Hand gesture recognition based bed position control for disabled patients

Lamb, Kalpana; Madhe, Swati

doi:10.1109/casp.2016.7746159

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…Vision-based gestures include mainly hand gestures. Lamb et al [ 37 ] controlled the position of a bed automatically by using hand gesture recognition based on image processing algorithms [ 37 ], and Ransalu et al used it to control smart appliances [ 38 ]. Regarding 3D-camera-based recognition, Handosa et al used the Kinect sensor to control lights [ 39 ].…”

Section: State-of-the-artmentioning

confidence: 99%

Smart Assistive Architecture for the Integration of IoT Devices, Robotic Systems, and Multimodal Interfaces in Healthcare Environments

Brunete

Gambao

Hernando

et al. 2021

Sensors

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This paper presents a new architecture that integrates Internet of Things (IoT) devices, service robots, and users in a smart assistive environment. A new intuitive and multimodal interaction system supporting people with disabilities and bedbound patients is presented. This interaction system allows the user to control service robots and devices inside the room in five different ways: touch control, eye control, gesture control, voice control, and augmented reality control. The interaction system is comprised of an assistive robotic arm holding a tablet PC. The robotic arm can place the tablet PC in front of the user. A demonstration of the developed technology, a prototype of a smart room equipped with home automation devices, and the robotic assistive arm are presented. The results obtained from the use of the various interfaces and technologies are presented in the article. The results include user preference with regard to eye-base control (performing clicks, and using winks or gaze) and the use of mobile phones over augmented reality glasses, among others.

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Section: State-of-the-artmentioning

confidence: 99%

Smart Assistive Architecture for the Integration of IoT Devices, Robotic Systems, and Multimodal Interfaces in Healthcare Environments

Brunete

Gambao

Hernando

et al. 2021

Sensors

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“…They used the input of optical tracking device to control dual-arm robot remotely. Lamb K. and Madhe S. [15] added right and left movements to the existing electronic hospital-bed system, in which the position of the bed is controlled automatically by using hand gesture recognition. The features of the segment image are extracted by using wavelet decomposition, while the gestures are classified by using Euclidean Distance.…”

mentioning

confidence: 99%

Control of Robot Directions Based on Online Hand Gestures

Tawfeeq¹,

Abbass²

2018

IJEEE

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The evolution of wireless communication technology increases human machine interaction capabilities especially in controlling robotic systems. This paper introduces an effective wireless system in controlling the directions of a wheeled robot based on online hand gestures. The hand gesture images are captured and processed to be recognized and classified using neural network (NN). The NN is trained using extracted features to distinguish five different gestures; accordingly it produces five different signals. These signals are transmitted to control the directions of the cited robot. The main contribution of this paper is, the technique used to recognize hand gestures is required only two features, these features can be extracted in very short time using quite easy methodology, and this makes the proposed technique so suitable for online interaction. In this methodology, the preprocessed image is partitioned column-wise into two half segments; from each half one feature is extracted. This feature represents the ratio of white to black pixels of the segment histogram. The NN showed very high accuracy in recognizing all of the proposed gesture classes. The NN output signals are transmitted to the robot microcontroller wirelessly using Bluetooth. Accordingly the microcontroller guides the robot to the desired direction. The overall system showed high performance in controlling the robot movement directions.

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