Weichen Wang

CrossCheck: Integrating self-report, behavioral sensing, and smartphone use to identify digital indicators of psychotic relapse.

Objective: This purpose of this study was to describe and demonstrate CrossCheck, a multimodal data collection system designed to aid in continuous remote monitoring and identification of subjective and objective indicators of psychotic relapse. Method: Individuals with schizophrenia-spectrum disorders received a smartphone with the monitoring system installed along with unlimited data plan for 12 months. Participants were instructed to carry the device with them and to complete brief self-reports multiple times a week. Multimodal behavioral sensing (i.e., physical activity, geospatials activity, speech frequency, and duration) and device use data (i.e., call and text activity, app use) were captured automatically. Five individuals who experienced psychiatric hospitalization were selected and described for instructive purposes. Results: Participants had unique digital indicators of their psychotic relapse. For some, self-reports provided clear and potentially actionable description of symptom exacerbation prior to hospitalization. Others had behavioral sensing data trends (e.g., shifts in geolocation patterns, declines in physical activity) or device use patterns (e.g., increased nighttime app use, discontinuation of all smartphone use) that reflected the changes they experienced more effectively. Conclusion: Advancements in mobile technology are enabling collection of an abundance of information that until recently was largely inaccessible to clinical research and practice. However, remote monitoring and relapse detection is in its nascence. Development and evaluation of innovative data management, modeling, and signal-detection techniques that can identify changes within an individual over time (i.e., unique relapse signatures) will be essential if we are to capitalize on these data to improve treatment and prevention.

Predicting Symptom Trajectories of Schizophrenia using Mobile Sensing

Continuously monitoring schizophrenia patients’ psychiatric symptoms is crucial for in-time intervention and treatment adjustment. The Brief Psychiatric Rating Scale (BPRS) is a survey administered by clinicians to evaluate symptom severity in schizophrenia. The CrossCheck symptom prediction system is capable of tracking schizophrenia symptoms based on BPRS using passive sensing from mobile phones. We present results from an ongoing randomized control trial, where passive sensing data, self-reports, and clinician administered 7-item BPRS surveys are collected from 36 outpatients with schizophrenia recently discharged from hospital over a period ranging from 2-12 months. We show that our system can predict a symptom scale score based on a 7-item BPRS within ±1.45 error on average using automatically tracked behavioral features from phones (e.g., mobility, conversation, activity, smartphone usage, the ambient acoustic environment) and user supplied self-reports. Importantly, we show our system is also capable of predicting an individual BPRS score within ±1.59 error purely based on passive sensing from phones without any self-reported information from outpatients. Finally, we discuss how well our predictive system reflects symptoms experienced by patients by reviewing a number of case studies.

Stereo matching with pixel classification and reliable disparity propagation

In this paper, we propose a novel high-speed stereo matching algorithm using pixel classification and reliable disparity propagation. While the research on stereo matching has such a long history and many state-of-art strategies have been introduced in recent years, the contradiction between the quality and the time consumes has not yet been solved. Our stereo method tackles this problem with two key contributions. First, we classify all the pixels into two categories: consecutive pixels and isolated pixels. When we perform matching cost aggregation, different supports are constructed for different types of pixels. For a consecutive pixel, an orthogonal local support skeleton is adaptively constructed. For an isolated pixel, we build an adaptive binary window. Second, we simultaneously conduct the matching cost aggregation and reliability detection. Once a reliable disparity is found, we propagate it to the whole support region. Experiments show that this algorithm can significantly reduce the computational complexity and ensure the accuracy of the result at the same time.

Combined hole-filling with spatial and temporal prediction

A combined hole-filling approach with spatial and temporal prediction is presented in this paper. Depth image-based rendering (DIBR) is generally used to synthesize virtual view images in free viewpoint television (FTV) and three-dimensional (3-D) video. Limited original camera views and depth maps are used to generate the additional virtual views in the synthesizing process. One of the main problems in DIBR is that there are some regions are occluded by the foreground objects in the original views, and they will be some holes in the generated additional virtual views, especially for the view extrapolation cases. Therefore, the proposed algorithm is introduced and it can be used to fill the holes which caused by disocclusion regions and inaccurate depth values. The proposed algorithm combines the spatial and temporal prediction, and the performance is much better and more stable than the previous work. The experimental results show that the proposed method can improve the quality of the virtual views a lot compared with the previous work. The improvement is not only obvious in the objective comparison, but also in the subjective comparison.

Fusing Mobile Phone Sensing and Brain Imaging to Assess Depression in College Students: A Proof-of-Concept study

As smartphone usage has become increasingly prevalent in our society, so have rates of depression, particularly among young adults. Individual differences in smartphone usage patterns have been shown to reflect individual differences in underlying affective processes such as depression (Wang et al., 2018). In the current study, we identified a positive relationship between smartphone screen time (e.g. phone unlock duration) and resting-state functional connectivity (RSFC) between the subgenual cingulate cortex (sgCC), a brain region implicated in depression and antidepressant treatment response, and regions of the ventromedial/orbitofrontal cortex, such that increased phone usage was related to stronger connectivity between these regions. We then used this cluster to constrain subsequent analyses looking at depressive symptoms in the same cohort and observed partial replication in a separate cohort. We believe the data and analyses presented here provide relatively simplistic initial analyses which replicate and provide a first step in combining functional brain activity and smartphone usage patterns to better understand issues related to mental health. Smartphones are a prevalent part of modern life and the usage of mobile sensing data from smartphones promises to be an important tool for mental health diagnostics and neuroscience research.

Frame compatible format fast encoder with stereo matching

In this paper, a frame compatible format fast encoder with stereo matching is proposed. Through packing the two neighboring views after down-sampling into one frame, the frame compatible format coding allows stereo video to be encoded on the conventional video applications. The matching of content similarity is the key of the fast encoder of Frame Compatible Format (FCF) H.264. Experiments show that the stereo matching can achieve a better result than simply using a shift obtaining method. This paper gives a statistical analysis of the prediction correlation between the two packed views in FCF by using different method to prove that the system can obtain much better results with stereo matching method. A modified fast stereo matching algorithm which is suitable for FCF fast encoding is introduced. Finally, the process and simulated results of stereo matching combined frame compatible format fast encoder is shown. Through the experimental result, the proposed algorithm can obtain a better result than the previous work. It can achieve better video quality, less PSNR lost and the lower bit rate increment.