Online, interactive learning of gestures for human/robot interfaces

Lee, C.; Xu, Yangsheng

doi:10.1109/robot.1996.509165

Cited by 132 publications

(61 citation statements)

References 6 publications

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“…Only a few, like [2] and [3], actually analyze the type of grasp used. In fact, most of the work on hand-shape recognition is done for communicative gestures, such as pointing motions, symbols, or sign languages [4]- [9], and cannot be applied directly to manipulative gestures or grasps.…”

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confidence: 99%

A sensor fusion approach for recognizing continuous human grasping sequences using hidden Markov models

Bernardin¹,

et al. 2005

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Abstract-TheProgramming by Demonstration (PbD) technique aims at teaching a robot to accomplish a task by learning from a human demonstration. In a manipulation context, recognizing the demonstrator's hand gestures, specifically when and how objects are grasped, plays a significant role. Here, a system is presented that uses both hand shape and contact-point information obtained from a data glove and tactile sensors to recognize continuous human-grasp sequences. The sensor fusion, grasp classification, and task segmentation are made by a hidden Markov model recognizer. Twelve different grasp types from a general, task-independent taxonomy are recognized. An accuracy of up to 95% could be achieved for a multiple-user system. Index Terms-Hidden Markov models (HMMs), Programming by Demonstration (PbD), sensor fusion, user interfaces.

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confidence: 99%

A sensor fusion approach for recognizing continuous human grasping sequences using hidden Markov models

Bernardin¹,

et al. 2005

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“…Recognition was achieved by fusing classifier outputs. Lee and Yangsheng [20] used acceleration thresholds in combination with HMMs. In previous works of the authors on intake gesture recognition, HMMs were used together with an explicit data-adaptive segmentation [21].…”

Section: Movement Recognitionmentioning

confidence: 99%

Recognition of dietary activity events using on-body sensors

Amft

Tröster

2008

Artificial Intelligence in Medicine

195

148

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SummaryObjective: An imbalanced diet elevates health risks for many chronic diseases including obesity. Dietary monitoring could contribute vital information to lifestyle coaching and diet management, however, current monitoring solutions are not feasible for a long-term implementation. Towards automatic dietary monitoring, this work targets the continuous recognition of dietary activities using on-body sensors. Methods: An on-body sensing approach was chosen, based on three core activities during intake: arm movements, chewing and swallowing. In three independent evaluation studies the continuous recognition of activity events was investigated and the precision-recall performance analysed. An event recognition procedure was deployed, that addresses multiple challenges of continuous activity recognition, including the dynamic adaptability for variable-length activities and flexible deployment by supporting one to many independent classes. The approach uses a sensitive activity event search followed by a selective refinement of the detection using different information fusion schemes. The method is simple and modular in design and implementation. Results: The recognition procedure was successfully adapted to the investigated dietary activities. Four intake gesture categories from arm movements and two food groups from chewing cycle sounds were detected and identified with a recall of 80-90% and a precision of 50-64%. The detection of individual swallows resulted in 68% recall and 20% precision. Sample-accurate recognition rates were 79% for movements, 86% for chewing and 70% for swallowing. Conclusions: Body movements and chewing sounds can be accurately identified using on-body sensors, demonstrating the feasibility of on-body dietary monitoring. Further investigations are needed to improve the swallowing spotting performance. #

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“…Neural network approaches or statistical template-matching approaches are commonly used to identify static hand posses [Fels, 1993]. Time dependent neural network and Hidden Markov Model (HMM) are commonly used for dynamic gesture recognition [Lee, 1996]. In this case gestures are typically recognized using pre-trained templates, however gloves can also be used to identify natural or untrained gestures.…”

Section: Glove-based Approachesmentioning

confidence: 99%

Face and Gesture Recognition for Human-Robot Interaction

Ueno¹

2007

Face Recognition

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Online, interactive learning of gestures for human/robot interfaces

Cited by 132 publications

References 6 publications

A sensor fusion approach for recognizing continuous human grasping sequences using hidden Markov models

A sensor fusion approach for recognizing continuous human grasping sequences using hidden Markov models

Recognition of dietary activity events using on-body sensors

Face and Gesture Recognition for Human-Robot Interaction

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