Blockchain Papers

Gongfa Li, Dongxu Bai, Guozhang Jiang, Du Jiang · 7 authors

No abstract is available for this record.

Yu‐Jin Kwon, Kornrapat Pongmala, Kaihua Qin, Ariah Klages‐Mundt · 8 authors

In May 2022, an apparent speculative attack, followed by market panic, led to the precipitous downfall of UST, one of the most popular stablecoins at that time. However, UST is not the only stablecoin to have been depegged in the past. Designing resilient and long-term stable coins, therefore, appears to present a hard challenge. To further scrutinize existing stablecoin designs and ultimately lead to more robust systems, we need to understand where volatility emerges. Our work provides a game-theoretical model aiming to help identify why stablecoins suffer from a depeg. This game-theoretical model reveals that stablecoins have different price equilibria depending on the coin's architecture and mechanism to minimize volatility. Moreover, our theory is supported by extensive empirical data, spanning $1$ year. To that end, we collect daily prices for 22 stablecoins and on-chain data from five blockchains including the Ethereum and the Terra blockchain.

Md. Abdur Rahman, M. Shamim Hossain, Md. Mamunur Rashid, Stuart J. Barnes · 6 authors

Although ElectroEncephaloGram (EEG) signals allow subjects suffering from neuromuscular disorders to interface their brains with the cyber-physical world, occupational therapy can be enhanced with the introduction of further modalities better assist the disabled person. In this paper, we propose an in-home occupational therapy environment, which leverages a rich set of occupational therapy-related activity recognition modalities, namely, EEG signals to understand brain activity, ElectroMyoGram (EMG) signals for muscle activity, gesture-tracking sensors for forward and inverse kinematics activities, and smart home appliance control sensors. To support a wide variety of disabled people's in-home occupational therapy, we have incorporated both selective attention and motor imagery processes for mapping a mental command with that of an occupational therapy-related command within a serious game environment. To attain higher accuracy and to avoid a higher number of false positives, a subject is first recommended to use a selective attention-based serious game in which a digital avatar of the subject acting as a model therapist will guide the therapy session. Once familiar with the generation of proper motor imagery, an advanced user can use self-paced motor imagery signals to perform occupational therapy activities within the serious game environment. The occupational therapy consists of a serious game environment in which smart home appliances are mapped with therapeutic activities through forward and inverse kinematics. The therapy data has been secured through blockchain and off-chain-based distributed repositories. The test results show the viability of using the framework in a clinical environment.

Timothy H. Lucas, Xilin Liu, Milin Zhang, Sri Sritharan · 10 authors

BCI: brain–computer interface DCN: dorsal column nuclei ICMS: intracortical microstimulation LED: light-emitting diode PDMS: polydimethylsiloxane RF: radiofrequency The dexterous hand is a defining feature of human existence. Evolved over tens of millions of years, modern humans are able to perform remarkable tasks with their hands. From typing hundreds of words per minute to playing Rachmaninoff's Piano Concerto No. 2, the dexterous hand defines us. Unfortunately, a number of maladies rob us of this defining human characteristic. In the most extreme case, paralyzed individuals lose communication between the brain and the periphery. This condition affects an estimated 5.4 million people, or 2% of the US population.1 At present, no effective treatment restores function to these individuals. Regaining hand function is a principal concern for paralyzed patients. Toward this aim, significant advances in motor—or efferent—brain–computer interface (BCI) systems have occurred in recent years. Efferent BCI systems extract movement-relevant information from electrocorticography (ECoG) or electroencephalography (EEG). These analogue signals are transformed into control commands to drive robotic arms2 or evoke muscle contractions in paralyzed limbs.3-8 In the later example, compound wrist flexion may be evoked by brain-controlled functional electrical stimulation of forearm flexors. Planned clinical trials aim to capitalize upon these scientific advances to test efferent BCI across a range of conditions and control routines. While these proof-of-principal systems are encouraging, a number of substantial hurdles remain. Perhaps the most pressing barrier to restoring dexterous hand movements is the lack of systems to restore somatosensory feedback. Even in the presence of intact descending motor systems, precise hand movements are abolished when somatosensation is missing.9-16 Indeed, the majority of efferent BCI systems currently in testing rely solely upon visual guidance. This constraint is unnatural and unlikely to be useful if deployed clinically. Visual guidance requires constant vigilance and introduces substantial time-lags to error correct each movement. To restore naturalistic movements, bi-directional BCI systems that link movements and real-time sensory feedback must be developed. The feedback loop of bi-directional BCI is closed with sensory feedback. Unfortunately, the field of sensory—or afferent—brain–computer interface has not kept pace with the maturation of efferent systems. This is due, in part, to the challenges concerning sensory research in animals. Sensory perception is a uniquely subjective experience that does not lend itself readily to the quantitative metrics. For decades, experimentalists have attempted to characterize the perceptual experiences associated with stimulation of the sensory cortices, including primary somatosensory cortex (S1), secondary somatosensory cortex (S2), and parietal association areas in animal models. From this body of literature, we know that intracortical microstimulation (ICMS) of S1 yields sufficient percepts to permit limited binary decisions, such as differentiating between 2 stimulation frequencies or amplitudes.17-21 Despite exhaustive investigation, no study has convincingly reproduced the complex sensory phenomena that are fundamental to our routine encounters with the physical world. Compounding the problem, very limited human data are available to assess the efficacy of S1 stimulation. Animal studies do not answer the question of how stimulation feels. To answer these qualitative questions, we need human data. Most human data have been obtained during brief testing sessions in awake craniotomies or during stimulation in patients with implanted ECoG electrodes.22-24 Invariably, these patients reported that S1 stimulation yielded only vague ‘tingling’ sensations with modest regional localization. Flesher and colleagues recently reported the first human data using ICMS encoding in S1 with chronic penetrating arrays.25 In this experiment, a 28-yr-old male with a spinal cord injury underwent implantation of 2 32-channel multi-electrode arrays into primary somatosensory cortex (S1). Over the course of several months, the investigators mapped perceptual responses to ICMS up to 100 μA. The majority of responses (93%) were categorized as ‘possibly natural,’ ‘pressure’ sensations. The perceptual intensity was modulated by stimulation amplitude with increased pressure corresponding to increase stimulus amplitude. This finding mirrors that of ICMS in primary visual cortex where phosphine brightness is modulated by stimulus amplitude.26 These data constitute a substantial step toward clinical sensory BCI. However, there were a number of findings that tempered enthusiasm for immediately clinical implementation. For instance, none of the S1 electrodes activated sensory representations of the distal fingers where feedback is most needed. Instead, the majority of responses were localized to the palmar crease region of the hand proximal to the fingers. Also, the detection thresholds of a many electrode sites rose significantly over the short course of the study, raising the concern that the effect of S1 encoding will fade over time. Finally, few of the stimuli evoked properly ‘naturalistic’ percepts. These limitations and the disappointing results from similar work in visual cortex raise the question of whether cortical ICMS encoding is the optimal solution for sensory restoration. These unanswered questions motivate our research program. Our work aims to bridge the divide between current state-of-the-art and the clinical needs of our patients. Our overarching strategy is to develop closed-loop, autonomous bidirectional brain–machine interface systems. These systems, as conceived, provide real-time communication between the brain and body. Because the field of efferent BCI has vastly outpaced that of afferent BCI, our work primarily focuses on developing sensory-brain interfaces to couple with existing BCIs (see Bouton et al27 for example). Our strategy focuses on 3 critical intersections of engineering and neuroscience. The first is development of a suite of sensors that serve as mechanoreceptors for the paralyzed, insensate hand. The second is development of a chronic neural interface for artificial sensory encoding. The third is a body area network that links peripheral sensors with novel neural interfaces. The integration of these components is illustrated in Figure 1.FIGURE 1: Body area network. Fully integrated system with implantable force and flex sensors (1, 2), wearable analyzer (3), electrogoniometer (4), and neural interface (5).In this brief overview, we outline our approach, preliminary data, and future directions. This work collectively represents a fruitful collaboration between neurosurgery and electrical engineering. We are grateful to the National Science Foundation for funding our work. RESEARCH APPROACH Our research strategy follows 3 central aims: development of novel sensors, characterization of novel neural interfaces, and development of an autonomous body-area network. Novel Sensors Hand somatosensation can be characterized by a multidimensional space with axes defined by sensory modality (eg, light touch, proprioception), somatotopy, temporal dynamics, the influence of descending central inputs, and brain state. Restoring native somatosensation is perhaps too lofty a goal for a first-generation sensor–brain interface. Instead, we reduce the dimensionality of the problem to a single sensory modality at a single somatotopic location. We have developed a number of force sensors and a proprioceptive sensor as our first aim. The design of our force sensors is constrained by the form and function of the human hand. Relevant design features include: sensor sensitivity, range, power, form-factor, and complexity. Sensitivity is defined as a sensor's accuracy to convert mechanical force into voltage changes on the sensor. Dynamic range captures the extremes of mechanical force spanning interactions between the hand and the physical environment. The feature of power concerns both the requirements of the sensor (active or passive) as well as the sensor's efficiency to convert physical energy into electrical energy. For wireless sensors, the power feature also includes power harvesting and wireless transmission of data. Form-factor is defined as the mechanical properties of the sensor (size, shape) as well as the flexibility and elasticity of the substrate. Finally, the complexity of the sensor constrains fabrication and durability. These competing design constraints inevitably require engineering trade-offs. In the interest of brevity, we focus on 2 prototype force sensors and a proprioceptive sensor to illustrate these engineering trade-offs in the context of sensor–brain interface. First we consider scattering force sensors and optical force sensors before moving toward proprioceptive electrogoniometers. Scattering force sensors operate under the principle of radiofrequency (RF) back scatter. RF identification is a common technique used to track tags, like those attached to garments at a department store to prevent theft or those implanted subdermally in house pets to identify them when they are lost. The central concept is that RF energy polarizes conductive elements, such as the linear segments of an antenna, and scatter energy back in a measurable way. Deformations of the segment length or shape cause a shift in the back-scatter pattern as the polarization of each segment is related to its orientation in a pulsed electromagnetic field. By calibrating the back-scatter patterns induced by force-induced deformations of RF antenna segments, one may indirectly measure forces applied to a flexible antenna implanted under the skin. In the first series of experiments, our group characterized the back-scatter signatures of a number of antenna designs serving as passive sensor nodes. An advantage of passive sensors is that they do not require active power supplies. Therefore, flexible antennas can be implanted under the skin without the need of wires or batteries. Initial antennas were made with copper tape for rapid prototyping. Antenna shapes were constructed into space filling curves (eg, Hilbert, Peano curves) that varied in the number and length of conductive segments (Figure 2). Changes in size and shape of copper RF antennas were associated with reproducible batter scatter properties.FIGURE 2: Passive scattering force sensor design. A, Antenna shapes with different linear segments in second order Hilbert and Peano curves. B and C, Polarization of antenna segments within electromagnetic field. D, Radiofrequency response curves as a function of area of RF tag (left), and shifts in curves with ±2% change in area (right). E, Prototype indium–gallium tags in PDMS substrate. Central reservoir visible in series with antenna segments. F, RF tuning curve of indium–gallium tags in response to forces applied to central reservoir. Rapid shift noted in low end of force axes indicates appropriate sensitivity for precise finger grip.To build force sensitivity, our second series of experiments examined the flexibility of antennas across a range of forces routinely encountered by the human hand. Liquid metal indium–gallium antennas were designed within a flexible, skin-like polydimethylsiloxane (PDMS) substrate. Indium–gallium is a highly conductive eutectic alloy whose melting point is sufficiently low (∼ –2°F) to allow the alloy to remain in liquid phase at room temperature. Channels were laser-etched into the PDMS in the shape of space filling curves to house the alloy (Figure 2). Force sensitivity was amplified by creating a central compressible metal reservoir in series with the channels. When force was applied, the liquid metal filled the channel segments proportionally. As each successful segment of the antenna was filled with conductive metal, the RF back-scatter properties shifted (Figure 2). As can be seen in the RF response curve, the antenna was sufficiently sensitive to capture force changes within 5 N of fingertip pressure, appropriate for precision grip activities. These experiments verified the feasibility of force sensing RF tags. However, limitations to this technique include the need for sensitive detecting antennas to measure back scatter. For this reason, we examined force sensor designs that were independent of RF signal. Optical force sensing is a method to detect fingertip pressure without electromagnetic interference. An optical force sensor layers PDMS membrane on SiO2 within an implantable chip (Figure 3) that could be implanted subdermally. At one end of the floor of the sensor, an internal 80 μm2 light-emitting diode (LED) emits light. The light is reflected by the internal ceiling of the chip that is constructed of PDMS in an inverse lenticular structure. Reflected light is detected by a photodiode at the opposite end of the sensor. The intervening SiO2 acts as an optical waveguide. In the absence of force (or compressing pressure), the waveguide allows reflected light to excite the photodiode with an efficient electric-to-optical conversion, a high sensitivity (0.02 kPa−1) and a pressure sensing resolution (38 mPa). When force is applied, the PDMS ceiling bows downward, opening light channels in the membrane. This allows light to escape, which in turn decreases the voltage at the photodiode monotonically, and yields a scaled readout.FIGURE 3: Optical force sensor design. A, Side view of optical sensor in absence of load. Directional path of light shown in yellow reflected from internal surface of PDMS ceiling. LED emitter located in lower left of sensor; photodiode (PD) located in lower right. B, Applied forces reduce light received by photodiode end. C, Diagram of optical force sensor circuit. D, Idealized relationship between applied force and photodiode voltageBoth scatter sensors and optical sensors achieved their desired engineering goals of converting force into measurable data. Neither system represented optimal solutions. In the case of scatter sensors, environmental noise may obscure the back-scatter energy detected by a horn antenna. In the case of optical sensors, an active circuit is required. On-going experiments aim to address these limitations by increasing the signal-to-noise ratio (RF sensors) and integrating rechargeable power (optical sensors). Beyond touch sensation, proprioception is a fundamental sensory modality that informs us about limb position. To restore proprioception across large joints, we developed a wireless electrogoniometer.28 Unlike other electrogoniometers that require strain gauges or power-hungry potentiometers, our system was designed to have very low power requirements (∼20 μW) both in terms of sensing and wireless data transmission. This was achieved using a pair of impulse-radio ultrawide band wireless smart sensor nodes interfacing with low-power 3-axis accelerometers through event-driven analog-to-digital converters. Electrogoniometers are designed to operate across large joints, such as the elbow, which are too large for strain sensors or other position sensors. On-going experiments aim to combine multiple sensor modalities in the same organism. Novel Central Nervous System Targets Our second aim is to identify optimal sensory encoding nodes along the neuraxis. Cortical encoding has been attempted for decades in animals, and recently in humans, with mixed results. It remains to be seen how well S1 ICMS will faithfully reproduce naturalistic perception. ICMS in other sensory areas, like primary visual cortex, generates phosphenes but not complex visual images.26 This may be due to the fact that cortical representations are distributed. Complex experiential phenomena, like rich somatosensory percepts, are therefore unlikely to be reproduced with focal stimulation without activation of a larger network. Upstream sensory circuits have To to this we developed the first chronic neural interface of the dorsal column nuclei to and stimulation in awake The a for sensory encoding. These nuclei on the dorsal surface of the and proprioceptive signals from primary (Figure from the high information to the for sensory the descending from that may sensory column nuclei interface. A, between and nuclei and in are readily with of the B, implanted in the of a at of in for several C, of of electrode in to studies of the were limited to or In our first of in were implanted with multi-electrode arrays to the feasibility of a chronic interface. Over several months, we that these arrays are and well in without data from implanted yielded a number of Over were The most was that over that are frequencies occurred with a in the we that could be over multiple in with chronic by the results of we designed a series of stimulation In experiments, we the at sensory encoding through at in a highly precise stimulation of the evoked responses in primary sensory stimulation evoked and field in the S1 (Figure which is to from sensory The induced for up to This finding may the of perceptual experiences primary that circuits between and S1 have a function for sensory To test perceptual thresholds of were on a detection and When stimuli were with to detect the electrical stimuli over rose to thresholds for are to cortical thresholds This that encoding experiments to characterize the efficacy of evoked Cortical responses to encoding. A, evoked responses to stimulation. to the which B, of frequencies stimulation. the stimulus at a well the stimulus feasibility of and encoding testing not from these experiments have for somatosensory currently will characterize responses and their to nodes including the and sensory Novel BCI Novel systems are to link peripheral sensor nodes and sensory encoding We developed a bidirectional brain–machine the as our third aim. This when links a suite of implantable and wearable peripheral sensor nodes with neural and electrodes (Figure the system and its nodes are to as a body area network. At the of the are wireless including a neural a neural a sensor and a The of a neural neural feature neural and associated The neural feature are for or field the system includes an neural energy and a detection with control is in the form of a that sensor data from peripheral sensors to desired patterns related to somatosensory cortex (Figure of brain–computer interface A, intersections between BCI systems and in the case of paralyzed or feedback control from nodes within in B, control loop integrating neural and stimulation in the flexibility to paralyzed or neural may be to or or stimulation with a voltage of Our the to current the that that neural and current current stimulation with a to a phase that neural However, changes due to during the Over millions of develop and in that the interface and To properly for this we a feedback that the phase when a point is detected (Figure The of this circuit is an error that error by the during stimulus the are the range, stimulation are as error that this method over that the system will have in experiments are to test this principle A, and of between phase and phase shown Idealized shown in shown in B, the on of point body area network requires real-time communication between the is with an impulse-radio band The and components to and between For clinical communication between nodes must be and operate within an of data The features an data of 2 in The error was over a of 3 of these are well within the desired for human moving toward human a number of must be of the system must be in must also be to To whether of the system was and effective at percepts, we designed in experiments on the the were to a to the by visual a pattern the is were to ICMS by the as a on the (Figure As the animal the stimulation As significantly in the presence of perception. When the was during was are able to systems to perception in a and effective In feasibility testing of novel systems. A, B, design is to in by ICMS as Idealized illustrated from with and optimal in presence of In our strategy to develop a sensor–brain interface system focuses on 3 aims: development of novel sensors, characterization of novel neural and development of autonomous body-area network. We have made in each of these areas, but substantial work We to our systems up to channel peripheral sensors and our In we our systems to animal models. It is our goal to this sensor brain interface with existing efferent systems to a bidirectional BCI to paralyzed This work was in by the National Science The have no or interest in of the or in this

Tetsuya Akagi, Shujiro Dohta, Yuji Kenmotsu, Feifei Zhao · 5 authors

Due to the ageing and the decreasing birth rate in Japanese society, an important problem of providing nursing care for the elderly has occurred. Therefore, it is strongly desired to develop a wearable actuator to use in nursing care or rehabilitation. The purpose of this study is to develop a high-power flexible actuator with a displacement sensor which can be used in supporting a bathing. In our previous study, we proposed and tested a rubber artificial muscle with the inner diameter sensor. The inner diameter sensor consists of two electric circuit boards with two photo reflectors. Two boards are bonded together to contract the inner diameter sensor. The sensor also has a doughnutshaped bulkhead to keep a seal. The sensor is inserted into the tube of the artificial muscle. The senor is set at the end of tube. This sensor can be expected to estimate the axial direction displacement of the rubber artificial muscle, because the relation between the inner diameter and the axial directional displacement of the muscle has a strong correlation. However, if the external bending force is applied to the end of the muscle, the inner diameter sensor cannot hold at the center position of the tube. Therefore, the sensor cannot measure the inner diameter exactly. In this study, the improvement of the inner diameter sensor was executed. The improved sensor has 4 photo reflectors on the two electric circuit boards to compensate the measuring error. The position control was also carried out by using the actuator with the built-in inner diameter sensor. As a result, the axial direction displacement of the muscle could be estimated well by the tested inner diameter sensor, and a relatively good position control performance was obtained.