Wearable Smart Rings for Multi-Finger Gesture Recognition Using Supervised Learning

This thesis presents a wearable, smart ring with an integrated Bluetooth low-energy (BLE) module. The system uses an accelerometer and a gyroscope to collect fingers motion data. A prototype was manufactured, and its performance was tested. To detect complex finger movements, two rings are worn on the point and thumb fingers while performing the gestures. Nine pre-defined finger movements were introduced to verify the feasibility of the proposed method. Data pre-processing techniques, including normalization, statistical feature extraction, random forest recursive feature elimination (RF-RFE), and k-nearest neighbors sequential forward floating selection (KNN-SFFS), were applied to select well-distinguished feature vectors to enhance gesture recognition accuracy. Three supervised machine learning algorithms were used for gesture classification purposes, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes (NB). We demonstrated that when utilizing the KNN-SFFS recommended features as the machine learning input, our proposed finger gesture recognition approach not only significantly decreases the dimension of the feature vector, results in faster response time and prevents overfitted model, but also provides approximately similar machine learning prediction accuracy compared to when all elements of feature vectors were used. By using the KNN as the primary classifier, the system can accurately recognize six one-finger and three two-finger gestures with 97.1% and 97.0% accuracy, respectively

Activity Recognition with Evolving Data Streams: A Review

Activity recognition aims to provide accurate and opportune information on people’s activities by leveraging sensory data available in today’s sensory rich environments. Nowadays, activity recognition has become an emerging field in the areas of pervasive and ubiquitous computing. A typical activity recognition technique processes data streams that evolve from sensing platforms such as mobile sensors, on body sensors, and/or ambient sensors. This paper surveys the two overlapped areas of research of activity recognition and data stream mining. The perspective of this paper is to review the adaptation capabilities of activity recognition techniques in streaming environment. Categories of techniques are identified based on different features in both data streams and activity recognition. The pros and cons of the algorithms in each category are analysed and the possible directions of future research are indicated

Eddy current defect response analysis using sum of Gaussian methods

This dissertation is a study of methods to automatedly detect and produce approximations of eddy current differential coil defect signatures in terms of a summed collection of Gaussian functions (SoG). Datasets consisting of varying material, defect size, inspection frequency, and coil diameter were investigated. Dimensionally reduced representations of the defect responses were obtained utilizing common existing reduction methods and novel enhancements to them utilizing SoG Representations. Efficacy of the SoG enhanced representations were studied utilizing common Machine Learning (ML) interpretable classifier designs with the SoG representations indicating significant improvement of common analysis metrics

An automated algorithmic approach for activity recognition and step detection in the presence of functional compromise

Wearable technology is a potential stepping stone towards personalised healthcare. It provides the opportunity to collect objective physical activity data from the users and could enable clinicians to make more informed decisions and hence provide better treatments. Current physical activity monitors generally work well in healthy populations but can be problematic when used in some patient groups with severely abnormal function. We studied healthy volunteers to assess how different algorithms might perform for those with normal and simulated-pathological conditions. Participants (n=30) were recruited from the University of Leeds to perform nine predifined activities under normal and simulated-pathological conditions using two MOX accelerometers on wrist and ankle (Maastricht Instruments, NL). Condition classification was performed using a Support Vector Machine algorithm. Activity classification was performed with five different Machine Learning algorithms: Support Vector Machine, k-Nearest Neighbour, Random Forest, Multilayer Perceptron, and Naive Bayes. A step count algorithm was developed based on pattern recognition approach, using two main techniques, Dynamic Time Warping and Dynamic Time Warping-Barycentre Averaging. Finally, synthetic acceleration signal was generated that represented walking activities since there was limited access to patient data and to refine synthetic data generation in this field. Three dynamic coupled equations were used to represent the morphology of the desired signal. Wrist and ankle locations performed similarly and the wrist location was used for further analysis. Both condition and activity classification algorithms achieved good performance metrics i.e. that the volunteer has been correctly classified in the right condition, and the activities performed have been correctly recognised. Additionally, the novel step count algorithm achieved more accurate results for both conditions in comparison to existing algorithms from the literature. Finally, the signal generation approach seems promising since the normal condition synthetic signals matched closely to their associated original signals. Algorithms developed for a specific group or even person with functional pathology, using techniques such as Dynamic Time Warping-Barycentre Averaging produce better results than traditional algorithms trained on data from a different group