Sensors for Gesture Recognition Systems

Berman, Sigal; Stern, Helman I.

doi:10.1109/tsmcc.2011.2161077

Cited by 92 publications

(43 citation statements)

References 63 publications

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“…Berman and Stern [10] describes sensors as equipment or group of gadgets which can be used as detectors for grasping gesture signals. Two of the most important platform characteristics of sensors are to check if the user needs to be a large number of user attached sensors or to be unencumbered and also does the system needs single sensor or numerous sensors.…”

Section: Sensorsmentioning

confidence: 99%

Study of Hand Gesture Recognition and Classification

Srinivas

Rajagopal

2017

Asian J Pharm Clin Res

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To recognize different hand gestures and achieve efficient classification to understand static and dynamic hand movements used for communications. Static and dynamic hand movements are first captured using gesture recognition devices including Kinect device, hand movement sensors, connecting electrodes, and accelerometers. These gestures are processed using hand gesture recognition algorithms such as multivariate fuzzy decision tree, hidden Markov models (HMM), dynamic time warping framework, latent regression forest, support vector machine, and surface electromyogram. Hand movements made by both single and double hands are captured by gesture capture devices with proper illumination conditions. These captured gestures are processed for occlusions and fingers close interactions for identification of right gesture and to classify the gesture and ignore the intermittent gestures. Real-time hand gestures recognition needs robust algorithms like HMM to detect only the intended gesture. Classified gestures are then compared for the effectiveness with training and tested standard datasets like sign language alphabets and KTH datasets. Hand gesture recognition plays a very important role in some of the applications such as sign language recognition, robotics, television control, rehabilitation, and music orchestration.

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Section: Sensorsmentioning

confidence: 99%

Study of Hand Gesture Recognition and Classification

Srinivas

Rajagopal

2017

Asian J Pharm Clin Res

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“…Wearable computing approaches [1] to sign language data acquisition offer accurate means of extracting information about the signers hand movements and hand shape. Each sensing technology varies along several dimensions, including accuracy, resolution, range of motion, user comfort, and cost .…”

Section: Wearable Computing Based Acquisitionmentioning

confidence: 99%

Review of Literature for the Development of Indian Sign Language Recognition System

Dour¹,

Sharma²

2015

IJCA

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“…Hands could be visually detected or tracked by a range of sensors [29], such as ego-centric/self-mounted cameras [30], stereo camera [31], or Kinect [32]. In this paper, we focus on hand detection and tracking solely based on a single, static camera feed, which is a non-invasive and low-cost solution, leading to a convenient, computer-vision-based system.…”

Section: Introductionmentioning

confidence: 99%

HD: Efficient Hand Detection and Tracking

Rouge

Shaikh

Olszewska

2016

Annals of Computer Science and Information Systems

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Abstract-Automated hand detection is useful for applications requiring reliable hand posture and hand gesture processing. Such applications include human-computer/human-robot interfaces for rehabilitation, serious games, or non-invasive medical diagnosis. Hence, in this paper, we focus on the design and development of a robust and fast hand detection and tracking (HD) system. The design of our HD system involved the study of the human skin colour and people's foreground properties, in order to merge these information for an efficient hand detection and tracking. Experiments have been carried out in real-world environment and have demonstrated the excellent performance of our HD system.

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Sensors for Gesture Recognition Systems

Cited by 92 publications

References 63 publications

Study of Hand Gesture Recognition and Classification

Study of Hand Gesture Recognition and Classification

Review of Literature for the Development of Indian Sign Language Recognition System

HD: Efficient Hand Detection and Tracking

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