Hand shape classification in various pronation angles using a wearable wrist contour sensor

Abstract

Hand gestures are potentially useful for communications between humans and between a human and a machine. However, existing methods entail several problems for practical use. We have proposed an approach to hand shape recognition based on wrist contour measurement. Especially in this paper, two assignments are addressed. First is the development of a new sensing device in which all elements are installed in a wrist-watch-type device. Second is the development of a new hand shape classifier that can accommodate pronation angle changes. The developed sensing device enables wrist contour data collection under conditions in which the pronation angle varies. The classifier recognizes the hand shape based on statistics produced through data forming and statistics conversion processes. The most important result is that no large difference exists between classification rates that include or those that exclude the independent (preliminary) pronation estimation process using inertia measurement units. This result shows two possible insights: (1) the wrist contour has some features that depend on the hand shape but which do not depend on the pronation angle, or (2) the wrist contour potentially includes pronation angle variation information. These insights indicate the possibility that hand shape can be recognized solely from the wrist contour, even while changing the pronation angle.

Finger Joint Angle Estimation from Electrical Contact Resistance

“…The study of the forearm pronation/supination motions based on a photoreflector [13] for assessing the forearm pronation motion effect on the measurement device, and it was revealed that the hand postures can be identified by referring to the wrist shape even with the forearm in pronation motion. Thus, for the purpose of measuring hand motions without restricting the user's actions, the effects of forearm and wrist motions on the measurement device have to be taken into consideration.…”

“…For example, it is necessary to examine the wrist deformation due to the forearm pronation/supination motions and forearm angle. The study of the forearm pronation/supination motions based on a photoreflector [13] for assessing the forearm pronation motion effect on the measurement device, and it was revealed that the hand postures can be identified by referring to the wrist shape even with the forearm in pronation motion. In addition, the measurement device used in the proposed method is attached to the wrist in stable contact state, and it is thin and smallsized which is robust against the gravitational force and the like.…”

Gesture Recognition Method Utilizing Ultrasonic Active Acoustic Sensing

“…Pirkl et al [20] described the design and implementation of a cheap, low power, and easily wearable system for tracking the relative position and orientation of body parts by utilizing magnetic field technology. Fukui et al [12] proposed an approach to hand shape recognition based on wrist contour measurement. Sato et al [23] proposed Swept Frequency Capacitive Sensing technique that can not only detect a touch event but also recognize complex configurations of the human hands and body.…”

A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition

“…HMM-based techniques may also not be suitable for hard real-time (synchronized clock-based) systems due to its latency [5]. Since data sets are not necessarily large enough for training, Support Vector Machine (SVM) is a classical alternative method [6][7][8]. SVM is, nevertheless, very sensitive to the selection of its kernel type and parameters related to the latter.…”