EEG and Keystroke Based Multimodal Biometric System
Electroencephalography (EEG) based biometric systems are gaining attention for their anti-spoofing capability but lack accuracy due to signal variability at different psychological and physiological conditions. On the other hand, keystroke dynamics-based systems achieve very high accuracy but have low anti-spoofing capability. To address these issues, a novel multimodal biometric system combining EEG and keystroke dynamics is proposed in this project. A dataset was created by acquiring both keystroke dynamics and EEG signals simultaneously from 10 users. Each user participated in 500 trials at 10 different sessions (days) to replicate real-life signal variability. A machine learning classification pipeline is developed using multi-domain feature extraction (time, frequency, time-frequency), feature selection (Gini impurity), classifier design, and score level fusion. Different classifiers were trained, validated, and tested for two different classification experiments – personalized and generalized. For identification and authentication, 99.9% and 99.6% accuracies are achieved, respectively for the Random Forest classifier in 5 fold cross-validation. These results outperform the individual modalities with a significant margin (~5%). We also developed a binary template matching-based algorithm, which gives 93.64% accuracy 6X faster. The proposed method can be considered secure and reliable for any kind of biometric identification and authentication.
Oct 2020 - Jun 2021
Nurse Care Activity recognition
Nurse care activity recognition is a new challenging research field in human activity recognition (HAR) because unlike other activity recognition, it has severe class imbalance problem and intra-class variability depending on both the subject and the receiver. In this project, we applied the Random Forest-based resampling method to solve the class imbalance problem in the Heiseikai data, nurse care activity dataset. This method consists of resampling, feature selection based on Gini impurity, and model training and validation with Stratified KFold cross-validation. By implementing the Random Forest classifier, we achieved 65.9% average cross-validation accuracy in classifying 12 activities conducted by nurses in both lab and real-life settings. Our team, "Britter Baire" developed this algorithmic pipeline for "The 2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data".
June 2020 - Dec 2020
Deep Learning Based EMG Hand Gesture Classification
In this project, a comparative study of classifying different hand gestures of two well-known surface Electromyogram (sEMG) data sets, Rami Khusaba EMG repository, and UCI Machine Learning Repository, is shown. Applying transfer learning and CNN-LSTM neural network architectures, we find out a suitable control scheme for a myoelectric prosthetic hand (we mention it as DUFAB Hand). At first, the continuous wavelet transform (CWT) is exploited to create images from the sEMG signal, which serves as a powerful feature for the classification of different hand gestures. Then, we transferred the learning of various neural nets of image classification, e.g., AlexNet, and ResNet-18 to the sEMG image classification. Application of these deep neural networks outperformed general machine learning techniques with higher accuracy and performance. For example, the combination of CNN and LSTM has achieved the state of the art accuracies for these data sets, of 99.72% for UCI Machine Learning Repository and 99.83% for Rami Khusaba EMG repository respectively. The main contribution of this paper is, establishing an algorithmic pipeline using continuous wavelet transform (CWT) and CNN-LSTM deep neural networks to achieve high accuracy in two sEMG datasets.
Aug 2019 - Mar 2020
Design and Implementation of a Low-Cost prosthetic hand
In this project, the design and implementation of a low-cost myoelectric prosthetic hand prototype are done. Our main contributions are - (i) proposing a new Computer Aided Design (CAD) suitable for laser cutter for prosthetic hand and (ii) a new feature, XCORR that calculates cross-correlation between two channels of EMG signals for same gesture, is introduced to classify Electromyography (EMG) signals. Individual parts are designed in a 2D plane and assembled to make a 3D structure in CAD software. Acrylic is used as the main material. The prosthetic hand can exhibit 6-degree of freedom (DOF), i.e., six gestures which are required to perform essential daily tasks and grabbing objects. Commonly used features, such as SMAT, HJORTH parameters are extracted from raw EMG signals and combined with newly introduced feature XCORR to classify 6 types of individual and combined finger movements. Using the classified EMG signal as input we have controlled the designed prosthetic hand in both simulation and hardware level. The controller is designed in such a way that the angular displacement of each finger meets a predefined angle value for each classified gesture. For classification of EMG signals, the highest accuracy is observed as 93.9% with a low computational load. The proposed hand will be affordable and suitable for amputees and help them regain their ability to work.
Jan 2018 - Dec 2018