Rahul R. Kaliki

User Training for Pattern Recognition-Based Myoelectric Prostheses: Improving Phantom Limb Movement Consistency and Distinguishability

We assessed the ability of four transradial amputees to control a virtual prosthesis capable of nine classes of movement both before and after a two-week training period. Subjects attended eight one-on-one training sessions that focused on improving the consistency and distinguishability of their hand and wrist movements using visual biofeedback from a virtual prosthesis. The virtual environment facilitated the precise quantification of three prosthesis control measures. During a final evaluation, the subject population saw an average increase in movement completion percentage from 70.8% to 99.0%, an average improvement in normalized movement completion time from 1.47 to 1.13, and an average increase in movement classifier accuracy from 77.5% to 94.4% (p<;0.001). Additionally, all four subjects were reevaluated after eight elapsed hours without retraining the classifier, and all subjects demonstrated minimal decreases in performance. Our analysis of the underlying sources of improvement for each subject examined the sizes and separation of high-dimensional data clusters and revealed that each subject formed a unique and effective strategy for improving the consistency and/or distinguishability of his or her phantom limb movements. This is the first longitudinal study designed to examine the effects of user training in the implementation of pattern recognition-based myoelectric prostheses.

Neuromimetic Event-Based Detection for Closed-Loop Tactile Feedback Control of Upper Limb Prostheses

Upper limb amputees lack the valuable tactile sensing that helps provide context about the surrounding environment. Here, we utilize tactile information to provide active touch feedback to a prosthetic hand. First, we developed fingertip tactile sensors for producing biomimetic spiking responses for monitoring contact, release, and slip of an object grasped by a prosthetic hand. We convert the sensor output into pulses, mimicking the rapid and slowly adapting spiking responses of receptor afferents found in the human body. Second, we designed and implemented two neuromimetic event-based algorithms, Compliant Grasping and Slip Prevention, on a prosthesis to create a local closed-loop tactile feedback control system (i.e., tactile information is sent to the prosthesis). Grasping experiments were designed to assess the benefit of this biologically inspired neuromimetic tactile feedback to a prosthesis. Results from able-bodied and amputee subjects show the average number of objects that broke or slipped during grasping decreased by over 50 percent and the average time to complete a grasping task decreased by at least 10 percent for most trials when comparing neuromimetic tactile feedback with no feedback on a prosthesis. Our neuromimetic method of closed-loop tactile sensing is a novel approach to improving the function of upper limb prostheses.

Tactile feedback in upper limb prosthetic devices using flexible textile force sensors

Many upper limb amputees are faced with the difficult challenge of using a prosthesis that lacks tactile sensing. State of the art research caliber prosthetic hands are often equipped with sophisticated sensors that provide valuable information regarding the prosthesis and its surrounding environment. Unfortunately, most commercial prosthetic hands do not contain any tactile sensing capabilities. In this paper, a textile based tactile sensor system was designed, built, and evaluated for use with upper limb prosthetic devices. Despite its simplicity, we demonstrate the ability of the sensors to determine object contact and perturbations due to slip during a grasping task with a prosthetic hand. This suggests the use of low-cost, customizable, textile sensors as part of a closed-loop tactile feedback system for monitoring grasping forces specifically in an upper limb prosthetic device.

Radio frequency identification — An innovative solution to guide dexterous prosthetic hands

Dexterous manipulation of a multi-fingered prosthetic hand requires far more cognitive effort compared to typical 1 degree of freedom hands, which hinders their acceptance clinically. This paper presents a Myoelectrically-Operated Radio Frequency Identification (RFID) Prosthetic Hand (MORPH); an implementation of RFID with a myoelectric prosthetic hand as a means to amplify the controllable degrees of freedom. Contextual information from an object equipped with an RFID tag allows automatic preshaping along with dexterous control in an attempt to reduce the cognitive effort required to operate the terminal device. The myoelectric-RFID hybrid has been demonstrated in a proof-of-concept case study where an amputee was fitted with the device and subjected to activities adapted from the Jebsen Hand Function Test and the Smith Hand Function Evaluation with RFID-tagged and untagged items. Evaluation tests revealed that the MORPH system performed significantly better in 4 of the 8 tasks, and comparable to the control in the remainder.

Prosthesis with neuromorphic multilayered e-dermis perceives touch and pain

A multilayered e-dermis allows a prosthesis and an amputee to perceive a range of innocuous and noxious tactile stimuli. The human body is a template for many state-of-the-art prosthetic devices and sensors. Perceptions of touch and pain are fundamental components of our daily lives that convey valuable information about our environment while also providing an element of protection from damage to our bodies. Advances in prosthesis designs and control mechanisms can aid an amputee’s ability to regain lost function but often lack meaningful tactile feedback or perception. Through transcutaneous electrical nerve stimulation (TENS) with an amputee, we discovered and quantified stimulation parameters to elicit innocuous (nonpainful) and noxious (painful) tactile perceptions in the phantom hand. Electroencephalography (EEG) activity in somatosensory regions confirms phantom hand activation during stimulation. We invented a multilayered electronic dermis (e-dermis) with properties based on the behavior of mechanoreceptors and nociceptors to provide neuromorphic tactile information to an amputee. Our biologically inspired e-dermis enables a prosthesis and its user to perceive a continuous spectrum from innocuous to noxious touch through a neuromorphic interface that produces receptor-like spiking neural activity. In a pain detection task (PDT), we show the ability of the prosthesis and amputee to differentiate nonpainful or painful tactile stimuli using sensory feedback and a pain reflex feedback control system. In this work, an amputee can use perceptions of touch and pain to discriminate object curvature, including sharpness. This work demonstrates possibilities for creating a more natural sensation spanning a range of tactile stimuli for prosthetic hands.

Limb Position Tolerant Pattern Recognition for Myoelectric Prosthesis Control with Adaptive Sparse Representations From Extreme Learning

Myoelectric signals can be used to predict the intended movements of an amputee for prosthesis control. However, untrained effects like limb position changes influence myoelectric signal characteristics, hindering the ability of pattern recognition algorithms to discriminate among motion classes. Despite frequent and long training sessions, these deleterious conditional influences may result in poor performance and device abandonment. <italic>Goal:</italic> We present a robust sparsity-based adaptive classification method that is significantly less sensitive to signal deviations resulting from untrained conditions. <italic>Methods:</italic> We compare this approach in the offline and online contexts of untrained upper-limb positions for amputee and able-bodied subjects to demonstrate its robustness compared against other myoelectric classification methods. <italic>Results:</italic> We report significant performance improvements (<inline-formula><tex-math notation=LaTeX>

Utilizing tactile feedback for biomimetic grasping control in upper limb prostheses

p<0.001

Multi-Position Training Improves Robustness of Pattern Recognition and Reduces Limb-Position Effect in Prosthetic Control

</tex-math></inline-formula>) in untrained limb positions across all subject groups. <italic>Significance:</italic> The robustness of our suggested approach helps to ensure better untrained condition performance from fewer training conditions. <italic>Conclusions:</italic> This method of prosthesis control has the potential to deliver real-world clinical benefits to amputees: better condition-tolerant performance, reduced training burden in terms of frequency and duration, and increased adoption of myoelectric prostheses.

Limb-position robust classification of myoelectric signals for prosthesis control using sparse representations

A biomimetic system for enhancing the control and reliability of grasping with prosthetic hands was designed and experimentally evaluated. Barometric pressure sensors as well as a force-sensitive resistor (FSR) were placed on a prosthetic hand to provide valuable tactile feedback. Contact and slip detection grip control algorithms were developed to interpret force signals for enhancing stable grasping. Recent advances in radio-frequency identification (RFID) technology enable the amputee to select between grip control strategies based on the desired object to be grasped. Experimental results indicate that the control algorithms are capable of utilizing real-time force responses to detect object contact as well as slip. By allowing the user to act as a high-level controller with RFID technology, a multi-faceted low-level controller that responds to tactile feedback can be developed for enhancing grasping functionality in prosthetic hands.

Live demonstration: Prosthesis grip force modulation using neuromorphic tactile sensing

Introduction Electromyogram (EMG)-based pattern recognition control of prosthetic limbs is the current state of the art. However, these systems commonly fail when the user attempts to use the limb in a different position from which it was trained, resulting in significantly reduced functionality. Robust models for decoding EMG signals, accounting for specific changes that occur with positional variation, are needed to reduce this negative effect. Methods Ten able-bodied participants and two participants with transradial amputation were included in the study. Participants were fitted with surface EMG electrodes as well as a network of inertial measurement units (IMUs) to monitor limb position during tasks. Positional covariates including elbow angle, hand height, and forearm angle were analyzed for impact on EMG signal features to drive the generation of unique LDA classifier algorithms. Offline analysis of classification error for each control scheme was then completed. Results Elbow angle demonstrated the strongest impact on the EMG signal. Hand height also demonstrated a consistent increase in EMG signal with increasing height. Incorporating these specific covariates into classifier algorithms improved performance compared with classifiers trained in the conventional fashion (single-position EMG). However, able-bodied participants demonstrated lowest classification error when data from random-training positions were incorporated (10.3% vs. 17.2% single position, P < 0.001). These results were even more dramatic in participants with amputation (with five training repetitions: 7.14% vs. 32.08%, P < 0.001). Performance differences between single-position and random-position training for individuals with amputations were significantly larger when the user was wearing his/her prosthesis than otherwise. Conclusions Incorporating position-specific covariates into myoelectric classification algorithms can dramatically improve robustness and classification accuracy when using the prosthesis in the users entire workspace. In single-position training paradigms, classification error rates were 39.22% and 32.18%, respectively, for two participants with amputation and resulted in unusable classifiers. Conversely, classification errors were at 10% for able-bodied and near 7% for participants with amputation when at least five training repetitions were used to train either a random position or position-specific classifier. As position-tracking hardware becomes smaller and can be implemented into socket designs, incorporating this information into classifier algorithms can dramatically reduce the limb-position effect. Current users can experience reduction of the limb-position effect through training in multiple random positions.