The speed and pen-pressure while sketching a spiral are lower among Parkinson’s disease (PD) patients with higher severity of the disease. However, the correlation between these features and the severity level (SL) of PD has been reported to be 0.4. There is a need for identifying parameters with a stronger correlation for considering this for accurate diagnosis of the disease. This study has proposed the use of the Composite Index of Speed and Pen-pressure (CISP) of sketching as a feature for analyzing the severity of PD. 28 Control group (CG) and 27 PD patients; total 55 participants, were recruited and assessed for United Parkinson’s Disease Rating Scale (UPDRS). They drew guided Archimedean spiral on an A3 sheet. Speed, pen-pressure and CISP were computed and analyzed to obtain their correlation with severity of the disease. The correlation of speed, pen-pressure and CISP with the severity of PD was; -0.415, -0.584 and -0.641 respectively. Mann-Whitney U test confirmed that CISP was suitable to distinguish between PD and CG, while nonparametric k-sample Kruskal-Wallis test confirmed that it was significantly different for PD SL-1 and PD SL-3. This shows that CISP during spiral sketching may be used to differentiate between CG, Parkinson’s disease and between PD SL-1 and PD SL-3 but not SL-2.

Surface electromyography (sEMG) data acquired during lower limb movements has the potential for investigating knee pathology. Nevertheless, a major challenge encountered with sEMG signals generated by lower limb movements is the intersubject variability, because the signals recorded from the leg or thigh muscles are contingent on the characteristics of a subject such as gait activity and muscle structure. In order to cope with this difficulty, we have designed a three-step classification scheme. First, the multichannel sEMG is decomposed into activities of the underlying sources by means of independent component analysis via entropy bound minimization. Next, a set of time-domain features, which would best discriminate various movements, are extracted from the source estimates. Finally, the feature selection is performed with the help of the Fisher score and a screen-plot-based statistical technique, prior to feeding the dimension-reduced features to the linear discriminant analysis. The investigation involves 11 healthy subjects and 11 individuals with knee pathology performing three different lower limb movements, namely, walking, sitting, and standing, which yielded an average classification accuracy of 96.1% and 86.2%, respectively. While the outcome of this study per se is very encouraging, with suitable improvement, the clinical application of such an sEMG-based pattern recognition system that distinguishes healthy and knee pathological subjects would be an attractive consequence.

Identifying finger and wrist flexion based actions using a single channel surface electromyogram (sEMG) can lead to a number of applications such as sEMG based controllers for near elbow amputees, human computer interface (HCI) devices for elderly and for defence personnel. These are currently infeasible because classification of sEMG is unreliable when the level of muscle contraction is low and there are multiple active muscles. The presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when muscles are weakly active such as during sustained wrist and finger flexion. This paper reports the use of fractal properties of sEMG to reliably identify individual wrist and finger flexion, overcoming the earlier shortcomings.

We present in this paper a study of three EEG signals feature extraction techniques. These techniques have been widely employed in researches of emotional states recognition: statistical characteristics, features based on PSD (Power Spectral Density) and features based on HOC (High Order Crossings). The validation was performed via classification of emotional states of calm and stress using the K-NN based classifier in off-line mode using EEG signals from available DEAP database. The best results achieved were 70.1%, using the PSD based technique, and 69.59% using the HOC based technique.

Background: The enhancement in the performance of the myoelectric pattern recognition techniques based on deep learning algorithm possess computationally expensive and exhibit extensive memory behavior. Therefore, in this paper we report a deep learning framework named ‘Low-Complex Movement recognition-Net’ (LoCoMo-Net) built with convolution neural network (CNN) for recognition of wrist and finger flexion movements; grasping and functional movements; and force pattern from single channel surface electromyography (sEMG) recording. The network consists of a two-stage pipeline: 1) input data compression; 2) data-driven weight sharing. Methods: The proposed framework was validated on two different datasets- our own dataset (DS1) and publicly available NinaPro dataset (DS2) for 16 movements and 50 movements respectively. Further, we have prototyped the proposed LoCoMo-Net on Virtex-7 Xilinx field-programmable gate array (FPGA) platform and validated for 15 movements from DS1 to demonstrate its feasibility for real-time execution. Results: The effectiveness of the proposed LoCoMo-Net was verified by a comparative analysis against the benchmarked models using the same datasets wherein our proposed model outperformed Twin- Support Vector Machine (SVM) and existing CNN based model by an average classification accuracy of 8.5 % and 16.0 % respectively. In addition, hardware complexity analysis is done to reveal the advantages of the two-stage pipeline where approximately 27 %, 49 %, 50 %, 23 %, and 43 % savings achieved in lookup tables (LUT’s), registers, memory, power consumption and computational time respectively. Conclusion: The clinical significance of such sEMG based accurate and low-complex movement recognition system can be favorable for the potential improvement in quality of life of an amputated persons.