The use of smart devices (e.g., smartphones, smartwatches) and other wearables to deliver digital interventions to improve health outcomes has grown significantly in the past few years. Mobile health (mHealth) systems are excellent tools for the delivery of adaptive interventions that aim to provide the right type and amount of support, at the right time, by adapting to an individual's changing context. Micro-randomized trials (MRTs) are an increasingly common experimental design that is the main source for data-driven evidence of mHealth intervention effectiveness. To assess time-varying causal effect moderation in an MRT, individuals are intensively randomized to receive treatment over time. In addition, measurements, including individual characteristics, and context are also collected throughout the study. The effective utilization of covariate information to improve inferences regarding causal effects has been well-established in the context of randomized control trials (RCTs), where covariate adjustment is applied to leverage baseline data to address chance imbalances and improve the asymptotic efficiency of causal effect estimation. However, the application of this approach to longitudinal data, such as MRTs, has not been thoroughly explored. Recognizing the connection to Neyman Orthogonality, we propose a straightforward and intuitive method to improve the efficiency of moderated causal excursion effects by incorporating auxiliary variables. We compare the robust standard errors of our method with those of the benchmark method. The efficiency gain of our approach is demonstrated through simulation studies and an analysis of data from the Intern Health Study (NeCamp et al., 2020).
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Soft robotics is an emergent and swiftly evolving field. Pneumatic actuators are suitable for driving soft robots because of their superior performance. However, their control is not easy due to their hysteresis characteristics. In response to these challenges, we propose an adaptive control method to compensate hysteresis of a soft actuator. Employing a novel dual pneumatic artificial muscle (PAM) bending actuator, the innovative control strategy abates hysteresis effects by dynamically modulating gains within a traditional PID controller corresponding with the predicted motion of the reference trajectory. Through comparative experimental evaluation, we found that the new control method outperforms its conventional counterparts regarding tracking accuracy and response speed. Our work reveals a new direction for advancing control in soft actuators.
In online conferencing applications, estimating the perceived quality of an audio signal is crucial to ensure high quality of experience for the end user. The most reliable way to assess the quality of a speech signal is through human judgments in the form of the mean opinion score (MOS) metric. However, such an approach is labor intensive and not feasible for large-scale applications. The focus has therefore shifted towards automated speech quality assessment through end-to-end training of deep neural networks. Recently, it was shown that leveraging pre-trained wav2vec-based XLS-R embeddings leads to state-of-the-art performance for the task of speech quality prediction. In this paper, we perform an in-depth analysis of the pre-trained model. First, we analyze the performance of embeddings extracted from each layer of XLS-R and also for each size of the model (300M, 1B, 2B parameters). Surprisingly, we find two optimal regions for feature extraction: one in the lower-level features and one in the high-level features. Next, we investigate the reason for the two distinct optima. We hypothesize that the lower-level features capture characteristics of noise and room acoustics, whereas the high-level features focus on speech content and intelligibility. To investigate this, we analyze the sensitivity of the MOS predictions with respect to different levels of corruption in each category. Afterwards, we try fusing the two optimal feature depths to determine if they contain complementary information for MOS prediction. Finally, we compare the performance of the proposed models and assess the generalizability of the models on unseen datasets.
Following the successful debut of polyp detection and characterization, more advanced automation tools are being developed for colonoscopy. The new automation tasks, such as quality metrics or report generation, require understanding of the procedure flow that includes activities, events, anatomical landmarks, etc. In this work we present a method for automatic semantic parsing of colonoscopy videos. The method uses a novel DL multi-label temporal segmentation model trained in supervised and unsupervised regimes. We evaluate the accuracy of the method on a test set of over 300 annotated colonoscopy videos, and use ablation to explore the relative importance of various method's components.
Since wearable linkage mechanisms could control the moment transmission from actuator(s) to wearers, they can help ensure that even low-cost wearable systems provide advanced functionality tailored to users' needs. For example, if a hip mechanism transforms an input torque into a spatially-varying moment, a wearer can get effective assistance both in the sagittal and frontal planes during walking, even with an affordable single-actuator system. However, due to the combinatorial nature of the linkage mechanism design space, the topologies of such nonlinear-moment-generating mechanisms are challenging to determine, even with significant computational resources and numerical data. Furthermore, on-premise production development and interactive design are nearly impossible in conventional synthesis approaches. Here, we propose an innovative autonomous computational approach for synthesizing such wearable robot mechanisms, eliminating the need for exhaustive searches or numerous data sets. Our method transforms the synthesis problem into a gradient-based optimization problem with sophisticated objective and constraint functions while ensuring the desired degree of freedom, range of motion, and force transmission characteristics. To generate arbitrary mechanism topologies and dimensions, we employed a unified ground model. By applying the proposed method for the design of hip joint mechanisms, the topologies and dimensions of non-series-type hip joint mechanisms were obtained. Biomechanical simulations validated its multi-moment assistance capability, and its wearability was verified via prototype fabrication. The proposed design strategy can open a new way to design various wearable robot mechanisms, such as shoulders, knees, and ankles.
In human-robot collaboration, unintentional physical contacts occur in the form of collisions and clamping, which must be detected and classified separately for a reaction. If certain collision or clamping situations are misclassified, reactions might occur that make the true contact case more dangerous. This work analyzes data-driven modeling based on physically modeled features like estimated external forces for clamping and collision classification with a real parallel robot. The prediction reliability of a feedforward neural network is investigated. Quantification of the classification uncertainty enables the distinction between safe versus unreliable classifications and optimal reactions like a retraction movement for collisions, structure opening for the clamping joint, and a fallback reaction in the form of a zero-g mode. This hypothesis is tested with experimental data of clamping and collision cases by analyzing dangerous misclassifications and then reducing them by the proposed uncertainty quantification. Finally, it is investigated how the approach of this work influences correctly classified clamping and collision scenarios.
We consider a network of smart sensors for an edge computing application that sample a time-varying signal and send updates to a base station for remote global monitoring. Sensors are equipped with sensing and compute, and can either send raw data or process them on-board before transmission. Limited hardware resources at the edge generate a fundamental latency-accuracy trade-off: raw measurements are inaccurate but timely, whereas accurate processed updates are available after processing delay. Hence, one needs to decide when sensors should transmit raw measurements or rely on local processing to maximize network monitoring performance. To tackle this sensing design problem, we model an estimation-theoretic optimization framework that embeds both computation and communication latency, and propose a Reinforcement Learning-based approach that dynamically allocates computational resources at each sensor. Effectiveness of our proposed approach is validated through numerical experiments motivated by smart sensing for the Internet of Drones and self-driving vehicles. In particular, we show that, under constrained computation at the base station, monitoring performance can be further improved by an online sensor selection.
Phase information has a significant impact on speech perceptual quality and intelligibility. However, existing speech enhancement methods encounter limitations in explicit phase estimation due to the non-structural nature and wrapping characteristics of the phase, leading to a bottleneck in enhanced speech quality. To overcome the above issue, in this paper, we proposed MP-SENet, a novel Speech Enhancement Network which explicitly enhances Magnitude and Phase spectra in parallel. The proposed MP-SENet adopts a codec architecture in which the encoder and decoder are bridged by time-frequency Transformers along both time and frequency dimensions. The encoder aims to encode time-frequency representations derived from the input distorted magnitude and phase spectra. The decoder comprises dual-stream magnitude and phase decoders, directly enhancing magnitude and wrapped phase spectra by incorporating a magnitude estimation architecture and a phase parallel estimation architecture, respectively. To train the MP-SENet model effectively, we define multi-level loss functions, including mean square error and perceptual metric loss of magnitude spectra, anti-wrapping loss of phase spectra, as well as mean square error and consistency loss of short-time complex spectra. Experimental results demonstrate that our proposed MP-SENet excels in high-quality speech enhancement across multiple tasks, including speech denoising, dereverberation, and bandwidth extension. Compared to existing phase-aware speech enhancement methods, it successfully avoids the bidirectional compensation effect between the magnitude and phase, leading to a better harmonic restoration. Notably, for the speech denoising task, the MP-SENet yields a state-of-the-art performance with a PESQ of 3.60 on the public VoiceBank+DEMAND dataset.
The proliferation of consumer health devices such as smart watches, sleep monitors, smart scales, etc, in many countries, has not only led to growing interest in health monitoring, but also to the development of a countless number of ``smart'' applications to support the exploration of such data by members of the general public, sometimes with integration into professional health services. While a variety of health data streams has been made available by such devices to users, these streams are often presented as separate time-series visualizations, in which the potential relationships between health variables are not explicitly made visible. Furthermore, despite the fact that other aspects of life, such as work and social connectivity, have become increasingly digitised, health and well-being applications make little use of the potentially useful contextual information provided by widely used personal information management tools, such as shared calendar and email systems. This paper presents a framework for the integration of these diverse data sources, analytic and visualization tools, with inference methods and graphical user interfaces to help users by highlighting causal connections among such time-series.
Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks is typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as the graph-structured data with high dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many studies on extending deep neural networks for graph-structured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNNs structures (e.g., graph convolutional networks, graph recurrent neural networks, graph attention networks, graph generative networks, spatial-temporal graph convolutional networks, and hybrid forms of GNNs) are summarized, and key applications in power systems such as fault diagnosis, power prediction, power flow calculation, and data generation are reviewed in detail. Furthermore, main issues and some research trends about the applications of GNNs in power systems are discussed.
Object detection typically assumes that training and test data are drawn from an identical distribution, which, however, does not always hold in practice. Such a distribution mismatch will lead to a significant performance drop. In this work, we aim to improve the cross-domain robustness of object detection. We tackle the domain shift on two levels: 1) the image-level shift, such as image style, illumination, etc, and 2) the instance-level shift, such as object appearance, size, etc. We build our approach based on the recent state-of-the-art Faster R-CNN model, and design two domain adaptation components, on image level and instance level, to reduce the domain discrepancy. The two domain adaptation components are based on H-divergence theory, and are implemented by learning a domain classifier in adversarial training manner. The domain classifiers on different levels are further reinforced with a consistency regularization to learn a domain-invariant region proposal network (RPN) in the Faster R-CNN model. We evaluate our newly proposed approach using multiple datasets including Cityscapes, KITTI, SIM10K, etc. The results demonstrate the effectiveness of our proposed approach for robust object detection in various domain shift scenarios.
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