The goal of this work is the identification of humans based on motion data in the form of natural hand gestures. In this paper, the identification problem is formulated as classification with classes corresponding to persons' identities, based on recorded signals of performed gestures. The identification performance is examined with a database of twenty-two natural hand gestures recorded with two types of hardware and three state-of-art classifiers: Linear Discrimination Analysis (LDA), Support Vector machines (SVM) and k-Nearest Neighbour (k-NN). Results show that natural hand gestures allow for an effective human classification.
While non-invasive vision-based methods for gathering hand movement data are popular, device-based techniques receive attention due to widespread use of motion sensors in mobile devices. For example @cite_20 presents a high performance, two-stage recognition algorithm for acceleration signals, that was adapted in Samsung cell phones.
@cite_20
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This paper presents an energy-aware method for recognizing time series acceleration data containing both activities and gestures using a wearable device coupled with a smartphone. In our method, we use a small wearable device to collect accelerometer data from a user's wrist, recognizing each data segment using a minimal feature set chosen automatically for that segment. For each collected data segment, if our model finds that recognizing the segment requires high-cost features that the wearable device cannot extract, such as dynamic time warping for gesture recognition, then the segment is transmitted to the smartphone where the high-cost features are extracted and recognition is performed. Otherwise, only the minimum required set of low-cost features are extracted from the segment on the wearable device and only the recognition result, i.e., label, is transmitted to the smartphone in place of the raw data, reducing transmission costs. Our method automatically constructs this adaptive processing pipeline solely from training data. Reliable hand detection and tracking in passive 2D video still remains a challenge. Yet the consumer market for gesture-based interaction is expanding rapidly and surveillance systems that can deduce fine-grained human activities involving hand and arm postures are in high demand. In this paper, we present a hand tracking method that does not require reliable detection. We built it on top of “Flocks of Features” which combines grey-level optical flow, a “flocking” constraint, and a learned foreground color distribution. By adding probabilistic (instead of binary classified) detections based on grey-level appearance as an additional image cue, we show improved tracking performance despite rapid hand movements and posture changes. This helps overcome tracking difficulties in texture-rich and skin-colored environments, improving performance on a 10-minute collection of video clips from 75 to 86 (see examples on our website). Direct use of the hand as an input device is an attractive method for providing natural human-computer interaction (HCI). Currently, the only technology that satisfies the advanced requirements of hand-based input for HCI is glove-based sensing. This technology, however, has several drawbacks including that it hinders the ease and naturalness with which the user can interact with the computer-controlled environment, and it requires long calibration and setup procedures. Computer vision (CV) has the potential to provide more natural, non-contact solutions. As a result, there have been considerable research efforts to use the hand as an input device for HCI. In particular, two types of research directions have emerged. One is based on gesture classification and aims to extract high-level abstract information corresponding to motion patterns or postures of the hand. The second is based on pose estimation systems and aims to capture the real 3D motion of the hand. This paper presents a literature review on the latter research direction, which is a very challenging problem in the context of HCI. Generating a visually appealing human motion sequence using low-dimensional control signals is a major line of study in the motion research area in computer graphics. We propose a novel approach that allows us to reconstruct full body human locomotion using a single inertial sensing device, a smartphone. Smartphones are among the most widely used devices and incorporate inertial sensors such as an accelerometer and a gyroscope. To find a mapping between a full body pose and smartphone sensor data, we perform low dimensional embedding of full body motion capture data, based on a Gaussian Process Latent Variable Model. Our system ensures temporal coherence between the reconstructed poses by using a state decomposition model for automatic phase segmentation. Finally, application of the proposed nonlinear regression algorithm finds a proper mapping between the latent space and the sensor data. Our framework effectively reconstructs plausible 3D locomotion sequences. We compare the generated animation to ground truth data obtained using a commercial motion capture system.