Seafarer citizen scientist ocean transparency data as a resource for phytoplankton and climate research.

The oceans' phytoplankton that underpin the marine food chain appear to be changing in abundance due to global climate change. Here, we compare the first four years of data from a citizen science ocean transparency study, conducted by seafarers using home-made Secchi Disks and a free Smartphone application called Secchi, with contemporaneous satellite ocean colour measurements. Our results show seafarers collect useful Secchi Disk measurements of ocean transparency that could help future assessments of climate-induced changes in the phytoplankton when used to extend historical Secchi Disk data.

A preliminary investigation of the effect of force variation for myoelectric control of hand prosthesis.

The myoelectric control of prostheses has been an important area of research for the past 40 years. Significant advances have been achieved with Pattern Recognition (PR) systems regarding the number of movements to be classified with high accuracy. However, practical robustness still needs further research. This paper focuses on investigating the effect of the change in force levels by transradial amputee persons on the performance of PR systems. Two below-elbow amputee persons participated in the study. Three levels of forces (low, medium, and high) were recorded for different hand grips with the help of visual feedback from the Electromyography (EMG) signals. Results showed that changing the force level degraded the performance of the myoelectric control system by up to 60% with 12 EMG channels for 4 hand grips and a rest position. We investigated different EMG feature sets in combination with a Linear Discriminant Analysis (LDA) classifier. The performance was slightly better with Time Domain (TD) features compared to Auto Regression (AR) coefficients and Root Mean Square (RMS) features. Finally, the error of the classification was considerably reduced to approximately 17% when the PR system was trained with all force levels.

Protocol for site selection and movement assessment for the myoelectric control of a multi-functional upper-limb prosthesis.

Although there have been many advances in electromyography (EMG) signal processing and pattern recognition (PR) for the control of multi-functional upper-limb prostheses, some the outstanding problems need to be solved before practical PR-based prostheses can be put into service. Some of these are the lack of training and deployment protocols and the provision of the tools required. Therefore, we present a preliminary procedure to personalize the prosthesis deployment. In the first step, we record the demographic information of each individual amputee person and their background. In the second step of the protocol, the EMG signals are acquired. PR algorithms and parameters will be chosen in the 3(rd) step of the protocol. In the 4(th) step, the best number of EMG sensors to achieve the maximal performance with a full set of gestures is identified. The final step involves finding the best set of movements that the amputee person can produce with an accuracy > 95% as well as identifying the movements with the worst performance, which would require further training. This proposed approach is validated with 2 transradial amputees.

Classification of finger movements for the dexterous hand prosthesis control with surface electromyography.

A method for the classification of finger movements for dexterous control of prosthetic hands is proposed. Previous research was mainly devoted to identify hand movements as these actions generate strong electromyography (EMG) signals recorded from the forearm. In contrast, in this paper, we assess the use of multichannel surface electromyography (sEMG) to classify individual and combined finger movements for dexterous prosthetic control. sEMG channels were recorded from ten intact-limbed and six below-elbow amputee persons. Offline processing was used to evaluate the classification performance. The results show that high classification accuracies can be achieved with a processing chain consisting of time domain-autoregression feature extraction, orthogonal fuzzy neighborhood discriminant analysis for feature reduction, and linear discriminant analysis for classification. We show that finger and thumb movements can be decoded accurately with high accuracy with latencies as short as 200 ms. Thumb abduction was decoded successfully with high accuracy for six amputee persons for the first time. We also found that subsets of six EMG channels provide accuracy values similar to those computed with the full set of EMG channels (98% accuracy over ten intact-limbed subjects for the classification of 15 classes of different finger movements and 90% accuracy over six amputee persons for the classification of 12 classes of individual finger movements). These accuracy values are higher than previous studies, whereas we typically employed half the number of EMG channels per identified movement.

Single channel-based myoelectric control of hand movements with Empirical Mode Decomposition.

Myoelectric control has been an important area of research for the past 40 years for prosthetic control, since it targets amputees who lost their body limbs. Advances were achieved concerning the number of movements to be classified with high accuracy. Hence, not much research was done to extract information from single channel Electromyogram (EMG). This paper presents Empirical Mode Decomposition (EMD) for Feature Extraction (FE) from single-channel EMG for ten class wrist movements and handgrips. Two classification schemes were applied based on Time Domain-Auto Regression (TDAR) features (a commonly used approach in the Literature) and EMD, with Principle Component Analysis (PCA) for dimensionality reduction, and Support Vector Machine (SVM) for classification. With the use of only one single-channel EMG, the EMD achieved an improvement in the classification rate for a single flexor and extensor EMG channel of 11.2% (from 83.7% to 94.4%) and 13% (from 80.16% to 93.16%), respectively. The results suggested that EMD remarkably improves the classification performance for a single-channel EMG over the traditional time domain FE technique. This will reduce the computational cost of applying only one channel EMG and facilitates the acquisition of the EMG. The main drawback of using EMD technique is that it is not suitable for real time processing of prosthetic control.

Development and assessment of methods for detecting dementia using the human electroencephalogram.

This paper makes an outline case for the need for a low-cost, easy to administer method for detecting dementia within the growing at risk population. It proposes two methods for electroencephalogram (EEG) analysis for detecting dementia that could fulfil such a need. The paper describes a fractal dimension-based method for analyzing the EEG waveforms of subjects with dementia and reports on an assessment which demonstrates that an appropriate fractal dimension measure could achieve 67% sensitivity to probable Alzheimer's disease (as suggested by clinical psychometric testing and EEG findings) with a specificity of 99.9%. An alternative method based on the probability density function of the zero-crossing intervals is shown to achieve 78% sensitivity to probable Alzheimer's disease and an estimated sensitivity to probable Vascular (or mixed) dementia of 35% (as suggested by clinical psychometric testing and EEG findings) with a specificity of 99.9%. This compares well with other studies, reported by the American Academy of Neurology, which typically provide a sensitivity of 81% and specificity of 70%. The EEG recordings used to assess these methods included artefacts and had no a priori selection of elements "suitable for analysis." This approach gives a good prediction of the usefulness of the methods, as they would be used in practice. A total of 39 patients (30 probable Alzheimer's Disease, six Vascular Dementia and three mixed dementia) and 42 healthy volunteers were involved in the study. However, although results from the preliminary evaluation of the methods are promising, there is a need for a more extensive study to validate the methods using EEGs from a larger and more varied patient cohorts with neuroimaging results, to exclude other causes and cognitive scores to correlate results with severity of cognitive status.

A comparative study of fetal heart rate variability analysis techniques.

This study examines a novel methodology for continuous fetal heart rate variability (FHRV) assessment in a non-stationary intrapartum fetal heart rate (FHR). The specific aim was to investigate simple statistics, dimension estimates and entropy estimates as methods to discriminate situations of low FHRV related to non-reassuring fetal status or as a consequence of sedatives given to the mother. Using a t-test it is found that the dimension of the zero set and sample entropy reveal a difference in mean distribution of significance >99%. Thus it may prove possible to build a discriminating system based on either one or a combination of these techniques.