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A Comprehensive Review of Leap Motion Controller-based Hand Gesture Datasets

Bharatesh Chakravarthi, Prabhu Prasad B M, Pavan Kumar B N

TL;DR

The paper addresses the need for a consolidated resource on Leap Motion Controller–based hand gesture datasets. It combines a technical overview of the LMC hardware with a survey of diverse, modality-varied datasets and an experimental evaluation of LMC 2 for gesture data generation in touchless and VR/AR applications. Key contributions include a catalog of datasets across domains, domain-specific gesture coverage, and practical insights into data modalities and collection setups, supported by real-time gesture visualization experiments. This work provides a roadmap for researchers to select appropriate datasets and informs future data collection and application development in HCI, AR/VR, robotics, and healthcare.

Abstract

This paper comprehensively reviews hand gesture datasets based on Ultraleap's leap motion controller, a popular device for capturing and tracking hand gestures in real-time. The aim is to offer researchers and practitioners a valuable resource for developing and evaluating gesture recognition algorithms. The review compares various datasets found in the literature, considering factors such as target domain, dataset size, gesture diversity, subject numbers, and data modality. The strengths and limitations of each dataset are discussed, along with the applications and research areas in which they have been utilized. An experimental evaluation of the leap motion controller 2 device is conducted to assess its capabilities in generating gesture data for various applications, specifically focusing on touchless interactive systems and virtual reality. This review serves as a roadmap for researchers, aiding them in selecting appropriate datasets for their specific gesture recognition tasks and advancing the field of hand gesture recognition using leap motion controller technology.

A Comprehensive Review of Leap Motion Controller-based Hand Gesture Datasets

TL;DR

The paper addresses the need for a consolidated resource on Leap Motion Controller–based hand gesture datasets. It combines a technical overview of the LMC hardware with a survey of diverse, modality-varied datasets and an experimental evaluation of LMC 2 for gesture data generation in touchless and VR/AR applications. Key contributions include a catalog of datasets across domains, domain-specific gesture coverage, and practical insights into data modalities and collection setups, supported by real-time gesture visualization experiments. This work provides a roadmap for researchers to select appropriate datasets and informs future data collection and application development in HCI, AR/VR, robotics, and healthcare.

Abstract

This paper comprehensively reviews hand gesture datasets based on Ultraleap's leap motion controller, a popular device for capturing and tracking hand gestures in real-time. The aim is to offer researchers and practitioners a valuable resource for developing and evaluating gesture recognition algorithms. The review compares various datasets found in the literature, considering factors such as target domain, dataset size, gesture diversity, subject numbers, and data modality. The strengths and limitations of each dataset are discussed, along with the applications and research areas in which they have been utilized. An experimental evaluation of the leap motion controller 2 device is conducted to assess its capabilities in generating gesture data for various applications, specifically focusing on touchless interactive systems and virtual reality. This review serves as a roadmap for researchers, aiding them in selecting appropriate datasets for their specific gesture recognition tasks and advancing the field of hand gesture recognition using leap motion controller technology.
Paper Structure (6 sections, 5 figures, 3 tables)

This paper contains 6 sections, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Leap Motion Controller: Technical Overview
  3. Hand Gesture Datasets
  4. Experimental Evaluation
  5. Experimental Setup
  6. Gesture Visualization

Figures (5)

  • Figure 1: Growing trend in the adoption of leap motion controller for hand gesture recognition across various application domains.
  • Figure 2: Ultraleap's Leap Motion Sensors: (a) Leap Motion Controller 2, (b) Ultraleap 3Di, and (c) Stereo IR 170
  • Figure 3: The Leap Motion Controller 2 is being utilized in our experimental study, featuring both Screentop and Desktop mode. (highlighted by a red box) (a) depicts a single-hand gesture demonstration, and (b) a two-hand gesture.
  • Figure 4: Ultraleap Control Panel User Interface. (a) display of single hand gesture and (b) display of both hand gestures, showcasing the stereo images of the respective poses of the hands gestures captured from the Leap Motion Controller 2.
  • Figure 5: Gestures in Various Domains: Each snapshots represents (In rows from Gesture 1 (G1) to Gesture 24 (G24)) gestures performed by participants across different domains, as listed in Table \ref{['table03']}, illustrating the diverse range of gestures explored in the study.