Faculty Publications
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Item Reducing the effect of wrist variation on pattern recognition of Myoelectric Hand Prostheses Control through Dynamic Time Warping(Elsevier Ltd, 2020) Powar, O.S.; Chemmangat, K.For upper limb prostheses, research carried out earlier mainly focused on increasing the classification accuracy of the hand movements; but there exist a little work done on factors affecting it in real-time control such as wrist variation. Amputees with functional wrist use their prostheses in multiple wrist positions. Since the Electromyography (EMG) data is taken while the subject is performing the motion in different wrist position, it can degrade the performance of the Pattern Recognition (PR) system. In this work, a wrist independent PR scheme has been developed. In this regard, Dynamic Time Warping (DTW) is used to overcome the effects due to wrist variation. The performance of the DTW scheme as a PR system is validated using two training methods; with classification accuracy as a performance measure on data taken from the database of ten intact subjects for six hand motions carried out at three different wrist orientations. On the database, an average classification accuracy of about 93.3% was obtained while trained using EMG data from all possible wrist positions. The effectiveness of the method is demonstrated in terms of classification accuracy and processing time when compared with the Time-domain power spectral descriptors (TD-PSD) method which outperformed other methods in the literature for reducing the impact of wrist variation on EMG based PR. The results show that the DTW can be a computationally cheap and accurate PR system for real-time hand movement classification. © 2019 Elsevier LtdItem Detection of arrhythmia from electrocardiogram signals using a novel gaussian assisted signal smoothing and pattern recognition(Elsevier Ltd, 2022) Chandrasekar, A.; Shekar, D.D.; Hiremath, A.C.; Chemmangat, K.The electrocardiogram is a widely used measurement for individual heart conditions, and much effort has been put into automatic arrhythmia diagnosis using machine learning. However, the classification performance is hampered by the use of less representative data in conjunction with traditional machine learning models. This paper proposes a novel algorithm for pre-processing raw Electrocardiogram signals via Gaussian Assisted Signal Smoothing. In this method, the ECG signal is modeled as a low pass component and a weighted sum of Gaussians. The Gaussians are used to model the peak characteristics of the signal, effectively preserving its structure and morphology while eliminating the noise, which is evident by the enhanced peak signal-to-noise ratio of the GASS signal. The R peaks obtained from the Pan Tompkins algorithm are used to extract the heartbeats from the filtered signal using a windowing technique. A cascaded combination of a Convolutional Neural Network and a Quadratic Support Vector Machine is then used to classify the heartbeats. The CNN model has 131,661 parameters, making it much lighter than previously reported works. The MIT-BIH Arrhythmia Database was used for our experiments. Across eleven classes, our results reveal that the model has an accuracy of 97.63% and an average F1 score of 0.9263. In contrast, previous works have primarily focused on a one vs. all or a five-class classification. From a signal processing standpoint, the proposed method offers a promising solution for Signal Filtering and Arrhythmia Classification. © 2021 Elsevier Ltd