Topology-based goodness-of-fit tests for sliced spatial data

In materials science and many other application domains, 3D information can often only be obtained by extrapolating from 2D slices. In topological data analysis, persistence vineyards have emerged as a powerful tool to take into account topological features stretching over several slices. It is illustrated how persistence vineyards can be used to design rigorous statistical hypothesis tests for 3D microstructure models based on data from 2D slices. More precisely, by establishing the asymptotic normality of suitable longitudinal and cross-sectional summary statistics, goodness-of-fit tests that become asymptotically exact in large sampling windows are devised. The testing methodology is illustrated through a detailed simulation study and a prototypical example from materials science is provided

Optimal Spatial Sensor Design for Magnetic Tracking in a Myokinetic Control Interface.

Abstract Background and Objectives Magnetic tracking involves the use of magnetic sensors to localize one or more magnetic objectives, in those applications in which a free line-of-sight between them and the operator is hampered. We applied this concept to prosthetic hands, which could be controlled by tracking permanent magnets implanted in the forearm muscles of amputees (the myokinetic control interface). Concerning the system design, the definition of a sensor distribution which maximizes the information, while minimizing the computational cost of localization, is still an open problem. We present a simple yet effective strategy to define an optimal sensor set for tracking multiple magnets, which we called the Peaks method. Methods We simulated a proximal amputation using a 3D CAD model of a human forearm, and the implantation of 11 magnets in the residual muscles. The Peaks method was applied to select a subset of sensors from an initial grid of 480 elements. The approach involves setting an appropriate threshold to select those sensors associated with the peaks in the magnetic flux density and its gradient distributions. Selected sensors were used to track the magnets during muscle contraction. For validating our strategy, an alternative method based on state-of-the-art solutions was implemented. We finally proposed a calibration phase to customize the sensor distribution on the specific patient's anatomy. Results 80 sensors were selected with the Peaks method, and 101 with the alternative one. A localization accuracy below 0.22 mm and 1.86° for position and orientation, respectively, was always achieved. Unlike alternative methods from the literature, neither iterative or analytical solution, nor a-priori knowledge on the magnet positions or trajectories were required, and yet the outcomes achieved with the two strategies proved statistically comparable. The calibration phase proved useful to adapt the sensors to the patient's stump and to increase the signal-to-noise ratio against intrinsic noise. Conclusions We demonstrated an efficient and general solution for solving the design optimization problem (i.e. identifying an optimal sensor set) and reducing the computational cost of localization. The optimal sensor distribution mirrors the field shape traced by the magnets on the sensing surface, being an intuitive and fast way of achieving the same results of more complex and application-specific methods. Several applications in the (bio)medical field involving magnetic tracking will benefit from the outcomes of this work

The SmartHand transradial prosthesis

<p>Abstract</p> <p>Background</p> <p>Prosthetic components and control interfaces for upper limb amputees have barely changed in the past 40 years. Many transradial prostheses have been developed in the past, nonetheless most of them would be inappropriate if/when a large bandwidth human-machine interface for control and perception would be available, due to either their limited (or inexistent) sensorization or limited dexterity. <it>SmartHand </it>tackles this issue as is meant to be clinically experimented in amputees employing different neuro-interfaces, in order to investigate their effectiveness. This paper presents the design and on bench evaluation of the SmartHand.</p> <p>Methods</p> <p>SmartHand design was bio-inspired in terms of its physical appearance, kinematics, sensorization, and its multilevel control system. Underactuated fingers and differential mechanisms were designed and exploited in order to fit all mechatronic components in the size and weight of a natural human hand. Its sensory system was designed with the aim of delivering significant afferent information to the user through adequate interfaces.</p> <p>Results</p> <p>SmartHand is a five fingered self-contained robotic hand, with 16 degrees of freedom, actuated by 4 motors. It integrates a bio-inspired sensory system composed of 40 proprioceptive and exteroceptive sensors and a customized embedded controller both employed for implementing automatic grasp control and for potentially delivering sensory feedback to the amputee. It is able to perform everyday grasps, count and independently point the index. The weight (530 g) and speed (closing time: 1.5 seconds) are comparable to actual commercial prostheses. It is able to lift a 10 kg suitcase; slippage tests showed that within particular friction and geometric conditions the hand is able to stably grasp up to 3.6 kg cylindrical objects.</p> <p>Conclusions</p> <p>Due to its unique embedded features and human-size, the SmartHand holds the promise to be experimentally fitted on transradial amputees and employed as a bi-directional instrument for investigating -during realistic experiments- different interfaces, control and feedback strategies in neuro-engineering studies.</p

Development of an Embedded Myokinetic Prosthetic Hand Controller

The quest for an intuitive and physiologically appropriate human machine interface for the control of dexterous prostheses is far from being completed. In the last decade, much effort has been dedicated to explore innovative control strategies based on the electrical signals generated by the muscles during contraction. In contrast, a novel approach, dubbed myokinetic interface, derives the control signals from the localization of multiple magnetic markers (MMs) directly implanted into the residual muscles of the amputee. Building on this idea, here we present an embedded system based on 32 magnetic field sensors and a real time computation platform. We demonstrate that the platform can simultaneously localize in real-time up to five MMs in an anatomically relevant workspace. The system proved highly linear (R2 = 0.99) and precise (1% repeatability), yet exhibiting short computation times (4 ms) and limited cross talk errors (10% the mean stroke of the magnets). Compared to a previous PC implementation, the system exhibited similar precision and accuracy, while being ~75% faster. These results proved for the first time the viability of using an embedded system for magnet localization. They also suggest that, by using an adequate number of sensors, it is possible to increase the number of simultaneously tracked MMs while introducing delays that are not perceivable by the human operator. This could allow to control more degrees of freedom than those controllable with current technologies

Abstract and proportional myoelectric control for multi-fingered hand prostheses

Powered hand prostheses with many degrees of freedom are moving from research into the market for prosthetics. In order to make use of the prostheses’ full functionality, it is essential to study efficient ways of high dimensional myoelectric control. Human subjects can rapidly learn to employ electromyographic (EMG) activity of several hand and arm muscles to control the position of a cursor on a computer screen, even if the muscle-cursor map contradicts directions in which the muscles would act naturally. But can a similar control scheme be translated into real-time operation of a dexterous robotic hand? We found that despite different degrees of freedom in the effector output, the learning process for controlling a robotic hand was surprisingly similar to that for a virtual two-dimensional cursor. Control signals were derived from the EMG in two different ways, with a linear and a Bayesian filter, to test how stable user intentions could be conveyed through them. Our analysis indicates that without visual feedback, control accuracy benefits from filters that reject high EMG amplitudes. In summary, we conclude that findings on myoelectric control principles, studied in abstract, virtual tasks can be transferred to real-life prosthetic applications. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10439-013-0876-5) contains supplementary material, which is available to authorized users

Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals

We present a novel computational technique intended for the robust and adaptable control of a multifunctional prosthetic hand using multichannel surface electromyography. The initial processing of the input data was oriented towards extracting relevant time domain features of the EMG signal. Following the feature calculation, a piecewise modeling of the multidimensional EMG feature dynamics using vector autoregressive models was performed. The next step included the implementation of hierarchical hidden semi-Markov models to capture transitions between piecewise segments of movements and between different movements. Lastly, inversion of the model using an approximate Bayesian inference scheme served as the classifier. The effectiveness of the novel algorithms was assessed versus methods commonly used for real-time classification of EMGs in a prosthesis control application. The obtained results show that using hidden semi-Markov models as the top layer, instead of the hidden Markov models, ranks top in all the relevant metrics among the tested combinations. The choice of the presented methodology for the control of prosthetic hand is also supported by the equal or lower computational complexity required, compared to other algorithms, which enables the implementation on low-power microcontrollers, and the ability to adapt to user preferences of executing individual movements during activities of daily living

Discrete vibro-tactile feedback prevents object slippage in hand prostheses more intuitively than other modalities

In the case of a hand amputation, the affected can use myoelectric prostheses to substitute the missing limb and regain motor functionality. Unfortunately, these prostheses do not restore sensory feedback, thus users are forced to rely on vision to avoid object slippage. This is cognitively taxing, as it requires continuous attention to the task. Thus, providing functionally effective sensory feedback is pivotal to reduce the occurrence of slip events and reduce the users’ cognitive burden. However, only a few studies investigated which kind of feedback is the most effective for this purpose, mostly using unrealistic experimental scenarios. Here we attempt a more realistic simulation of involuntary hand opening and subsequent recovery of a stable grasp of the slipping object using a robotic hand operated by the subjects through a standard myoelectric control interface. We compared three stimulation modalities (vision, continuous grip force feedback and discrete slip feedback) and found that the discrete feedback allowed subjects to have higher success rates (close to 100%) in terms of objects recovered from slippage, basically requiring no learning. These results suggest that this simple yet effective feedback can be used to reduce grasp failures in prosthetic users, increasing their confidence in the device

The SSSA-MyHand: a dexterous lightweight myoelectric hand prosthesis

The replacement of a missing hand by a prosthesis is one of the most fascinating challenges in rehabilitation engineering. State of art prostheses are curtailed by the physical features of the hand, like poor functionality and excessive weight. Here we present a new multi-grasp hand aimed at overcoming such limitations. The SSSA-MyHand builds around a novel transmission mechanism that implements a semi-independent actuation of the abduction/adduction of the thumb and of the flexion/extension of the index, by means of a single actuator. Thus, with only three electric motors the hand is capable to perform most of the grasps and gestures useful in activities of daily living, akin commercial prostheses with up to six actuators, albeit it is as lightweight as conventional 1-Degrees of Freedom prostheses. The hand integrates position and force sensors and an embedded controller that implements automatic grasps and allows inter-operability with different human-machine interfaces. We present the requirements, the design rationale of the first prototype and the evaluation of its performance. The weight (478 g), force (31 N maximum force at the thumb fingertip) and speed of the hand (closing time: <370 ms), make this new design an interesting alternative to clinically available multi-grasp prostheses

A database of multi-channel intramuscular electromyogram signals during isometric hand muscles contractions.

Hand movement is controlled by a large number of muscles acting on multiple joints in the hand and forearm. In a forearm amputee the control of a hand prosthesis is traditionally depending on electromyography from the remaining forearm muscles. Technical improvements have made it possible to safely and routinely implant electrodes inside the muscles and record high-quality signals from individual muscles. In this study, we present a database of intramuscular EMG signals recorded with fine-wire electrodes alongside recordings of hand forces in an isometric setup and with the addition of spike-sorted metadata. Six forearm muscles were recorded from twelve able-bodied subjects and nine forearm muscles from two subjects. The fully automated recording protocol, based on command cues, comprised a variety of hand movements, including some requiring slowly increasing/decreasing force. The recorded data can be used to develop and test algorithms for control of a prosthetic hand. Assessment of the signals was done in both quantitative and qualitative manners