Privacy-Preserving Action Recognition for Smart Hospitals using Low-Resolution Depth Images

Chou, Edward; Tan, Matthew; Zou, Cherry; Guo, Michelle; Haque, Albert; Milstein, Arnold; Li, Feifei

doi:10.48550/arxiv.1811.09950

Cited by 10 publications

(11 citation statements)

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“…Such systems have been able to accurately classify patients' high-level activities such as nothing in bed (doing nothing, lying in bed), in-bed activity, out-of-bed activity , and walking ( 30 ), and postures such as lying in bed, sitting on the bed, sitting on the chair , and standing ( 22 ) in the ICU. Depth camera-based systems have been able to classify four provider activities: “moving the patient into and out of bed” and “moving the patient into and out of a chair” without incorporating the challenging step of patient recognition ( 48 , 49 ).…”

Section: Physical Function Monitoringmentioning

confidence: 99%

Potentials and Challenges of Pervasive Sensing in the Intensive Care Unit

Davoudi

Shickel

Tighe

et al. 2022

Front. Digit. Health

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Patients in critical care settings often require continuous and multifaceted monitoring. However, current clinical monitoring practices fail to capture important functional and behavioral indices such as mobility or agitation. Recent advances in non-invasive sensing technology, high throughput computing, and deep learning techniques are expected to transform the existing patient monitoring paradigm by enabling and streamlining granular and continuous monitoring of these crucial critical care measures. In this review, we highlight current approaches to pervasive sensing in critical care and identify limitations, future challenges, and opportunities in this emerging field.

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Section: Physical Function Monitoringmentioning

confidence: 99%

Potentials and Challenges of Pervasive Sensing in the Intensive Care Unit

Davoudi

Shickel

Tighe

et al. 2022

Front. Digit. Health

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“…It goes without saying that manual efforts to alleviate issues are costly and not scalable. In [7], Chou et al…”

Section: Related Workmentioning

confidence: 99%

Activity Detection in Long Surgical Videos using Spatio-Temporal Models

He¹,

Mottaghi²,

Sharghi³

et al. 2022

Preprint

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Automatic activity detection is an important component for developing technologies that enable next generation surgical devices and workflow monitoring systems. In many application, the videos of interest are long and include several activities; hence, the deep models designed for such purposes consist of a backbone and a temporal sequence modeling architecture. In this paper, we investigate both the state-of-the-art activity recognition and temporal models to find the architectures that yield the highest performance. We first benchmark these models on a large-scale activity recognition dataset in the operating room with over 800 full-length surgical videos. However, since most other medical applications lack such a large dataset, we further evaluate our models on the Cholec80 surgical phase segmentation dataset, consisting of only 40 training videos. For backbone architectures, we investigate both 3D Con-vNets and most recent transformer-based models; for temporal modeling, we include temporal ConvNets, RNNs, and transformer models for a comprehensive and thorough study. We show that even in the case of limited labeled data, we can outperform the existing work by benefiting from models pre-trained on other tasks.

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“…Depth sensors are used in hospitals to monitor hand hygiene compliance using CNNs [12] and detect patient mobility [13,14]. In [15], a super-resolution model is used to enhance healthcare assist in smart hospitals, and surgical phase recognition in laparoscopic videos is performed in [11,16]. However, recognizing surgical activities in the OR has not been studied due to lack of data.…”

Section: Related Workmentioning

confidence: 99%

Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery

Sharghi¹,

Haugerud²,

Oh³

et al. 2020

Preprint

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Automatic recognition of surgical activities in the operating room (OR) is a key technology for creating next generation intelligent surgical devices and workflow monitoring/support systems. Such systems can potentially enhance efficiency in the OR, resulting in lower costs and improved care delivery to the patients. In this paper, we investigate automatic surgical activity recognition in robot-assisted operations. We collect the first large-scale dataset including 400 full-length multiperspective videos from a variety of robotic surgery cases captured using Time-of-Flight cameras. We densely annotate the videos with 10 most recognized and clinically relevant classes of activities. Furthermore, we investigate state-of-the-art computer vision action recognition techniques and adapt them for the OR environment and the dataset. First, we finetune the Inflated 3D ConvNet (I3D) for clip-level activity recognition on our dataset and use it to extract features from the videos. These features are then fed to a stack of 3 Temporal Gaussian Mixture layers which extracts context from neighboring clips, and eventually go through a Long Short Term Memory network to learn the order of activities in full-length videos. We extensively assess the model and reach a peak performance of ∼88% mean Average Precision.

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Privacy-Preserving Action Recognition for Smart Hospitals using Low-Resolution Depth Images

Cited by 10 publications

References 0 publications

Potentials and Challenges of Pervasive Sensing in the Intensive Care Unit

Potentials and Challenges of Pervasive Sensing in the Intensive Care Unit

Activity Detection in Long Surgical Videos using Spatio-Temporal Models

Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery

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