Dynamic Spatiotemporal Pattern Identification and Analysis Using a Fingertipbased ElectroTactile Display Array This study is designed to validate the feasibility of generating identifiable moving patterns using electrotactile stimulation. An electrotactile display is built using an array of 16 contacts to deliver the electrical signal to the fingertip skin. This signal can have varying voltages, frequencies or duty cycles to form the most comfortable sensation.Moving patterns can be generated by individually or collectively switching on or off the contacts on the display. This is done to stimulate a moving pattern. In this case, a moving pattern is comparable to a group of framebyframe pictures constructing a movie. Similarly, by toggling the contacts in a specific order, a moving pattern can be achieved.A program on a singleboard computer (Raspberry Pi) was used to control and generate 6 different patterns. These patterns are delivered to the display and consequently to the fingertip skin of the participants. A total of 8 subjects participated in this study. They filled a questionnaire to indicate the corresponding movement.The results of these experiments were analyzed and a conclusion regarding the direction of the movement was drawn. It became clear that the direction of the movement had a significant impact on the recognition of the patterns.

Sequential clustering for tactile image compression to enable direct adaptive feedback The sense of touch is often crucial for humans to perform manipulation tasks. Providing tactile feedback during teleoperation or for users of prosthetic devices would be beneficial. However, the representation of tactile information constitutes a major technical challenge, since the numerous and possibly multimodal sensor readings are massive compared to the available tactile display technology. We introduce an algorithm that deploys two stages of Kmeans clustering along and across tactile image frames that render tactile sensor information at each time instant. In this manner, the massive tactile information is adaptively compressed in realtime while preserving its physical meaning, thus, remains intuitive and direct. We experimentally verify and examine the characteristics of our algorithm by evaluating the original and compressed tactile data. The data was gathered during the active tactile exploration of several objects of daily living by an Allegro robot hand that was covered with 15 uSkin sensor modules providing 2403axis force vector measurements at each time instant. Our novel algorithm is straight forward enough to be implemented into tactile feedback systems. Finally, our algorithm allows for the direct feedback of massive tactile sensor data for a broad variety of tactile sensors and tactile displays, thereby, enables the compressed yet intuitive representation of massive tactile sensor information for realtime applications.