A Review of Hand Gesture Recognition Systems Based on Noninvasive Wearable Sensors

Abstract

Hand gesture, one of the essential ways for a human to convey information and express intuitive intention, has a significant degree of differentiation, substantial flexibility, and high robustness of information transmission to make hand gesture recognition (HGR) one of the research hotspots in the fields of human–human and human–computer or human–machine interactions. Noninvasive, on‐body sensors can monitor, track, and recognize hand gestures for various applications such as sign language recognition, rehabilitation, myoelectric control for prosthetic hands and human–machine interface (HMI), and many other applications. This article systematically reviews recent achievements from noninvasive upper‐limb sensing techniques for HGR, multimodal sensing fusion to gain additional user information, and wearable gesture recognition algorithms to obtain more reliable and robust performance. Research challenges, progress, and emerging opportunities for sensor‐based HGR systems are also analyzed to provide perspectives for future research and progress.

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

“…Regarding GES1, the agreement rates are overall moderate in magnitude (M =0.24, SD=0.157), ranging between 0.074 (low) for ''Turn assistant help off'' and 0.536 (very high) for ''Answer help phone Call''. On the global sampling, 5 19 =26% of the rates belong to the low consensus category, 9 19 =47% of the rates belong to the moderate range, 3 19 =16% are high, and 2 19 =10% are very high. Apart from a few exceptions, most gestures received an agreement rate slightly higher or close to those reported in the GES literature ( [76] that summarized agreement rates of 18 GESs).…”

“…• Number of sampling points (N ): represents the number of points per gesture template: N ={x ∈ N|4≤x≤40}. • Antenna pairs (AP): defines the set of antenna pairs from which the data were used: AP={ (1,2,3,6,8,9), (4,5,7,10,11,12), (1,2,3,4,5,6,7,8,9,10,11,12)}. These three sets demonstrated admissible results [26].…”

“…R ADIO Detection And Ranging (Radar) technologies detect objects and movements via electromagnetic or radio waves [1] that can penetrate non-conducting materials, such as glass, wood, plastic, paper, cardboard, and rubber [2], [3]. This capability makes radars appropriate for sensing human gestures in conditions usually not covered by other sensing technologies such as computer vision: environments with poor visibility (e.g., dark places, conditions with fog, rain, dust, smoke, or adverse light) [4], [5], limited accessibility (e.g., hidden or indirect locations, places behind or below rough surfaces) [6], [7], and privacy-sensitivity (e.g., when the user does not want to be filmed or recognized) [8], [9].…”

A Framework for Real-Time Gestural Recognition and Augmented Reality for Industrial Applications

“…Gesture recognition uses a set of techniques that allow computers to recognize human gestures [ 16 , 17 , 18 ]. It has many potential applications, such as device control [ 19 , 20 ], interaction with virtual environments [ 21 ], and the real-time translation of sign language [ 22 , 23 , 24 ].…”

Recent Advances in Tactile Sensory Systems: Mechanisms, Fabrication, and Applications

“…Wearable tactile devices can provide a more natural and realistic touch sensation for the wearer and are used to improve immersion in virtual reality/augmented reality (VR/AR) systems [ 146 , 147 , 148 , 149 , 150 ]. The glove is the bridge connecting virtuality and reality, and is used to send real-time tactile-feedback information.…”