A Gesture Interaction System Based on Improved Finger Feature and WE-KNN

Li, Feifei; Li, Yujun; Du, Baozhen; Xu, Hongke; Huagang, Xiong; Chen, Min

doi:10.1145/3325730.3325759

Cited by 9 publications

(2 citation statements)

References 5 publications

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“…The SVM is well-known to be a strong classifier for hand gestures [ 23 , 44 , 71 , 72 , 73 , 74 , 75 , 76 ]. It is a robust algorithm for high-dimensional datasets with smaller numbers of sampling points.…”

Section: Methodsmentioning

confidence: 99%

Classification of Activities of Daily Living Based on Grasp Dynamics Obtained from a Leap Motion Controller

Sharif

Ahmadreza

Chembrammel

et al. 2022

Sensors

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Stroke is one of the leading causes of mortality and disability worldwide. Several evaluation methods have been used to assess the effects of stroke on the performance of activities of daily living (ADL). However, these methods are qualitative. A first step toward developing a quantitative evaluation method is to classify different ADL tasks based on the hand grasp. In this paper, a dataset is presented that includes data collected by a leap motion controller on the hand grasps of healthy adults performing eight common ADL tasks. Then, a set of features with time and frequency domains is combined with two well-known classifiers, i.e., the support vector machine and convolutional neural network, to classify the tasks, and a classification accuracy of over 99% is achieved.

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Section: Methodsmentioning

confidence: 99%

Classification of Activities of Daily Living Based on Grasp Dynamics Obtained from a Leap Motion Controller

Sharif

Ahmadreza

Chembrammel

et al. 2022

Sensors

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show abstract

“…Most studies [12]- [18] have reported their perfor-mance in terms of high recognition accuracy rates, making dynamic [16], [17], real-time [19]- [21] interaction feasible. High-frequency, wide-bandwidth radars detect even the finest movements [22], such as the fingers of a hand [23], at a reasonable distance [24]. However, the wide variety of radars used [25] and the particularity of their models, methods, and tools used to recognize gestures limit reuse in another context [26].…”

Section: Introductionmentioning

confidence: 99%

Analysis of User-Defined Radar-Based Hand Gestures Sensed Through Multiple Materials

Sluÿters,

Lambot,

Vanderdonckt

et al. 2024

IEEE Access

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Radar sensing can penetrate non-conducting materials, such as glass, wood, and plastic, which makes it appropriate for recognizing gestures in environments with poor visibility, limited accessibility, and privacy sensitivityWhile the performance of radar-based gesture recognition in these environments has been extensively researched, the preferences that users express for these gestures are less known. To analyze such gestures simultaneously according to their user preference and their system recognition performance, we conducted three gesture elicitation studies each with n 1 =30 participants to identify user-defined, radar-based gestures sensed through three distinct materials: the glass of a shop window, the wood of an office door, and polyvinyl chloride in an emergency. On this basis, we created a new dataset of nine selected gesture classes for n 2 =20 participants repeating twice the same gesture captured by radar through three materials, i.e., glass, wood, and polyvinyl chloride. To uniformly compare recognition rates in these conditions with sensing variations, a specifically tailored procedure was defined and conducted with one-shot radar calibration to train and evaluate a gesture recognizer. 'Wood' achieved the best recognition rate (96.44%), followed by 'Polyvinyl chloride' and 'Glass'. We perform a preference-performance analysis of the gestures by combining the agreement rate from the elicitation studies and the recognition rate from the evaluation.

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Real-Time Hand Gesture Recognition Using KNN-DTW and Leap Motion Controller

Nogales

Benalcázar

2020

Communications in Computer and Information Science

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A Gesture Interaction System Based on Improved Finger Feature and WE-KNN

Cited by 9 publications

References 5 publications

Classification of Activities of Daily Living Based on Grasp Dynamics Obtained from a Leap Motion Controller

Classification of Activities of Daily Living Based on Grasp Dynamics Obtained from a Leap Motion Controller

Analysis of User-Defined Radar-Based Hand Gestures Sensed Through Multiple Materials

Real-Time Hand Gesture Recognition Using KNN-DTW and Leap Motion Controller

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