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Tremor is the most common movement disorder and differs from other disorders by its repetitive, stereotyped movements, with regular frequency and amplitude. The three most frequent pathological forms of it are the essential tremor (ET), the Parkinson's disease (PD) tremor, and the enhanced physiological tremor. The ET and PD tremor affect the older population mostly. Although there are cases of tremor reported since ancient times, there is currently no consensus about its causes or about its main differential characteristics. In this article, we present a review of the methods more frequently used in measurement and analysis of tremor and the difficulties encountered in the research for the identification of methodologies that allow a significant advance in the study of tremor.

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% and the average time to complete a grasping task decreased by at least 10% 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.

We propose a new method for detecting the onset of the stretch reflex response for assessment of spasticity based on the Tonic Stretch Reflex Threshold (TSRT). Our strategy relies on a three-stage approach to detect the onset of the reflex EMG activity: (i) Reduction of baseline activity by means of Empirical Mode Decomposition; (ii) Extraction of the complex envelope of the EMG signal by means of Hilbert Transform (HT) and; iii) A double threshold decision rule. Simulated and real EMG data were used to evaluate and compare our method (TSRT-EHD) against three other popular methods described in the literature to assess TSRT ('Kim', 'Ferreira' and 'Blanchette'). Four different groups of signals containing simulated evoked stretch reflex EMG activities were generated: groups A and B without spontaneous EMG activity at rest and signal-to-noise ratio (SNR) of 10dB and 20dB respectively; groups C and D with spontaneous EMG activity at rest, as observed frequently in spastic muscles, and SNR of 10dB and 20dB respectively. The results with simulated data showed a significantly higher accuracy of TSRT-EHD for detecting the onset of the reflex EMG activity in groups C and D when compared to the other methods. Analyses using real data from five post stroke spastic subjects demonstrated that the TSRTs generated by each method were dramatically different from one another. Nevertheless, only TSRT-EHD provided valid measures across all subjects.

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 user's 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.

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