Estimating heart rate via depth video motion tracking

Depth sensors like Microsoft Kinect can acquire partial geometric information in a 3D scene via captured depth images, with potential application to non-contact health monitoring. However, captured depth videos typically suffer from low bit-depth representation and acquisition noise corruption, and hence using them to deduce health metrics that require tracking subtle 3D structural details is difficult. In this paper, we propose to capture depth video using Kinect 2.0 to estimate the heart rate of a human subject; as blood is pumped to circulate through the head, tiny oscillatory head motion can be detected for periodicity analysis. Specifically, we first perform a joint bit-depth enhancement / denoising procedure to improve the quality of the captured depth images, using a graph-signal smoothness prior for regularization. We then track an automatically detected nose region throughout the depth video to deduce 3D motion vectors. The deduced 3D vectors are then analyzed via principal component analysis to estimate heart rate. Experimental results show improved tracking accuracy using our proposed joint bit-depth enhancement / denoising procedure, and estimated heart rates are close to ground truth

Estimating heart rate and rhythm via 3D motion tracking in depth video

Low-cost depth sensors, such as Microsoft Kinect, have potential for non-intrusive, non-contact health monitoring that is robust to ambient lighting conditions. However, captured depth images typically suer from low bit-depth and high acquisition noise, and hence processing them to estimate biometrics is dicult. In this paper, we propose to capture depth video of a human subject using Kinect 2.0 to estimate his/her heart rate and rhythm (regularity); as blood is pumped from the heart to circulate through the head, tiny oscillatory head motion due to Newtonian mechanics can be detected for periodicity analysis. Specifically, we first restore a captured depth video via a joint bit-depth enhancement / denoising procedure, using a graph-signal smoothness prior for regularization. Second, we track an automatically detected head region throughout the depth video to deduce 3D motion vectors. The detected vectors are fed back to the depth restoration module in a loop to ensure that the motion information in two modules are consistent, improving performance of both restoration and motion tracking in the process. Third, the computed 3D motion vectors are projected onto its principal component for 1D signal analysis, composed of trend removal, band-pass filtering, and wavelet-based motion denoising. Finally, the heart rate is estimated via Welch power spectrum analysis, and the heart rhythm is computed via peak detection. Experimental results show accurate estimation of the heart rate and rhythm using our proposed algorithm as compared to rate and rhythm estimated by a portable oximeter

A graph-based signal processing approach for low-rate energy disaggregation

Graph-based signal processing (GSP) is an emerging field that is based on representing a dataset using a discrete signal indexed by a graph. Inspired by the recent success of GSP in image processing and signal filtering, in this paper, we demonstrate how GSP can be applied to non-intrusive appliance load monitoring (NALM) due to smoothness of appliance load signatures. NALM refers to disaggregating total energy consumption in the house down to individual appliances used. At low sampling rates, in the order of minutes, NALM is a difficult problem, due to significant random noise, unknown base load, many household appliances that have similar power signatures, and the fact that most domestic appliances (for example, microwave, toaster), have usual operation of just over a minute. In this paper, we proposed a different NALM approach to more traditional approaches, by representing the dataset of active power signatures using a graph signal. We develop a regularization on graph approach where by maximizing smoothness of the underlying graph signal, we are able to perform disaggregation. Simulation results using publicly available REDD dataset demonstrate potential of the GSP for energy disaggregation and competitive performance with respect to more complex Hidden Markov Model-based approaches

Blind non-intrusive appliance load monitoring using graph-based signal processing

With ongoing massive smart energy metering deployments, disaggregation of household's total energy consumption down to individual appliances using purely software tools, aka. non-intrusive appliance load monitoring (NALM), has generated increased interest. However, despite the fact that NALM was proposed over 30 years ago, there are still many open challenges. Indeed, the majority of approaches require training and are sensitive to appliance changes requiring regular re-training. In this paper, we tackle this challenge by proposing a "blind" NALM approach that does not require any training. The main idea is to build upon an emerging field of graph-based signal processing to perform adaptive thresholding, signal clustering and feature matching. Using two datasets of active power measurements with 1min and 8sec resolution, we demonstrate the effectiveness of the proposed method using a state-of-the-art NALM approaches as benchmarks

Blind non-intrusive appliance load monitoring using graph-based signal processing

With ongoing massive smart energy metering deployments, disaggregation of household's total energy consumption down to individual appliances using purely software tools, aka. non-intrusive appliance load monitoring (NALM), has generated increased interest. However, despite the fact that NALM was proposed over 30 years ago, there are still many open challenges. Indeed, the majority of approaches require training and are sensitive to appliance changes requiring regular re-training. In this paper, we tackle this challenge by proposing a 'blind' NALM approach that does not require any training. The main idea is to build upon an emerging field of graph-based signal processing to perform adaptive threshold-ing, signal clustering and feature matching. Using two datasets of active power measurements with 1min and 8sec resolution, we demonstrate the effectiveness of the proposed method using a state-of-the-art NALM approaches as benchmarks

Towards developing a reliable medical device for automated epileptic seizure detection in the ICU

Abstract. Epilepsy is a prevalent neurological disorder that affects millions of people globally, and its diagnosis typically involves laborious manual inspection of electroencephalography (EEG) data. Automated detection of epileptic seizures in EEG signals could potentially improve diagnostic accuracy and reduce diagnosis time, but there should be special attention to the number of false alarms to reduce unnecessary treatments and costs. This research presents a study on the use of machine learning techniques for EEG seizure detection with the aim of investigating the effectiveness of different algorithms in terms of high sensitivity and low false alarm rates for feature extraction, selection, pre-processing, classification, and post-processing in designing a medical device for detecting seizure activity in EEG data. The current state-of-the-art methods which are validated clinically using large amounts of data are introduced. The study focuses on finding potential machine learning methods, considering KNN, SVM, decision trees and, Random forests, and compares their performance on the task of seizure detection using features introduced in the literature. Also using ensemble methods namely, bootstrapping and majority voting techniques we achieved a sensitivity of 0.80 and FAR/h of 2.10, accuracy of 97.1% and specificity of 98.2%. Overall, the findings of this study can be useful for developing more accurate and efficient algorithms for EEG seizure detection medical device, which can contribute to the early diagnosis and treatment of epilepsy in the intensive care unit for critically ill patients

Sensing and Signal Processing in Smart Healthcare

In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included

Representation Learning: A Review and New Perspectives

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning