Gyroscope-Based Continuous Human Hand Gesture Recognition for Multi-Modal Wearable Input Device for Human Machine Interaction

Abstract

Human hand gestures are a widely accepted form of real-time input for devices providing a human-machine interface. However, hand gestures have limitations in terms of effectively conveying the complexity and diversity of human intentions. This study attempted to address these limitations by proposing a multi-modal input device, based on the observation that each application program requires different user intentions (and demanding functions) and the machine already acknowledges the running application. When the running application changes, the same gesture now offers a new function required in the new application, and thus, we can greatly reduce the number and complexity of required hand gestures. As a simple wearable sensor, we employ one miniature wireless three-axis gyroscope, the data of which are processed by correlation analysis with normalized covariance for continuous gesture recognition. Recognition accuracy is improved by considering both gesture patterns and signal strength and by incorporating a learning mode. In our system, six unit hand gestures successfully provide most functions offered by multiple input devices. The characteristics of our approach are automatically adjusted by acknowledging the application programs or learning user preferences. In three application programs, the approach shows good accuracy (90–96%), which is very promising in terms of designing a unified solution. Furthermore, the accuracy reaches 100% as the users become more familiar with the system.

Echo State Networks and Long Short-Term Memory for Continuous Gesture Recognition: a Comparative Study

“…We believe that our experiments show a new research direction in a domain that is still dominated by classic computational techniques like DTW. Another important factor affecting the performance are subgestures and symmetric gestures [11]. In our experiments, the individual gestures snap left and snap right as well as the snap forward and snap backward gestures were often falsely classified with their corresponding shake gestures in both networks.…”

“…Recent approaches primarily use simple techniques like sliding windows and DTW or an SVM. However, all the techniques need special tuning of the window size or the specific kernel [27], which led to an additional subject customization step in the system [11]. In our study, we show how ESNs learn different activity patterns while being able to distinguish between gestures, subtle movements, and pauses.…”

“…Similarly, Han and Yoon [11] proposed a system for the recognition of 6 gestures obtained from a wireless triaxial gyroscope worn by 5 subjects. The gyro data was preprocessed using a sliding window and the normalized covariance between the sequences calculated.…”

IOT Based Gesture Recognition for Smart Controlling

“…Recognition means accuracy extended up to 94.44% [20]. Gyroscope based continuous hand gesture recognition for wearable device for HMI proposed by Hobeom Han [21]. SVM threshold technique was used for hand recognition.…”

A Gesture Air-Writing Tracking Method that Uses 24 GHz SIMO Radar SoC

“…However, vision sensors often have the following problems: susceptibility to light, hidden privacy risks, and inability to penetrate media, such as plastic and foam. Other choices, such as accelerometers and gyroscopes [6]- [8], are unpractical for wearable gesture recognition devices.…”