Publications

From 2020 (Clear Search)

“Notice: FX Palo Alto Laboratory will be closing. All Research and related operations will cease as of June 30, 2020.”

2020

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Managing post-surgical pain is critical for successful surgical outcomes. One of the challenges of pain management is accurately assessing the pain level of patients. Self-reported numeric pain ratings are limited because they are subjective, can be affected by mood, and can influence the patient’s perception of pain when making comparisons. In this paper, we introduce an approach that analyzes 2D and 3D facial keypoints of post-surgical patients to estimate their pain intensity level.Our approach leverages the previously unexplored capabilities of a smartphone to capture a dense3D representation of a person’s face as input for pain intensity level estimation. Our contributions are a data collection study with post-surgical patients to collect ground-truth labeled sequences of2D and 3D facial keypoints for developing a pain estimation algorithm, a pain estimation model that uses multiple instance learning to overcome inherent limitations in facial keypoint sequences, and the preliminary results of the pain estimation model using 2D and 3D features with comparisons of alternate approaches.

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Flow-like experiences at work are important for productivity and worker well-being. However, it is difficult to objectively detect when workers are experiencing flow in their work. In this paper, we investigate how to predict a worker's focus state based on physiological signals. We conducted a lab study to collect physiological data from knowledge workers experienced different levels of flow while performing work tasks. We used the nine characteristics of flow to design tasks that would induce different focus states. A manipulation check using the Flow Short Scale verified that participants experienced three distinct flow states, one overly challenging non-flow state, and two types of flow states, balanced flow, and automatic flow. We built machine learning classifiers that can distinguish between non-flow and flow states with 0.889 average AUC and rest states from working states with 0.98 average AUC. The results show that physiological sensing can detect focused flow states of knowledge workers and can enable ways to for individuals and organizations to improve both productivity and worker satisfaction.

Interpretable Contrastive Learning for Networks

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  • arXiv
  • Jun 3, 2020

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Contrastive learning (CL) is an emerging analysis approach that aims to discover unique patterns in one dataset relative to another. By applying this approach to network analysis, we can reveal unique characteristics in one network by contrasting with another. For example, with networks of protein interactions obtained from normal and cancer tissues, we can discover unique types of interactions in cancer tissues. However, existing CL methods cannot be directly applied to networks. To address this issue, we introduce a novel approach called contrastive network representation learning (cNRL). This approach embeds network nodes into a low-dimensional space that reveals the uniqueness of one network compared to another. Within this approach, we also propose a method, named i-cNRL, that offers interpretability in the learned results, allowing for understanding which specific patterns are found in one network but not the other. We demonstrate the capability of i-cNRL with multiple network models and real-world datasets. Furthermore, we provide quantitative and qualitative comparisons across i-cNRL and other potential cNRL algorithm designs.
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  • Personal and Ubiquitous Computing
  • May 31, 2020

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An important capability of most smart, Internet-of-Things-enabled spaces (e.g., office, home, hospital, factory) is the ability to leverage context of use. This can support social awareness, allowing people to interact more effectively which each other. Location is a key context element; particularly indoor location. Recent advances in radio ranging technologies, such as 802.11-2016 FTM, promise the availability of low-cost, near-ubiquitous time-of-flight-based ranging estimates. In this paper, we build on prior work to enhance this ranging technology's ability to provide useful location estimates. For further improvements, we model user-motion behavior to estimate the user motion state by taking the temporal measurements available from time-of-flight ranging. We select the velocity parameter of a particle-filter-based on this motion state. We demonstrate meaningful improvements in coordinate-based estimation accuracy and substantial increases in room-level estimation accuracy. Furthermore, insights gained in our real-world deployment provides important implications for future Internet of Things context applications and their supporting technology deployments such as social interaction, workflow management, inventory control, or healthcare information tools.
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  • CHI 2020
  • Apr 25, 2020

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The demands of daily work offer few opportunities for workers to take stock of their own progress, big or small, which can lead to lower motivation, engagement, and higher risk of burnout. We present Highlight Matome, a personal online tool that encourages workers to quickly record and rank a single work highlight each day, helping them gain awareness of their own successes. We describe results from a field experiment investigating our tool's effectiveness for improving workers' engagement, perceptions, and affect. Thirty-three knowledge workers in Japan and the U.S. used Highlight Matome for six weeks. Our results show that using our tool for less than one minute each day significantly increased measures of work engagement, dedication, and positivity. A qualitative analysis of the highlights offers a window into participants' emotions and perceptions. We discuss implications for theories of inner work life and worker well-being.

Social VR: A New Medium for RemoteCommunication and Collaboration

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  • CHI 2020
  • Apr 25, 2020

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There is a growing need for effective remote communication, which has many positive societal impacts, such as reducing environmental pollution and travel costs, supporting rich collaboration by remotely connecting talented people. Social Virtual Reality (VR) invites multiple users to join a collaborative virtual environment, which creates new opportunities for remote communication. The goal of social VR is not to completely replicate reality, but to facilitate and extend the existing communication channels of the physical world. Apart from the benefits provided by social VR, privacy concerns and ethical risks are raised when the boundary between the real and the virtual world is blurred. This workshop is intended to spur discussions regarding technology, evaluation protocols, application areas, research ethics and legal regulations for social VR as an emerging immersive remote communication tool.

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While it is often critical for indoor-location- and proximity-aware applications to know whether a user is in a space or not (e.g., a specific room or office), a key challenge is that the difference between standing on one side or another of a doorway or wall is well within the error range of most RF-based approaches. In this work, we address this challenge by augmenting RF-based localization and proximity detection with active ultrasonic sensing, taking advantage of the limited propagation of sound waves. This simple and cost-effective approach can allow, for example, a Bluetooth smart-lock to discern whether a user is inside or outside their home. We describe a configurable architecture for our solution and present experiments that validate this approach but also demonstrate that different user behavior and application needs can impact system configuration decisions. Finally, we describe applications that could benefit from our solution and address privacy concerns.