Raman Spectroscopy as a PAT-Tool for Film-Coating Processes: In-Line Predictions Using one PLS Model for Different Cores.

Although Raman spectroscopy has been described as a potential process analytical technique for tablet coating, it has rarely been transferred from academic studies to commercial manufacturing applications. The reasons for this are probably not only the high level of process understanding and experience with multivariate data analysis required, but also the product-dependent elaborate model-building. Hence, this study represents a feasibility study to investigate, whether subtraction of core spectra is a suitable approach to generate versatile models for one specific coating that can be applied on a multitude of different tablet cores. Raman spectroscopy was used to predict the application of coatings on three different tablet cores using PLS regression. The obtained spectra were preprocessed, and differential spectra were calculated by subtraction of the core spectrum from each inline spectrum. Normalization ensured comparability between the spectral data of the different cores. It was shown that in general it is possible to build models for a specific coating suspension that can predict the application of this suspension on different cores. In the presence of a strong Raman marker (TiO2), promising results were obtained. Without the presence of a strong Raman marker this modeling approach is to be considered critical.

Adaptive common average filtering for myocontrol applications.

The use of electromyography (EMG) for the control of upper-limb prostheses has received great interest in neurorehabilitation engineering since decades. Important advances have been performed in the development of machine learning algorithms for myocontrol. This paper describes a novel adaptive filter for EMG preprocessing to be applied as conditioning stage for optimal subsequent information extraction. The aim of this filter is to improve both the quality (signal-to-noise ratio) and the selectivity of the EMG recordings. The filter is based on the classic common average reference (CAR), often used in EEG processing. However, while CAR is stationary, the proposed filter, which is referred to as adaptive common average reference (ACAR), is signal-dependent and its spatial transfer function is adapted over time. The ACAR filter is evaluated in this study for noise reduction and selectivity. Furthermore, it is proven that its application improves the performance of both pattern recognition and regression methods for myoelectric control. It is concluded that the proposed novel filter for EMG conditioning is a useful preprocessing tool in myocontrol applications.

The extraction of neural information from the surface EMG for the control of upper-limb prostheses: emerging avenues and challenges.

Despite not recording directly from neural cells, the surface electromyogram (EMG) signal contains information on the neural drive to muscles, i.e., the spike trains of motor neurons. Using this property, myoelectric control consists of the recording of EMG signals for extracting control signals to command external devices, such as hand prostheses. In commercial control systems, the intensity of muscle activity is extracted from the EMG and used for single degrees of freedom activation (direct control). Over the past 60 years, academic research has progressed to more sophisticated approaches but, surprisingly, none of these academic achievements has been implemented in commercial systems so far. We provide an overview of both commercial and academic myoelectric control systems and we analyze their performance with respect to the characteristics of the ideal myocontroller. Classic and relatively novel academic methods are described, including techniques for simultaneous and proportional control of multiple degrees of freedom and the use of individual motor neuron spike trains for direct control. The conclusion is that the gap between industry and academia is due to the relatively small functional improvement in daily situations that academic systems offer, despite the promising laboratory results, at the expense of a substantial reduction in robustness. None of the systems so far proposed in the literature fulfills all the important criteria needed for widespread acceptance by the patients, i.e. intuitive, closed-loop, adaptive, and robust real-time ( 200 ms delay) control, minimal number of recording electrodes with low sensitivity to repositioning, minimal training, limited complexity and low consumption. Nonetheless, in recent years, important efforts have been invested in matching these criteria, with relevant steps forwards.

Noninvasive, accurate assessment of the behavior of representative populations of motor units in targeted reinnervated muscles.

Targeted muscle reinnervation (TMR) redirects nerves that have lost their target, due to amputation, to remaining muscles in the region of the stump with the intent of establishing intuitive myosignals to control a complex prosthetic device. In order to directly recover the neural code underlying an attempted limb movement, in this paper, we present the decomposition of high-density surface electromyographic (EMG) signals detected from three TMR patients into the individual motor unit spike trains. The aim was to prove, for the first time, the feasibility of decoding the neural drive that would reach muscles of the missing limb in TMR patients, to show the accuracy of the decoding, and to demonstrate the representativeness of the pool of extracted motor units. Six to seven flexible EMG electrode grids of 64 electrodes each were mounted over the reinnervated muscles of each patient, resulting in up to 448 EMG signals. The subjects were asked to attempt elbow extension and flexion, hand open and close, wrist extension and flexion, wrist pronation and supination, of their missing limb. The EMG signals were decomposed using the Convolution Kernel Compensation technique and the decomposition accuracy was evaluated with a signal-based index of accuracy, called pulse-to-noise ratio (PNR). The results showed that the spike trains of 3 to 27 motor units could be identified for each task, with a sensitivity of the decomposition > 90%, as revealed by PNR. The motor unit discharge rates were within physiological values of normally innervated muscles. Moreover, the detected motor units showed a high degree of common drive so that the set of extracted units per task was representative of the behavior of the population of active units. The results open a path for a new generation of human-machine interfaces in which the control signals are extracted from noninvasive recordings and the obtained neural information is based directly on the spike trains of motor neurons.

Is accurate mapping of EMG signals on kinematics needed for precise online myoelectric control?

In this paper, we present a systematic analysis of the relationship between the accuracy of the mapping between EMG and hand kinematics and the control performance in goal-oriented tasks of three simultaneous and proportional myoelectric control algorithms: nonnegative matrix factorization (NMF), linear regression (LR), and artificial neural networks (ANN). The purpose was to investigate the impact of the precision of the kinematics estimation by a myoelectric controller for accurately complete goal-directed tasks. Nine naïve subjects performed a series of goal-directed myoelectric control tasks using the three algorithms, and their online performance was characterized by 6 indexes. The results showed that, although the three algorithms' mapping accuracies were significantly different, their online performance was similar. Moreover, for LR and ANN, the offline performance was not correlated to any of the online performance indexes, and only a weak correlation was found with three of them for NMF . We conclude that for reliable simultaneous and proportional myoelectric control, it is not necessary to achieve high accuracy in the mapping between EMG and kinematics. Rather, good online myoelectric control is achieved by the continuous interaction and adaptation of the user with the myoelectric controller through feedback (visual in the current study). Control signals generated by EMG with rather poor association with kinematic variables can still be fully exploited by the user for precise control. This conclusion explains the possibility of accurate simultaneous and proportional control over multiple degrees of freedom when using unsupervised algorithms, such as NMF.

Intuitive, online, simultaneous, and proportional myoelectric control over two degrees-of-freedom in upper limb amputees.

We propose an approach for online simultaneous and proportional myoelectric control of two degrees-of-freedom (DoF) of the wrist, using surface electromyographic signals. The method is based on the nonnegative matrix factorization (NMF) of the wrist muscle activation to extract low-dimensional control signals translated by the user into kinematic variables. This procedure does not need a training set of signals for which the kinematics is known (labeled dataset) and is thus unsupervised (although it requires an initial calibration without labeled signals). The estimated control signals using NMF are used to directly control two DoFs of wrist. The method was tested on seven subjects with upper limb deficiency and on seven able-bodied subjects. The subjects performed online control of a virtual object with two DoFs to achieve goal-oriented tasks. The performance of the two subject groups, measured as the task completion rate, task completion time, and execution efficiency, was not statistically different. The approach was compared, and demonstrated to be superior to the online control by the industrial state-of-the-art approach. These results show that this new approach, which has several advantages over the previous myoelectric prosthetic control systems, has the potential of providing intuitive and dexterous control of artificial limbs for amputees.

Real time simultaneous and proportional control of multiple degrees of freedom from surface EMG: Preliminary results on subjects with limb deficiency.

We present the real time simultaneous and proportional control of two degrees of freedom (DoF), using surface electromyographic signals from the residual limbs of three subject with limb deficiency. Three subjects could control a virtual object in two dimensions using their residual muscle activities to achieve goal-oriented tasks. The subjects indicated that they found the control intuitive and useful. These results show that such a simultaneous and proportional control paradigm is a promising direction for multi-functional prosthetic control.

A multisensor implant for continuous monitoring of intracranial pressure dynamics.

In humans, intracranial pressure (ICP) is not only influenced by pathology, but also by orientation in space and body movements. Therefore, it is proposed to measure ICP dynamics and body acceleration simultaneously. An algorithm for acceleration analysis was developed to monitor orientation in space and allow more accurate examination of ICP dynamics during quiet periods. For continuous monitoring, an implant was developed and wireless data transmission was implemented; this prototype was successfully tested in five pigs. Hydrocephalus with increased ICP was experimentally induced in the animals using a surgical kaolin infusion. This model of porcine pathology was then tested with the implant with the aim for eventual use in humans. ICP dynamics and 2D-acceleration data were simultaneously recorded for up to two weeks. This study allowed 24-h monitoring and provided analysable data on porcine ICP dynamics with humanlike ICP waves, the so called B- and P-waves. Results show that acceleration often had a stronger influence on ICP than the amplitudes of the physiological ICP characteristics. With test animals in a standing position, without obvious body movement, ICP varied to an extent that made the characteristic ICP waves difficult to identify. These data allow us to conclude that analysis of both ICP and acceleration may be essential for autonomous implants.