Functionally Adaptive Myosite Selection using conformable HD sEMG electrodes for movement-pattern classification

Myoelectric prosthesis systems currently use advanced control schemes such as pattern recognition to classify muscle activation signals as intended movement classes. For this classification, generally, untargeted, equally spaced electrodes placed circumferentially around the muscle belly of the forearm, are used for acquisition of surface electromyogram (sEMG) for tran-radial amputee subjects. We propose a novel system, consisting of a hardware and software component. We built the hardware component in the form of a flexible and conformable high-density sEMG array. We tested the signal quality and electrode-skin contact characteristics to demonstrate the quality and conformability of the electrode array. We built the software component of the system based on separability criteria. This proposed system is called functionally adaptive myoelectrode site (myosite) selection (FAMS) and is to identify optimal myosites for pattern recognition. Our study investigates the effects of optimal myosite selection with increase in the number of movement classes and inclusion of fine motor movements. We also used myosite selection from current clinical and research procedures and compared the performances of FAMS to existing systems. Results of our study indicate that using optimal myosites selected using FAMS for movement pattern classification improves performance and this becomes more evident with increase in the number of selected myosites. The significance of using optimal myosites increases when more movement classes are included. This work also shows that the optimal myosites change spatially with the type and number of movement classes included for classification. We then explored other future applications of 1) FAMS in temporal adaptations to help prosthetic users begin early use of pattern recognition based prosthesis system and 2) extending FAMS to site selection for direct control so as to make FAMS a universal electrode interface for myoelectric prosthesis. Preliminary study results in these areas are presented in this work. The electrode design was further improved to fit inside of a prosthesis. This system has the capabilities to become an off-the-shelf universal system that can be prescribed for any myoelectric prosthesis user irrespective of their level of amputation and experience with using a myoelectric prosthesis. This system can reduce pre-prosthetic training time and facilitate early fitting. This system also removes the need for refitting every time the user changes the movement classes controlled by the prosthesis

Multi-frequency segmental bio-impedance device:design, development and applications

Bio-impedance analysis (BIA) provides a rapid, non-invasive technique for body composition estimation. BIA offers a convenient alternative to standard techniques such as MRI, CT scan or DEXA scan for selected types of body composition analysis. The accuracy of BIA is limited because it is an indirect method of composition analysis. It relies on linear relationships between measured impedance and morphological parameters such as height and weight to derive estimates. To overcome these underlying limitations of BIA, a multi-frequency segmental bio-impedance device was constructed through a series of iterative enhancements and improvements of existing BIA instrumentation. Key features of the design included an easy to construct current-source and compact PCB design. The final device was trialled with 22 human volunteers and measured impedance was compared against body composition estimates obtained by DEXA scan. This enabled the development of newer techniques to make BIA predictions. To add a ‘visual aspect’ to BIA, volunteers were scanned in 3D using an inexpensive scattered light gadget (Xbox Kinect controller) and 3D volumes of their limbs were compared with BIA measurements to further improve BIA predictions. A three-stage digital filtering scheme was also implemented to enable extraction of heart-rate data from recorded bio-electrical signals. Additionally modifications have been introduced to measure change in bio-impedance with motion, this could be adapted to further improve accuracy and veracity for limb composition analysis. The findings in this thesis aim to give new direction to the prediction of body composition using BIA. The design development and refinement applied to BIA in this research programme suggest new opportunities to enhance the accuracy and clinical utility of BIA for the prediction of body composition analysis. In particular, the use of bio-impedance to predict limb volumes which would provide an additional metric for body composition measurement and help distinguish between fat and muscle content

Intraneural stimulation elicits discrimination of textural features by artificial fingertip in intact and amputee humans.

Restoration of touch after hand amputation is a desirable feature of ideal prostheses. Here, we show that texture discrimination can be artificially provided in human subjects by implementing a neuromorphic real-time mechano-neuro-transduction (MNT), which emulates to some extent the firing dynamics of SA1 cutaneous afferents. The MNT process was used to modulate the temporal pattern of electrical spikes delivered to the human median nerve via percutaneous microstimulation in four intact subjects and via implanted intrafascicular stimulation in one transradial amputee. Both approaches allowed the subjects to reliably discriminate spatial coarseness of surfaces as confirmed also by a hybrid neural model of the median nerve. Moreover, MNT-evoked EEG activity showed physiologically plausible responses that were superimposable in time and topography to the ones elicited by a natural mechanical tactile stimulation. These findings can open up novel opportunities for sensory restoration in the next generation of neuro-prosthetic hands

Restoring Upper Extremity Mobility through Functional Neuromuscular Stimulation using Macro Sieve Electrodes

The last decade has seen the advent of brain computer interfaces able to extract precise motor intentions from cortical activity of human subjects. It is possible to convert captured motor intentions into movement through coordinated, artificially induced, neuromuscular stimulation using peripheral nerve interfaces. Our lab has developed and tested a new type of peripheral nerve electrode called the Macro-Sieve electrode which exhibits excellent chronic stability and recruitment selectivity. Work presented in this thesis uses computational modeling to study the interaction between Macro-Sieve electrodes and regenerated peripheral nerves. It provides a detailed understanding of how regenerated fibers, both on an individual level and on a population level respond differently to functional electrical stimulation compared to non-disrupted axons. Despite significant efforts devoted to developing novel regenerative peripheral interfaces, the degree of spatial clustering between functionally related fibers in regenerated nerves is poorly understood. In this thesis, bioelectrical modeling is also used to predict the degree of topographical organization in regenerated nerve trunks. In addition, theoretical limits of the recruitment selectivity of the device is explored and a set of optimal stimulation paradigms used to selectively activate fibers in different regions of the nerve are determined. Finally, the bioelectrical model of the interface/nerve is integrated with a biomechanical model of the macaque upper limb to study the feasibility of using macro-sieve electrodes to achieve upper limb mobilization

Interoperability of fingerprint sensors and matching algorithms

Biometric systems are widely deployed in governmental, military and commercial/civilian applications. There are a multitude of sensors and matching algorithms available from different vendors. This creates a competitive market for these products, which is good for the consumers but emphasizes the importance of interoperability. In fingerprint recognition, interoperability is the ability of a system to work with a diverse set of fingerprint devices. Variations induced by fingerprint sensors include image resolution, scanning area, gray levels, etc. Such variations can impact the quality of the extracted features, and cross-device matching performance. This is true even when dealing with fingerprint sensors of the same sensing technology. In this thesis, we perform a large-scale empirical study of the status of interoperability between fingerprint sensors and assess the performance consequence when interoperability is lacking. Additionally we develop a method to increase interoperability in fingerprint-based recognition systems deploying optical fingerprint sensors. A set of features to measure differences in fingerprint acquisition is designed and evaluated. Finally, different fusion schemes based on machine learning are tested end evaluated in order to exploit the designed set of features. Experimental results show that the proposed approach is able to reduce cross-device match error rates by a significant margin

Relating forearm muscle electrical activity to finger forces

The electromyogram (EMG) signal is desired to be used as a control signal for applications such as multifunction prostheses, wheelchair navigation, gait generation, grasping control, virtual keyboards, and gesture-based interfaces [25]. Several research studies have attempted to relate the electromyogram (EMG) activity of the forearm muscles to the mechanical activity of the wrist, hand and/or fingers [41], [42], [43]. A primary interest is for EMG control of powered upper-limb prostheses and rehabilitation orthotics. Existing commercial EMG-controlled devices are limited to rudimentary control capabilities of either discrete states (e.g. hand close/open), or one degree of freedom proportional control [4], [36]. Classification schemes for discriminating between hand/wrist functions and individual finger movements have demonstrated accuracy up to 95% [38], [39], [29]. These methods may provide for increased amputee function, though continuous control of movement is not generally achieved. This thesis considered proportional control via EMG-based estimation of finger forces with the goal of identifying whether multiple degrees of freedom of proportional control information are available from the surface EMG of the forearm. Electromyogram (EMG) activity from the extensor and flexor muscles of the forearm was sensed with bipolar surface electrodes and related to the force produced at the four fingertips during constant-posture, slowly force-varying contractions from 20 healthy subjects. The contractions ranged between 30% maximum voluntary contractions (MVC) extension and 30% MVC flexion. EMG amplitude sampling rate, least squares regularization, linear vs. nonlinear models and number of electrodes used in the system identification were studied. Results are supportive that multiple degrees of freedom of proportional control information are available from the surface EMG of the forearm, at least in healthy subjects. An EMG amplitude sampling frequency of 4.096 Hz was found to produce models which allowed for good EMG amplitude estimates. Least squares regularization with a pseudo-inverse tolerance of 0.055 resulted in significant improvement in modeling results, with an average error of 4.69% MVC-6.59% MVC (maximum voluntary contraction). Increasing polynomial order did not significantly improve modeling results. Results from smaller electrode arrays remained fairly good with as few as six electrodes, with the average %MVC error ranging from 5.13%-7.01% across the four fingers. This study also identified challenges in the current experimental study design and subsequent system identification when EMG-force modeling is performed with four fingers simultaneously. Methods to compensate for these issues have been proposed in this thesis

Corrosion Monitoring Based on Recurrence Quantification Analysis of Electrochemical Noise and Machine Learning Methods

Although electrochemical noise (EN) has been studied for decades, the optimal approach for the analysis of EN data remains uncertain. This research innovatively combined the use of recurrence quantification analysis of electrochemical noise data and machine learning methods to develop models for corrosion monitoring and corrosion type identification. Case studies demonstrate that the proposed methodologies are potentially feasible for the development of online corrosion monitoring programs