Hands are vital in a wide range of fundamental daily activities, and neurological diseases that impede hand function can significantly affect quality of life. Wearable hand gesture interfaces hold promise to restore and assist hand function and to enhance human-human and human-computer communication. The purpose of this review is to synthesize current novel sensing interfaces and algorithms for hand gesture recognition, and the scope of applications covers rehabilitation, prosthesis control, exoskeletons for augmentation, sign language recognition, human-computer interaction, and user authentication. Results showed that electrical, mechanical, acoustical/vibratory, and optical sensing were the primary input modalities in gesture recognition interfaces. Two categories of algorithms were identified: 1) classification algorithms for predefined, fixed hand poses and 2) regression algorithms for continuous finger and wrist joint angles. Conventional machine learning algorithms, including linear discriminant analysis, support vector machines, random forests, and non-negative matrix factorization, have been widely used for a variety of gesture recognition applications, and deep learning algorithms have more recently been applied to further facilitate the complex relationship between sensor signals and multi-articulated hand postures. Future research should focus on increasing recognition accuracy with larger hand gesture datasets, improving reliability and robustness for daily use outside of the laboratory, and developing softer, less obtrusive interfaces.

While most wearable gesture recognition approaches focus on the forearm or fingers, the wrist may a more suitable location for practical use. We present the design and validation of a real-time gesture recognition wristband based on surface electromyography (sEMG) and inertial measurement unit (IMU) sensing fusion, which can recognize 8 air gestures and 4 surface gestures with 2 distinct force levels. Ten healthy subjects performed an initial gesture recognition experiment, followed by a second experiment 1 hour later and a third experiment 1 day later. Classification accuracies for the initial experiment were 92.6% and 88.8% for air and surface gestures, respectively, and there were no changes in accuracy results during testing 1 hour and 1 day later (p>0.05). These results demonstrate the feasibility of wrist-based gesture recognition paving the way for potential future integration in to a smart watch or other wrist-worn wearable for intuitive human computer interaction.

This paper presents a new approach to wearable hand gesture recognition and finger angle estimation based on modified barometric pressure sensing. Barometric pressure sensors were encased and injected with VytaFlex rubber such that the rubber directly contacted the sensing element allowing pressure change detection when the encasing rubber was pressed. A wearable prototype consisting of an array of 10 modified barometric pressure sensors around the wrist was developed and validated with experimental testing for three different hand gesture sets and finger flexion/extension trials for each of the five fingers. Overall hand gesture recognition classification accuracy was 94%. Further analysis revealed that the most important sensor location was the underside of the wrist and that when reducing the sensor number to only 5 optimally placed sensors, classification accuracy was still 90%. For continuous finger angle estimation, aggregate R2 values between actual and predicted angles were thumb: 0.81±0.10, index finger: 0.85±0.06, middle finger: 0.77±0.08, ring finger: 0.77±0.12, and pinkie finger: 0.75±0.10, and the overall average was 0.79±0.05. These results demonstrate that a modified barometric pressure wristband can be used to classify hand gestures and to estimate individual finger joint angles. This approach could serve to improve clinical treatment for upper extremity deficiencies, such as for stroke rehabilitation, by providing objective patient motor control metrics to inform and aid physicians and therapists throughout the rehabilitation process.

Gesture recognition is important for human computer interaction and a variety of emerging research and commercial areas including virtual and augmented reality. Current approaches typically require sensors to be placed on the forearm, wrist or directly across finger joints, however, they can be cumbersome or hinder human movement and sensation. We introduce a novel approach to recognize hand gestures by estimating skin strain with multiple soft sensors optimally placed across the back of the hand. A pilot study was first conducted by covering the back of the hand with 40 small 2.5mm reflective markers and using a high precision camera system to measure skin strain patterns for individual finger movements. Optimal strain locations were then determined and used for sensor placement in a stretchable e-skin patch prototype. Experimental testing was performed to evaluate the stretchable e-skin patch performance in classifying individual finger gestures and American Sign Language 0-9 number gestures. Results showed classification accuracies of 95.3% and 94.4% for finger gestures and American Sign Language 0-9 gestures, respectively. These results demonstrate the feasibility of a stretchable e-skin patch on the back of the hand for hand gesture recognition and hold potential to significantly enhance human computer interaction.

Walking, one of the most common daily activities, causes unwanted movement artifacts which can significantly deteriorate hand gesture recognition accuracy. However, traditional hand gesture recognition algorithms are typically developed and validated with wrist-worn devices only during static human poses, neglecting the critical importance of dynamic effects on gesture accuracy. Thus, we developed and validated a signal decomposition approach via empirical mode decomposition to accurately segment target gestures from coupled raw signals during dynamic walking and a transfer learning method based on distribution adaptation to enable gesture recognition through domain transfer between dynamic walking and static standing scenarios. Ten healthy subjects performed seven hand gestures during both walking and standing experiments while wearing an IMU wrist-worn device. Experimental results showed that the signal decomposition approach reduced the gesture detection error by 83.8%, and the transfer learning approach (20% transfer rate) improved hand gesture recognition accuracy by 15.1%. This ground-breaking work demonstrates the feasibility of hand gesture recognition while walking via wrist-worn sensing. These findings serve to inform real-life and ubiquitous adoption of wrist-worn hand gesture recognition for intuitive human-machine interaction in dynamic walking situations.