Xueyi Wang

Xueyi Wang

Postdoc Researcher

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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

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Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 2

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Blog Post number 1

less than 1 minute read

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portfolio

publications

Elderly fall detection systems: A literature survey

Published in , 2020

Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial.

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Fall detection and recognition from egocentric visual data: A case study

Published in , 2021

Falling is among the most damaging events for elderly people, which sometimes may end with significant injuries. Due to fear of falling, many elderly people choose to stay more at home in order to feel safer. In this work, we propose a new fall detection and recognition approach, which analyses egocentric videos collected by wearable cameras through a computer vision/machine learning pipeline. More specifically, we conduct a case study with one volunteer who collected video data from two cameras; one attached to the chest and the other one attached to the waist. A total of 776 videos were collected describing four types of falls and nine kinds of non-falls. Our method works as follows: extracts several uniformly distributed frames from the videos, uses a pre-trained ConvNet model to describe each frame by a feature vector, followed by feature fusion and a classification model. Our proposed model demonstrates its suitability for the detection and recognition of falls from the data captured by the two cameras together. For this case study, we detect all falls with only one false positive, and reach a balanced accuracy of 93% in the recognition of the 13 types of activities. Similar results are obtained for videos of the two cameras when considered separately. Moreover, we observe better performance of videos collected in indoor scenes.

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Fall detection with a nonintrusive and first-person vision approach

Published in , 2023

Falls have been widely recognized as one of the most dangerous incidents for the elderly and other people with mobility limitations. This problem has attracted wide scientific interest, which has led to several investigations based on nonvision wearable sensors and static cameras. We investigate the challenge of fall detection and recognition using egocentric wearable cameras, which, besides portability and affordability, capture visual information that can be further leveraged for a broad set of lifelogging applications. In this work, five volunteers were equipped with two cameras each, one attached to the neck and the other to the waist. They were asked to simulate four kinds of falls and nine types of nonfalls. The newly collected dataset consists of 5858 short video clips, which we make available online. The proposed approach is a late fusion methodology that combines spatial and motion descriptors along with deep features extracted by a pretrained convolutional neural network. For the spatial and deep features, we consider the similarity of such features between frames in regular intervals of a given time window. In this way, it is the transition between such frames that are encoded in our approach, while the actual scene content does not play a role. We design several experiments to investigate the best camera location and performance for indoor and outdoor activities and employ leave-one-subject-out cross-validation to test the generalization ability of our approach. For the fall detection (i.e., two-class) problem, our approach achieves 91.8% accuracy, 93.6% sensitivity, and 89.2% specificity.

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Multivariate Stress Forecast from Sparse Data during Lifestyle Interventions

Published in , 2024

We developed a protocol using a commercial Garmin smartwatch for stress forecasting, validated through a leave-one-subject-out approach, demonstrating robustness and effective use of sparse data from wearable devices. The model excels with a one-day prediction window and a three-day training window, sacrificing a bit of performance over extended predicting windows. This demonstrates the potential of wearable technology for noninvasive, real-time health management. Our findings proved the adaptability of our model to senior populations and its practicality for individuals requiring continuous stress monitoring. This work contributes significantly to the development of accessible, proactive mental health tools and sets a foundation for future research into enhancing the accuracy and responsiveness of stress forecasting.

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Egofalls: a visual-audio dataset and benchmark for fall detection using egocentric cameras

Published in , 2024

Falls are significant and often fatal for vulnerable populations such as the elderly. Previous work has addressed the detection of falls by relying on data captured by single sensors, images, or accelerometers. Firstly, we collected and published a new dataset on which we assess our proposed approach. We believe this to be the first public dataset of its kind. The dataset comprises 10,948 video samples from 14 subjects. Additionally, we relied on multimodal descriptors extracted from videos captured by egocentric cameras. Our proposed method includes a late decision fusion layer that builds on top of the extracted descriptors. We conducted ablation experiments to assess the performance of individual feature extractors, the fusion of visual information, and the fusion of both visual and audio information. Moreover, we experimented with internal and external cross-validation. Our results demonstrate that the fusion of audio and visual information through late decision fusion improves detection performance, making it a promising tool for fall prevention and mitigation.

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Personalized Sleep Prediction via Deep Adaptive Spatiotemporal Modeling and sparse data

Published in , 2025

A sleep forecast allows individuals, and healthcare providers to anticipate and proactively address factors influencing restful rest, ultimately improving mental and physical well-being. This work presents an adaptive spatial and temporal model (AdaST-Sleep) for predicting sleep scores. Our proposed model combines convolutional layers to capture spatial feature interactions between multiple features and recurrent neural network layers to handle longer-term temporal dependencies. A domain classifier is further integrated to generalize across different subjects. We conducted several experiments using five input window sizes (3, 5, 7, 9, 11 days) and five predicting window sizes (1, 3, 5, 7, 9 days). Our approach consistently outperformed four baseline models, achieving its lowest RMSE (0.282) with a seven-day input window and a one-day predicting window. Moreover, the method maintained strong performance even when forecasting multiple days into the future, demonstrating its versatility for real-world applications. Visual comparisons reveal that the model accurately tracks both the overall sleep score level and daily fluctuations. These findings prove that the proposed framework provides a robust and adaptable solution for personalized sleep forecasting using sparse data from commercial wearable devices and domain adaptation techniques.

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