A robust method for VR-based hand gesture recognition using density-based CNN

Many VR-based medical purposes applications have been developed to help patients with mobility decrease caused by accidents, diseases, or other injuries to do physical treatment efficiently. VR-based applications were considered more effective helper for individual physical treatment because of their low-cost equipment and flexibility in time and space, less assistance of a physical therapist. A challenge in developing a VR-based physical treatment was understanding the body part movement accurately and quickly. We proposed a robust pipeline to understanding hand motion accurately. We retrieved our data from movement sensors such as HTC vive and leap motion. Given a sequence position of palm, we represent our data as binary 2D images of gesture shape. Our dataset consisted of 14 kinds of hand gestures recommended by a physiotherapist. Given 33 3D points that were mapped into binary images as input, we trained our proposed density-based CNN. Our CNN model concerned with our input characteristics, having many 'blank block pixels', 'single-pixel thickness' shape and generated as a binary image. Pyramid kernel size applied on the feature extraction part and classification layer using softmax as loss function, have given 97.7% accuracy

ROBOT LEARNING OF OBJECT MANIPULATION TASK ACTIONS FROM HUMAN DEMONSTRATIONS

Robot learning from demonstration is a method which enables robots to learn in a similar way as humans. In this paper, a framework that enables robots to learn from multiple human demonstrations via kinesthetic teaching is presented. The subject of learning is a high-level sequence of actions, as well as the low-level trajectories necessary to be followed by the robot to perform the object manipulation task. The multiple human demonstrations are recorded and only the most similar demonstrations are selected for robot learning. The high-level learning module identifies the sequence of actions of the demonstrated task. Using Dynamic Time Warping (DTW) and Gaussian Mixture Model (GMM), the model of demonstrated trajectories is learned. The learned trajectory is generated by Gaussian mixture regression (GMR) from the learned Gaussian mixture model.  In online working phase, the sequence of actions is identified and experimental results show that the robot performs the learned task successfully

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

Human worker activity recognition in industrial environments

In this work, an intelligent human-machine interface (HMI) for human worker activity recognition in industrial environments is presented. The interface consists of components for robust and accurate 3D position estimation in workspace environments, the recognition of task-related worker activities and human-computer interaction via gestures. All components of the presented HMI are flexible with respect to applications and can be transferred to other activity recognition problems, as well

On the formulation and uses of SVD-based generalized curvatures

2016 Summer.Includes bibliographical references.In this dissertation we consider the problem of computing generalized curvature values from noisy, discrete data and applications of the provided algorithms. We first establish a connection between the Frenet-Serret Frame, typically defined on an analytical curve, and the vectors from the local Singular Value Decomposition (SVD) of a discretized time-series. Next, we expand upon this connection to relate generalized curvature values, or curvatures, to a scaled ratio of singular values. Initially, the local singular value decomposition is centered on a point of the discretized time-series. This provides for an efficient computation of curvatures when the underlying curve is known. However, when the structure of the curve is not known, for example, when noise is present in the tabulated data, we propose two modifications. The first modification computes the local singular value decomposition on the mean-centered data of a windowed selection of the time-series. We observe that the mean-center version increases the stability of the curvature estimations in the presence of signal noise. The second modification is an adaptive method for selecting the size of the window, or local ball, to use for the singular value decomposition. This allows us to use a large window size when curvatures are small, which reduces the effects of noise thanks to the use of a large number of points in the SVD, and to use a small window size when curvatures are large, thereby best capturing the local curvature. Overall we observe that adapting the window size to the data, enhances the estimates of generalized curvatures. The combination of these two modifications produces a tool for computing generalized curvatures with reasonable precision and accuracy. Finally, we compare our algorithm, with and without modifications, to existing numerical curvature techniques on different types of data such as that from the Microsoft Kinect 2 sensor. To address the topic of action segmentation and recognition, a popular topic within the field of computer vision, we created a new dataset from this sensor showcasing a pose space skeletonized representation of individuals performing continuous human actions as defined by the MSRC-12 challenge. When this data is optimally projected onto a low-dimensional space, we observed each human motion lies on a distinguished line, plane, hyperplane, etc. During transitions between motions, either the dimension of the optimal subspace significantly, or the trajectory of the curve through pose space nearly reverses. We use our methods of computing generalized curvature values to identify these locations, categorized as either high curvatures or changing curvatures. The geometric characterization of the time-series allows us to segment individual,or geometrically distinct, motions. Finally, using these segments, we construct a methodology for selecting motions to conjoin for the task of action classification

System-level design of energy-efficient sensor-based human activity recognition systems: a model-based approach

This thesis contributes an evaluation of state-of-the-art dataflow models of computation regarding their suitability for a model-based design and analysis of human activity recognition systems, in terms of expressiveness and analyzability, as well as model accuracy. Different aspects of state-of-the-art human activity recognition systems have been modeled and analyzed. Based on existing methods, novel analysis approaches have been developed to acquire extra-functional properties like processor utilization, data communication rates, and finally energy consumption of the system

Privacy-Protecting Techniques for Behavioral Data: A Survey

Our behavior (the way we talk, walk, or think) is unique and can be used as a biometric trait. It also correlates with sensitive attributes like emotions. Hence, techniques to protect individuals privacy against unwanted inferences are required. To consolidate knowledge in this area, we systematically reviewed applicable anonymization techniques. We taxonomize and compare existing solutions regarding privacy goals, conceptual operation, advantages, and limitations. Our analysis shows that some behavioral traits (e.g., voice) have received much attention, while others (e.g., eye-gaze, brainwaves) are mostly neglected. We also find that the evaluation methodology of behavioral anonymization techniques can be further improved

Early Abstraction of Inertial Sensor Data for Long-Term Deployments

Advances in microelectronics over the last decades have led to miniaturization of computing devices and sensors. A driving force to use these in various application scenarios is the desire to grasp physical phenomena from the environment, objects and living entities. We investigate sensing in two particularly challenging applications: one where small sensor modules are worn by people to detect their activities, and one where wirelessly networked sensors observe events over an area. This thesis takes a data-driven approach, focusing on human motion and vibrations caused by trains that are captured by accelerometer sensors as time series and shall be analyzed for characteristic patterns. For both, the acceleration sensor must be sampled at relatively high rates in order to capture the essence of the phenomena, and remain active for long stretches of time. The large amounts of gathered sensor data demand novel approaches that are able to swiftly process the data while guaranteeing accurate classification results. The following contributions are made in particular: * A data logger that would suit the requirements of long-term deployments is designed and evaluated. In a power profiling study both hardware components and firmware parameters are thoroughly tested, revealing that the sensor is able to log acceleration data at a sampling rate of 100 Hertz for up to 14 full days on a single battery charge. * A technique is proposed that swiftly and accurately abstracts an original signal with a set of linear segments, thus preserving its shape, while being twice as fast as a similar method. This allows for more efficient pattern matching, since for each pattern only a fraction of data points must be considered. A second study shows that this algorithm can perform data abstraction directly on a data logger with limited resources. * The railway monitoring scenario requires streaming vibration data to be analyzed for particular sparse and complex events directly on the sensor node, extracting relevant information such as train type or length from the shape of the vibration footprint. In a study conducted on real-world data, a set of efficient shape features is identified that facilitates train type prediction and length estimation with very high accuracies. * To achieve fast and accurate activity recognition for long-term bipolar patients monitoring scenarios, we present an approach that relies on the salience of motion patterns (motifs) that are characteristic for the target activity. These motifs are accumulated by using a symbolic abstraction that encodes the shape of the original signal. A large-scale study shows that a simple bag-of-words classifier trained with extracted motifs is on par with traditional approaches regarding the accuracy, while being much faster. * Some activities are hard to predict from acceleration data alone with the aforementioned approach. We argue that human-object interactions, captured as human motion and grasped objects through RFID, are an ideal supplement. A custom bracelet-like antenna to detect objects from up to 14 cm is proposed, along with a novel benchmark to evaluate such wearable setups. By aiming for wearable and wirelessly networked sensor systems, these contributions apply for particularly challenging applications that require long-term deployments of miniature sensors in general. They form the basis of a framework towards efficient event detection that relies heavily on early data abstraction and shape-based features for time series, while focusing less on the classification techniques

Interactive Tango Milonga: An Interactive Dance System for Argentine Tango Social Dance

abstract: When dancers are granted agency over music, as in interactive dance systems, the actors are most often concerned with the problem of creating a staged performance for an audience. However, as is reflected by the above quote, the practice of Argentine tango social dance is most concerned with participants internal experience and their relationship to the broader tango community. In this dissertation I explore creative approaches to enrich the sense of connection, that is, the experience of oneness with a partner and complete immersion in music and dance for Argentine tango dancers by providing agency over musical activities through the use of interactive technology. Specifically, I create an interactive dance system that allows tango dancers to affect and create music via their movements in the context of social dance. The motivations for this work are multifold: 1) to intensify embodied experience of the interplay between dance and music, individual and partner, couple and community, 2) to create shared experience of the conventions of tango dance, and 3) to innovate Argentine tango social dance practice for the purposes of education and increasing musicality in dancers.Dissertation/ThesisDoctoral Dissertation Music 201