Experimental assessment of the interface electronic system for PVDF-based piezoelectric tactile sensors

Tactile sensors are widely employed to enable the sense of touch for applications such as robotics and prosthetics. In addition to the selection of an appropriate sensing material, the performance of the tactile sensing system is conditioned by its interface electronic system. On the other hand, due to the need to embed the tactile sensing system into a prosthetic device, strict requirements such as small size and low power consumption are imposed on the system design. This paper presents the experimental assessment and characterization of an interface electronic system for piezoelectric tactile sensors for prosthetic applications. The interface electronic is proposed as part of a wearable system intended to be integrated into an upper limb prosthetic device. The system is based on a low power arm-microcontroller and a DDC232 device. Electrical and electromechanical setups have been implemented to assess the response of the interface electronic with PVDF-based piezoelectric sensors. The results of electrical and electromechanical tests validate the correct functionality of the proposed system

Embedded Electronic Systems for Electronic Skin Applications

The advances in sensor devices are potentially providing new solutions to many applications including prosthetics and robotics. Endowing upper limb prosthesis with tactile sensors (electronic/sensitive skin) can be used to provide tactile sensory feedback to the amputees. In this regard, the prosthetic device is meant to be equipped with tactile sensing system allowing the user limb to receive tactile feedback about objects and contact surfaces. Thus, embedding tactile sensing system is required for wearable sensors that should cover wide areas of the prosthetics. However, embedding sensing system involves set of challenges in terms of power consumption, data processing, real-time response and design scalability (e-skin may include large number of tactile sensors). The tactile sensing system is constituted of: (i) a tactile sensor array, (ii) an interface electronic circuit, (iii) an embedded processing unit, and (iv) a communication interface to transmit tactile data. The objective of the thesis is to develop an efficient embedded tactile sensing system targeting e-skin application (e.g. prosthetic) by: 1) developing a low power and miniaturized interface electronics circuit, operating in real-time; 2) proposing an efficient algorithm for embedded tactile data processing, affecting the system time latency and power consumption; 3) implementing an efficient communication channel/interface, suitable for large amount of data generated from large number of sensors. Most of the interface electronics for tactile sensing system proposed in the literature are composed of signal conditioning and commercial data acquisition devices (i.e. DAQ). However, these devices are bulky (PC-based) and thus not suitable for portable prosthetics from the size, power consumption and scalability point of view. Regarding the tactile data processing, some works have exploited machine learning methods for extracting meaningful information from tactile data. However, embedding these algorithms poses some challenges because of 1) the high amount of data to be processed significantly affecting the real time functionality, and 2) the complex processing tasks imposing burden in terms of power consumption. On the other hand, the literature shows lack in studies addressing data transfer in tactile sensing system. Thus, dealing with large number of sensors will pose challenges on the communication bandwidth and reliability. Therefore, this thesis exploits three approaches: 1) Developing a low power and miniaturized Interface Electronics (IE), capable of interfacing and acquiring signals from large number of tactile sensors in real-time. We developed a portable IE system based on a low power arm microcontroller and a DDC232 A/D converter, that handles an array of 32 tactile sensors. Upon touch applied to the sensors, the IE acquires and pre-process the sensor signals at low power consumption achieving a battery lifetime of about 22 hours. Then we assessed the functionality of the IE by carrying out Electrical and electromechanical characterization experiments to monitor the response of the interface electronics with PVDF-based piezoelectric sensors. The results of electrical and electromechanical tests validate the correct functionality of the proposed system. In addition, we implemented filtering methods on the IE that reduced the effect of noise in the system. Furthermore, we evaluated our proposed IE by integrating it in tactile sensory feedback system, showing effective deliver of tactile data to the user. The proposed system overcomes similar state of art solutions dealing with higher number of input channels and maintaining real time functionality. 2) Optimizing and implementing a tensorial-based machine learning algorithm for touch modality classification on embedded Zynq System-on-chip (SoC). The algorithm is based on Support Vector Machine classifier to discriminate between three input touch modality classes \u201cbrushing\u201d, \u201crolling\u201d and \u201csliding\u201d. We introduced an efficient algorithm minimizing the hardware implementation complexity in terms of number of operations and memory storage which directly affect time latency and power consumption. With respect to the original algorithm, the proposed approach \u2013 implemented on Zynq SoC \u2013 achieved reduction in the number of operations per inference from 545 M-ops to 18 M-ops and the memory storage from 52.2 KB to 1.7 KB. Moreover, the proposed method speeds up the inference time by a factor of 43 7 at a cost of only 2% loss in accuracy, enabling the algorithm to run on embedded processing unit and to extract tactile information in real-time. 3) Implementing a robust and efficient data transfer channel to transfer aggregated data at high transmission data rate and low power consumption. In this approach, we proposed and demonstrated a tactile sensory feedback system based on an optical communication link for prosthetic applications. The optical link features a low power and wide transmission bandwidth, which makes the feedback system suitable for large number of tactile sensors. The low power transmission is due to the employed UWB-based optical modulation. We implemented a system prototype, consisting of digital transmitter and receiver boards and acquisition circuits to interface 32 piezoelectric sensors. Then we evaluated the system performance by measuring, processing and transmitting data of the 32 piezoelectric sensors at 100 Mbps data rate through the optical link, at 50 pJ/bit communication energy consumption. Experimental results have validated the functionality and demonstrated the real time operation of the proposed sensory feedback system

Low-Power, Event-Driven System on a Chip for Charge Pulse Processing Applications

This dissertation presents an electronic architecture and methodology capable of processing charge pulses generated by a range of sensors, including radiation detectors and tactile synthetic skin. These sensors output a charge signal proportional to the input stimulus, which is processed electronically in both the analog and digital domains. The presented work implements this functionality using an event-driven methodology, which greatly reduces power consumption compared to standard implementations. This enables new application areas that require a long operating time or compact physical dimensions, which would not otherwise be possible. The architecture is designed, fabricated, and tested in the aforementioned applications to demonstrate its highly flexible and low-power operation. Advisors: Sina Balkır and Michael W. Hoffma

Low-Power, Event-Driven System on a Chip for Charge Pulse Processing Applications

This dissertation presents an electronic architecture and methodology capable of processing charge pulses generated by a range of sensors, including radiation detectors and tactile synthetic skin. These sensors output a charge signal proportional to the input stimulus, which is processed electronically in both the analog and digital domains. The presented work implements this functionality using an event-driven methodology, which greatly reduces power consumption compared to standard implementations. This enables new application areas that require a long operating time or compact physical dimensions, which would not otherwise be possible. The architecture is designed, fabricated, and tested in the aforementioned applications to demonstrate its highly flexible and low-power operation

Human-Machine Interfaces using Distributed Sensing and Stimulation Systems

As the technology moves towards more natural human-machine interfaces (e.g. bionic limbs, teleoperation, virtual reality), it is necessary to develop a sensory feedback system in order to foster embodiment and achieve better immersion in the control system. Contemporary feedback interfaces presented in research use few sensors and stimulation units to feedback at most two discrete feedback variables (e.g. grasping force and aperture), whereas the human sense of touch relies on a distributed network of mechanoreceptors providing a wide bandwidth of information. To provide this type of feedback, it is necessary to develop a distributed sensing system that could extract a wide range of information during the interaction between the robot and the environment. In addition, a distributed feedback interface is needed to deliver such information to the user. This thesis proposes the development of a distributed sensing system (e-skin) to acquire tactile sensation, a first integration of distributed sensing system on a robotic hand, the development of a sensory feedback system that compromises the distributed sensing system and a distributed stimulation system, and finally the implementation of deep learning methods for the classification of tactile data. It\u2019s core focus addresses the development and testing of a sensory feedback system, based on the latest distributed sensing and stimulation techniques. To this end, the thesis is comprised of two introductory chapters that describe the state of art in the field, the objectives, and the used methodology and contributions; as well as six studies that tackled the development of human-machine interfaces

Truck-based mobile wireless sensor networks for the experimental observation of vehicle–bridge interaction

Heavy vehicles driving over a bridge create a complex dynamic phenomenon known as vehicle–bridge interaction. In recent years, interest in vehicle–bridge interaction has grown because a deeper understanding of the phenomena can lead to improvements in bridge design methods while enhancing the accuracy of structural health monitoring techniques. The mobility of wireless sensors can be leveraged to directly monitor the dynamic coupling between the moving vehicle and the bridge. In this study, a mobile wireless sensor network is proposed for installation on a heavy truck to capture the vertical acceleration, horizontal acceleration and gyroscopic pitching of the truck as it crosses a bridge. The vehicle-based wireless monitoring system is designed to interact with a static, permanent wireless monitoring system installed on the bridge. Specifically, the mobile wireless sensors time-synchronize with the bridge's wireless sensors before transferring the vehicle response data. Vertical acceleration and gyroscopic pitching measurements of the vehicle are combined with bridge accelerations to create a time-synchronized vehicle–bridge response dataset. In addition to observing the vehicle vibrations, Kalman filtering is adopted to accurately track the vehicle position using the measured horizontal acceleration of the vehicle and positioning information derived from piezoelectric strip sensors installed on the bridge deck as part of the bridge monitoring system. Using the Geumdang Bridge (Korea), extensive field testing of the proposed vehicle–bridge wireless monitoring system is conducted. Experimental results verify the reliability of the wireless system and the accuracy of the vehicle positioning algorithm.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90810/1/0964-1726_20_6_065009.pd

Development of PVDF tactile dynamic sensing in a behaviour-based assembly robot

The research presented in this thesis focuses on the development of tactile event sig¬ nature sensors and their application, especially in reactive behaviour-based robotic assembly systems.In pursuit of practical and economic sensors for detecting part contact, the application ofPVDF (polyvinylidene fluoride) film, a mechanical vibration sensitive piezo material, is investigated. A Clunk Sensor is developed which remotely detects impact vibrations, and a Push Sensor is developed which senses small changes in the deformation of a compliant finger surface. The Push Sensor is further developed to provide some force direction and force pattern sensing capability.By being able to detect changes of state in an assembly, such as a change of contact force, an assembly robot can be well informed of current conditions. The complex structure of assembly tasks provides a rich context within which to interpret changes of state, so simple binary sensors can conveniently supply a lot more information than in the domain of mobile robots. Guarded motions, for example, which require sensing a change of state, have long been recognised as very useful in part mating tasks. Guarded motions are particularly well suited to be components of assembly behavioural modules.In behaviour-based robotic assembly systems, the high level planner is endowed with as little complexity as possible while the low level planning execution agent deals with actual sensing and action. Highly reactive execution agents can provide advantages by encapsulating low level sensing and action, hiding the details of sensori-motor complexity from the higher levels.Because behaviour-based assembly systems emphasise the utility of this kind of quali¬ tative state-change sensor (as opposed to sensors which measure physical quantities), the robustness and utility of the Push Sensor was tested in an experimental behaviourbased system. An experimental task of pushing a ring along a convoluted stiff wire is chosen, in which the tactile sensors developed here are aided by vision. Three differ¬ ent methods of combining these different sensors within the general behaviour-based paradigm are implemented and compared. This exercise confirms the robustness and utility of the PVDF-based tactile sensors. We argue that the comparison suggests that for behaviour-based assembly systems using multiple concurrent sensor systems, bottom-level motor control in terms of force or velocity would be more appropriate than positional control. Behaviour-based systems have traditionally tried to avoid symbolic knowledge. Considering this in the light of the above work, it was found useful to develop a taxonomy of type of knowledge and refine the prohibition

Event Driven Tactile Sensors for Artificial Devices

Present-day robots are, to some extent, able to deal with high complexity and variability of the real-world environment. Their cognitive capabilities can be further enhanced, if they physically interact and explore the real-world objects. For this, the need for efficient tactile sensors is growing day after day in such a way are becoming more and more part of daily life devices especially in robotic applications for manipulation and safe interaction with the environment. In this thesis, we highlight the importance of touch sensing in humans and robots. Inspired by the biological systems, in the the first part, we merge between neuromorphic engineering and CMOS technology where the former is a eld of science that replicates what is biologically (neurons of the nervous system) inside humans into the circuit level. We explain the operation and then characterize different sensor circuits through simulation and experiment to propose finally new prototypes based on the achieved results. In the second part, we present a machine learning technique for detecting the direction and orientation of a sliding tip over a complete skin patch of the iCub robot. Through learning and online testing, the algorithm classies different trajectories across the skin patch. Through this part, we show the results of the considered algorithm with a future perspective to extend the work

Self-sensing composite material based on piezoelectric nanofibers

Recently, efforts have been made to manufacture self-sensing smart composites by integrating piezoelectric sensors with laminates. However, the interleaving of pressure sensors, such as piezoelectric polymeric films, dramatically reduces the impact resistance of the hosting laminates, and consequently, delamination can occur. This study aimed to fabricate a self-sensing composite material by embedding piezoelectric nanofibers of poly(vinylidenefluoride-trifluoroethylene) (PVDF-TrFE) in a polymeric elastic matrix and carbon black-based electrodes to detect a piezoelectric signal. The mechanical and electrical properties of the self-sensing laminate were maintained after 106 fatigue test cycles. By appropriately tuning the parameters of the acquisition circuit, the sensor could measure not only impulsive loads but also low-frequency loads as low as 0.5 Hz. A piezoelectric model with lumped parameters for the polarization process and piezoelectric response of the nanofibers is proposed and validated by experimental results. As a proof of the model, the piezoelectric nanofiber sensors were embedded in a prosthetic carbon fiber sole, and the piezoelectric signal response closely followed the ground reaction force with a sensitivity of 0.14 mV/N