In the past two years, there have been a large number of publications on the topic of biomimetic dry adhesives from modeling, fabrication and testing perspectives. We review and compare the most recent advances in fabrication and testing of these materials. While there is increased convergence and consensus as to what makes a good dry adhesive, the fabrication of these materials is still challenging, particularly for anisotropic or hierarchal designs. Although qualitative comparisons between different adhesive designs can be made, quantifying the exact performance and rating each design is significantly hampered by the lack of standardized testing methods. Manufacturing dry adhesives, which can reliably adhere to rough surfaces, show directional and self-cleaning behavior and are relatively simple to manufacture, is still very challenging-great strides by multiple research groups have however made these goals appear achievable within the next few years.

The task of discriminating the motor imagery of different movements within the same limb using electroencephalography (EEG) signals is challenging because these imaginary movements have close spatial representations on the motor cortex area. There is, however, a pressing need to succeed in this task. The reason is that the ability to classify different same-limb imaginary movements could increase the number of control dimensions of a brain-computer interface (BCI). In this paper, we propose a 3-class BCI system that discriminates EEG signals corresponding to rest, imaginary grasp movements, and imaginary elbow movements. Besides, the differences between simple motor imagery and goal-oriented motor imagery in terms of their topographical distributions and classification accuracies are also being investigated. To the best of our knowledge, both problems have not been explored in the literature. Based on the EEG data recorded from 12 able-bodied individuals, we have demonstrated that same-limb motor imagery classification is possible. For the binary classification of imaginary grasp and elbow (goal-oriented) movements, the average accuracy achieved is 66.9%. For the 3-class problem of discriminating rest against imaginary grasp and elbow movements, the average classification accuracy achieved is 60.7%, which is greater than the random classification accuracy of 33.3%. Our results also show that goal-oriented imaginary elbow movements lead to a better classification performance compared to simple imaginary elbow movements. This proposed BCI system could potentially be used in controlling a robotic rehabilitation system, which can assist stroke patients in performing task-specific exercises.

We present a low-cost, large-scale method of fabricating biomimetic dry adhesives. This process is useful because it uses all photosensitive polymers with minimum fabrication costs or complexity to produce molds for silicone-based dry adhesives. A thick-film lift-off process is used to define molds using AZ 9260 photoresist, with a slow acting, deep UV sensitive material, PMGI, used as both an adhesion promoter for the AZ 9260 photoresist and as an undercutting material to produce mushroom-shaped fibers. The benefits to this process are ease of fabrication, wide range of potential layer thicknesses, no special surface treatment requirements to demold silicone adhesives and easy stripping of the full mold if process failure does occur. Sylgard R 184 silicone is used to cast full sheets of biomimetic dry adhesives off 4" diameter wafers, and different fiber geometries are tested for normal adhesion properties. Additionally, failure modes of the adhesive during fabrication are noted and strategies for avoiding these failures are discussed. We use this fabrication method to produce different fiber geometries with varying cap diameters and test them for normal adhesion strengths. The results indicate that the cap diameters relative to post diameters for mushroom-shaped fibers dominate the adhesion properties.

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BackgroundSurface electromyography (sEMG) signals have been used in numerous studies for the classification of hand gestures and movements and successfully implemented in the position control of different prosthetic hands for amputees. sEMG could also potentially be used for controlling wearable devices which could assist persons with reduced muscle mass, such as those suffering from sarcopenia. While using sEMG for position control, estimation of the intended torque of the user could also provide sufficient information for an effective force control of the hand prosthesis or assistive device. This paper presents the use of pattern recognition to estimate the torque applied by a human wrist and its real-time implementation to control a novel two degree of freedom wrist exoskeleton prototype (WEP), which was specifically developed for this work.MethodsBoth sEMG data from four muscles of the forearm and wrist torque were collected from eight volunteers by using a custom-made testing rig. The features that were extracted from the sEMG signals included root mean square (rms) EMG amplitude, autoregressive (AR) model coefficients and waveform length. Support Vector Machines (SVM) was employed to extract classes of different force intensity from the sEMG signals. After assessing the off-line performance of the used classification technique, the WEP was used to validate in real-time the proposed classification scheme.ResultsThe data gathered from the volunteers were divided into two sets, one with nineteen classes and the second with thirteen classes. Each set of data was further divided into training and testing data. It was observed that the average testing accuracy in the case of nineteen classes was about 88% whereas the average accuracy in the case of thirteen classes reached about 96%. Classification and control algorithm implemented in the WEP was executed in less than 125 ms.ConclusionsThe results of this study showed that classification of EMG signals by separating different levels of torque is possible for wrist motion and the use of only four EMG channels is suitable. The study also showed that SVM classification technique is suitable for real-time classification of sEMG signals and can be effectively implemented for controlling an exoskeleton device for assisting the wrist.