It's About Time: Analog Clock Reading in the Wild

Yang, Charig; Xie, Weidi; Zisserman, Andrew

doi:10.1109/cvpr52688.2022.00254

Cited by 10 publications

(3 citation statements)

References 19 publications

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“…If the clock numbers are complete and distributed clockwise, the score is 1 or 0 otherwise, as shown in Figure 3(a) . We used a clock identification architecture from the space converter network (STN) 53 and recorded clock time scores in binary. If the clock time is recognized as 11:10, the score is 1 or 0 otherwise, as shown in Figure 3(b) .…”

Section: Methodsmentioning

confidence: 99%

A new early warning method for mild cognitive impairment due to Alzheimer's disease based on dynamic evaluation of the “spatial executive process”

Li,

Ma,

Chen

et al. 2023

DIGITAL HEALTH

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Objective Mild cognitive impairment (MCI) due to Alzheimer's disease (AD), as an early stage of AD, is an important point for early warning of AD. Neuropathological studies have shown that AD pathology in pre-dementia patients involves the hippocampus and caudate nucleus, which are responsible for controlling cognitive mechanisms such as the spatial executive process (SEP). The aim of this study is to design a new method for early warning of MCI due to AD by dynamically evaluating SEP. Methods We designed fingertip interaction handwriting digital evaluation paradigms and analyzed the dynamic trajectory of fingertip interaction and image data during “clock drawing” and “repetitive writing” tasks. Extracted fingertip interaction digital biomarkers were used to assess participants’ SEP disorders, ultimately enabling intelligent diagnosis of MCI due to AD. A cross-sectional study demonstrated the predictive performance of this new method. Results We enrolled 30 normal cognitive (NC) elderly and 30 MCI due to AD patients, and clinical research results showed that there may be neurobehavioral differences between the two groups in digital biomarkers captured during SEP. The early warning performance for MCI due to AD of this new method (areas under the curve (AUC) = 0.880) is better than that of the Minimum Mental State Examination (MMSE) neuropsychological scale (AUC = 0.856) assessed by physicians. Conclusion Patients with MCI due to AD may have SEP disorders, and this new method based on dynamic evaluation of SEP will provide a novel human–computer interaction and intelligent early warning method for home and community screening of MCI due to AD.

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Section: Methodsmentioning

confidence: 99%

A new early warning method for mild cognitive impairment due to Alzheimer's disease based on dynamic evaluation of the “spatial executive process”

Li,

Ma,

Chen

et al. 2023

DIGITAL HEALTH

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show abstract

“…Existing object detectors can detect and recognise common object categories, but can also be easily re-trained to detect novel object categories. This capability to learn new objects has enabled the widespread application of detectors to a range of tasks including detection of illustrations in early printed books (Abhishek et al, 2021), chimpanzees in the wild (Bain et al, 2021), and the position of analog clocks in photographs (Yang et al, 2022). Here we have re-trained object detectors to automatically detect Picasso triggerfish and cylindrical obstacles.…”

Section: Behavioural Experimentsmentioning

confidence: 99%

Detect+Track: Robust and flexible software tools for improved tracking and behavioural analysis of fish

Dutta

Perez-Campanero

Taylor

et al. 2023

Preprint

Self Cite

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Developments in automated animal tracking software are increasing the efficiency of data collection and improving the standardization of behavioural measurements. There are now several open-source tools for tracking laboratory animals, but often these are only accurate under limited conditions (e.g. uniform lighting and background, uncluttered scenes, unobstructed focal animal). Tracking fish presents a particular challenge for these tools because movement at the water's surface introduces significant noise. Partial occlusion of the focal animal can also be troublesome, particularly when tracking the whole organism. We conducted a behavioural experiment that required us to track the trajectory of a fish as it swam through a field of obstacles. In addition to measuring the body's trajectory, we also needed to record the position of the obstacles, and to identify when the fish passed through the "virtual gates" between adjacent obstacles and/or the aquarium wall. We automated data collection by employing a range of computer vision and computational geometry algorithms (e.g. object detection and tracking, optical flow, parallel plane homology mapping, Voronoi tessellation). Our workflow is divided into several discrete steps, and provides a set of modular software building blocks that can be adapted to analyse other experimental designs. A detailed tutorial is provided, together with all the data and code required to reproduce our results.

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“…Optical character recognition (OCR) technology was used to recognize the numbers written by the participants and the results was recorded in a binary way. As shown in figure 4, the Space Converter Network (STN) was used to identify the clock architecture [20] to achieve end-to-end clock alignment and recognition training. The real clock picture was used to further narrow the gap between the simulation and real data, so as to realize the clock time reading and record the results in a binary way.…”

Section: Figure 3 Contour Edge Detection Processmentioning

confidence: 99%

A New Early Warning Method for Human-Computer Interaction of Alzheimer's Disease Patients Based on Deep Learning

Wang¹,

Wang²,

Wu³

et al. 2022

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Alzheimer's disease (AD), an incurable disease, poses a major health problem. It is important to identify patients with mild cognitive impairment (MCI) and early AD. Clock rendering test (CDT) is an effect way to screen AD patients quickly in the community. However, the current CDT methods require specific equipment to collect features, and the existing prediction models are inefficient in early warning of MCI. To solve the problem, this paper replaces digital pen with fingertip interaction, and proposes an early warning model for AD early dCDT images based on ResNet50. The dCDT tests were carried out on normal cognitive elderly, MCI patients and mild AD patients, and the results were used to verify the analysis and classification ability of the ResNet50-based early AD prediction model, in contrast to the clock score-based early AD prediction model. The comparison shows that the ResNet50-based early AD prediction model is efficient in early warning than the other model, and is suitable for large-scale screening of AD patients in the community, in the absence of doctors.

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It's About Time: Analog Clock Reading in the Wild

Cited by 10 publications

References 19 publications

A new early warning method for mild cognitive impairment due to Alzheimer's disease based on dynamic evaluation of the “spatial executive process”

A new early warning method for mild cognitive impairment due to Alzheimer's disease based on dynamic evaluation of the “spatial executive process”

Detect+Track: Robust and flexible software tools for improved tracking and behavioural analysis of fish

A New Early Warning Method for Human-Computer Interaction of Alzheimer's Disease Patients Based on Deep Learning

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