2.5
Summary
Learning from human hand motions provides a feasible way to transfer human hand motion skills to robot motions by a paradigm that can endow artifacts with the abil- ity for skill growth and life-long adaptation without detailed programming. Not only does it provide a user-friendly means of implicitly programming the robot, but it also makes the learning problem become significantly more tractable by separating redun- dancies from important characteristics of a task. In this Chapter, the developments of the three main fundamental phases of the learning process for multfingered manipula- tion are reported, which are human motion capture, hand modeling and hand motion recognition.
Hand motion capture is fundamental to human hand skill learning. The relationship between an EMG signal and the corresponding motion is not a one-to-one relationship because it varies with time (Kato et al.,2006). So far EMG has just been used for very limited gestures recognition. The accuracy of recognition method based on EMG is lower than two other methods. In many applications, a DataGlove is preferred since it can get data directly, without processing images, which is a hard and complex job for cameras. If the problem of huge computational cost of image processing for fea- ture extracting is solved, the non-contact capture using cameras could have the most potential way to model the human hand skills.
Though there are several alternatives for biological hand modeling, not only is it tremendously difficult to fully model the human hand due to its complex kinematic structure, but also such a detailed model is unnecessary for multifingered robotics. On the other hand, from the modeling perspective, the motion constraints among fingers and finger joints reduce the size and dimensions of the search space, making the esti- mation of hand postures more cost-effective. The static hand model consisting of 2D and 3D models emphasizes the hand appearance and hand deformation, which provide a visual and straight impression of hand. Dynamic hand model is preferred to store the human hand motion skills since it involves both spatial and temporal information.
Human hand motion recognition is becoming a field of great interest and playing a key role in a number of research areas and applications especially in multifingered robot manipulation. However, due to the dexterity and complexity of the human hand, the recognition of human hand motion involving spatio-temporal variability is still an
2.5 Summary
open problem though it has been investigated over the past two decades. Most of the existing methods can only achieve satisfactory results with sufficient training samples, which are difficult, sometimes impossible, to get in realistic scenarios, for example the trade-off between computational cost and recognition accuracy have to be handled for HMMs. The slow training processes of the statistic methods, e.g. GMMs, FSM and GMMs have limited their applications especially in real-time systems. In general, the real-time issue has been one of the bottlenecks for research development and practical implementation of human hand motion recognition. Most of the current methodolo- gies do not satisfy the requirements imposed by the different subjects with different personal issues such as habit, practice and anthropometric measurement. In addition, complex human hand motions such as in-hand manipulation has not been addressed. Thus several approaches with different extracted features to solve this problem are considered. TC (Chapter 3), based on the numerical value, is used to model the tra- jectories using TS fuzzy modeling; FGMM (Chapter 4) uses the Gaussian pattern as the extracted motion feature; FEC (Chapter5) studies the dependence structure among finger angle values and provides discriminating motion templates to differentiate the motions.
Chapter 3
Time Clustering
3.1
Introduction
Human hand motion recognition is the key for many research areas and applications es- pecially in multifingered robot manipulation and is attracting more and more research interest. However, due to the dexterity and complexity of the human hand, it is still a challenge though it had been investigated in the past two decades. A large number of approaches have been proposed and studied for human hand gesture recognition, but current recognition methods are still facing many problems such as constrained con- ditions and real-time issues. The speed and precision requirements of the recognition algorithm are difficult to fulfill simultaneously, thus the trade-offs between the speed and accuracy needs to be handled.
To address this problem,Palm et al. (2009) proposed a fuzzy clustering method, time clustering (TC), to recognize human grasps. To model time dependent trajectories using fuzzy modeling, the time instance takes the place of the input variable and the corresponding trajectory points become the outputs of the model. It has been demon- strated that this fuzzy method can speedily represent the dynamic hand grasps by a small number of local linear models and a few parameters (Palm et al., 2009). It can also be used for nonlinear filtering of noisy trajectories and as a simple interpolation between data samples. In addition, it is capable of identifying the start and end points of segments and thus the occurrence of grasps, as well as recognizing the grasp types themselves.