Survey of Biomechanical Models for the Human Shoulder Complex

Continuous body and hand gesture recognition for natural human-computer interaction

Intelligent gesture recognition systems open a new era of natural human-computer interaction: Gesturing is instinctive and a skill we all have, so it requires little or no thought, leaving the focus on the task itself, as it should be, not on the interaction modality. We present a new approach to gesture recognition that attends to both body and hands, and interprets gestures continuously from an unsegmented and unbounded input stream. This article describes the whole procedure of continuous body and hand gesture recognition, from the signal acquisition to processing, to the interpretation of the processed signals.

Our system takes a vision-based approach, tracking body and hands using a single stereo camera. Body postures are reconstructed in 3D space using a generative model-based approach with a particle filter, combining both static and dynamic attributes of motion as the input feature to make tracking robust to self-occlusion. The reconstructed body postures guide searching for hands. Hand shapes are classified into one of several canonical hand shapes using an appearance-based approach with a multiclass support vector machine. Finally, the extracted body and hand features are combined and used as the input feature for gesture recognition. We consider our task as an online sequence labeling and segmentation problem. A latent-dynamic conditional random field is used with a temporal sliding window to perform the task continuously. We augment this with a novel technique called multilayered filtering, which performs filtering both on the input layer and the prediction layer. Filtering on the input layer allows capturing long-range temporal dependencies and reducing input signal noise; filtering on the prediction layer allows taking weighted votes of multiple overlapping prediction results as well as reducing estimation noise.

We tested our system in a scenario of real-world gestural interaction using the NATOPS dataset, an official vocabulary of aircraft handling gestures. Our experimental results show that: (1) the use of both static and dynamic attributes of motion in body tracking allows statistically significant improvement of the recognition performance over using static attributes of motion alone; and (2) the multilayered filtering statistically significantly improves recognition performance over the nonfiltering method. We also show that, on a set of twenty-four NATOPS gestures, our system achieves a recognition accuracy of 75.37%.

Determining the three-dimensional relation between the skeletal elements of the human shoulder complex

In this paper, we present an inverse kinematics method to determining human shoulder joint motion coupling relationship based on experimental data in the literature. This work focuses on transferring Euler-angle-based coupling equations into a relationship based on the Denavit-Hartenberg (DH) method. We use analytical inverse kinematics to achieve the transferring. For a specific posture, we can choose points on clavicle, scapula, and humerus and represent the end-effector positions based on Euler angles or DH method. For both Euler and DH systems, the end-effectors have the same Cartesian positions. Solving these equations related to end-effector positions yields DH joint angles for that posture. The new joint motion coupling relationship is obtained by polynomial and cosine fitting of the DH joint angles for all different postures.