actúa en el mundo. La revisión se hizo en las bases de datos de mayor importancia en HCI y en algunas fuentes de literatura académica latinoamericana en el área, e incluye un análisis de la evolución de las interacciones basadas en gestos, el trabajo actual y las perspectivas a futuro. El análisis se desarrolla de forma holística y abarca asuntos técnicos y humanos: psicológicos, sociales y culturales, así como su relación. Este proceso analítico se presenta como una descripción cienciométrica de los resultados de las búsquedas, a fin de exponer el gesto como medio de interacción, las técnicas utilizadas para los diferentes pasos en el proceso de reconocimiento de gestos y las aplicaciones y desafíos de las interacciones basadas en gestos. Como conclusión se formula una serie de preguntas que invitan al lector a pensar en potenciales focos de investigación en las interacciones basadas en gestos.

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“…Wearable devices are trending topics in different areas such as healthcare and activity recognition [1,2,3,4,5,6,7], sports [8,9], education [10,11], industry [12,13], human–computer interaction [14] and other areas. The rise of new concepts like the Internet of Things (IoT) [15,16,17] and Industry 4.0 [18] along with hardware miniaturization allow for the development of novel devices and solutions.…”

Section: Introductionmentioning

confidence: 99%

Field Research Cooperative Wearable Systems: Challenges in Requirements, Design and Validation

Silva

Amorim

Ribeiro

et al. 2019

Sensors

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The widespread availability of wearable devices is evolving them into cooperative systems. Communication and distribution aspects such as the Internet of Things, Wireless Body Area Networks, and Local Wireless Networks provide the means to develop multi-device platforms. Nevertheless, the field research environment presents a specific feature set, which increases the difficulty in the adoption of this technology. In this text, we review the main aspects of Field Research Gears and Wearable Devices. This review is made aiming to understand how to create cooperative systems based on wearable devices directed to the Field Research Context. For a better understanding, we developed a case study in which we propose a cooperative system architecture and provide validation aspects. For this matter, we provide an overview of a previous device architecture and study an integration proposal.

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Hand Gesture Recognition and Real-time Game Control Based on A Wearable Band with 6-axis Sensors

Cited by 16 publications

References 17 publications

Echo State Networks and Long Short-Term Memory for Continuous Gesture Recognition: a Comparative Study

Echo State Networks and Long Short-Term Memory for Continuous Gesture Recognition: a Comparative Study

Interacciones basadas en gestos: revisión crítica

Field Research Cooperative Wearable Systems: Challenges in Requirements, Design and Validation

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