Hand movement classification based on surface electromyography (sEMG) pattern recognition is a promising approach for upper limb neuroprosthetic control. However, maintaining day-to-day performance is challenged by the non-stationary nature of sEMG in real-life operation. In this study, we propose a self-recalibrating classifier that can be automatically updated to maintain a stable performance over time without the need for user retraining. Our classifier is based on convolutional neural network (CNN) using short latency dimension-reduced sEMG spectrograms as inputs. The pretrained classifier is recalibrated routinely using a corrected version of the prediction results from recent testing sessions. Our proposed system was evaluated with the NinaPro database comprising of hand movement data of 40 intact and 11 amputee subjects. Our system was able to achieve ~10.18% (intact, 50 movement types) and ~2.99% (amputee, 10 movement types) increase in classification accuracy averaged over five testing sessions with respect to the unrecalibrated classifier. When compared with a support vector machine (SVM) classifier, our CNN-based system consistently showed higher absolute performance and larger improvement as well as more efficient training. These results suggest that the proposed system can be a useful tool to facilitate long-term adoption of prosthetics for amputees in real-life applications.

A high performance electrocardiogram (ECG) based arrhythmic beats classification system is presented in this paper. The classifier was designed based on convolutional neural network (CNN). Single channel ECG signal was segmented into heartbeats in accordance with the changing heartbeat rate. The beats were transformed into dual beat coupling matrix as 2-D inputs to the CNN classifier, which captured both the beat morphology and beat-to-beat correlation in ECG. A systematic training beat selection procedure was also proposed which automatically include the most representative beats into the training set to improve classification performance. The classification system was evaluated for detection of supraventricular ectopic beats (SVEB or S beats) and ventricular ectopic beats (VEB or V beats) using the MIT-BIH arrhythmia database. Our proposed method has demonstrated superior performance than several state-of-the-art detectors. In particular, our proposed CNN system has improved sensitivity and positive predictive rate for S beats by more than 12.2% and 11.9% respectively over these top performing algorithms. Our proposed CNN classifier with an automatic training beats selection process has shown to outperform the previous methods. The classifier is also a personalized one by combining training set from a common pool and a subject-specific set of ECG data. Our proposed system provides a reliable and fully automatic tool for detection of arrhythmia heartbeat without the need for manual feature extraction or expert assistant. It can potentially be implemented on portable device for long-term monitoring of cardiac arrhythmia.

Internal models and adaptive controls are empirical and mathematical paradigms that have evolved separately to describe learning control processes in brain systems and engineering systems, respectively. This paper presents a comprehensive appraisal of the correlation between these paradigms with a view to forging a unified theoretical framework that may benefit both disciplines. It is suggested that the classic equilibrium-point theory of impedance control of arm movement is analogous to continuous gain-scheduling or high-gain adaptive control within or across movement trials, respectively, and that the recently proposed inverse internal model is akin to adaptive sliding control originally for robotic manipulator applications. Modular internal models' architecture for multiple motor tasks is a form of multi-model adaptive control. Stochastic methods, such as generalized predictive control, reinforcement learning, Bayesian learning and Hebbian feedback covariance learning, are reviewed and their possible relevance to motor control is discussed. Possible applicability of a Luenberger observer and an extended Kalman filter to state estimation problems-such as sensorimotor prediction or the resolution of vestibular sensory ambiguity-is also discussed. The important role played by vestibular system identification in postural control suggests an indirect adaptive control scheme whereby system states or parameters are explicitly estimated prior to the implementation of control. This interdisciplinary framework should facilitate the experimental elucidation of the mechanisms of internal models in sensorimotor systems and the reverse engineering of such neural mechanisms into novel brain-inspired adaptive control paradigms in future.

Quantitative and dynamic analyses of immune cell secretory cytokines are essential for precise determination and characterization of the “immune phenotype” of patients for clinical diagnosis and treatment of immune-related diseases. Although multiple methods including the enzyme-linked immunosorbent assay (ELISA) have been applied for cytokine detection, such measurements remain very challenging in real-time, high-throughput, and high-sensitivity immune cell analysis. In this paper, we report a highly integrated microfluidic device that allows for on-chip isolation, culture, and stimulation, as well as sensitive and dynamic cytokine profiling of immune cells. Such a microfluidic sensing chip is integrated with cytometric fluorescent microbeads for real-time and multiplexed monitoring of immune cell cytokine secretion dynamics, consuming a relatively small extracted sample volume (160 nl) without interrupting the immune cell culture. Furthermore, it is integrated with a Taylor dispersion-based mixing unit in each detection chamber that shortens the immunoassay period down to less than 30 minutes. We demonstrate the profiling of multiple pro-inflammatory cytokine secretions (e.g. interleukin-6, interleukin-8, and tumor necrosis factors) of human peripheral blood mononuclear cells (PBMCs) with a sensitivity of 20 pg ml⁻¹ and a sample volume of 160 nl per detection. Further applications of this automated, rapid, and high-throughput microfluidic immunophenotyping platform can help unleash the mechanisms of systemic immune responses, and enable efficient assessments of the pathologic immune status for clinical diagnosis and immune therapy.