Watching the TV Watchers

Zhang, Yun C.; Rehg, James M.

doi:10.1145/3214291

Cited by 8 publications

(14 citation statements)

References 40 publications

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“…They reported scores of AUC of 0.90 for detecting a TV screen, 0.89 for detecting a computer screen, 0.83 for detecting both screens are present near the device. Zhang & Regh [ 35 ] tested a head-mounted wearable camera compared to eye tracking glasses, TV detector and video observation. They used machine learning algorithms to analyze and classify the video recorded by the wearable camera to identify when the participant was watching TV, or a variety of screens.…”

Section: Resultsmentioning

confidence: 99%

Validated assessment tools for screen media use: A systematic review

et al. 2023

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Objective Accurate measurement of adult and child screen media use are needed to robustly assess its impact on health outcomes. Our objective was to systematically review screen media use measurement tools that have been validated against an objective “gold standard” tool. Methods The search strategy was initially conducted in Medline Ovid and translated to Embase, Web of Science, PsychInfo and Cochrane. A modified natural language search was conducted in Google Scholar and IEEE. The initial search was conducted in March 2021, and an updated search was conducted in June 2022. Additional studies were included from the references. Studies had to describe the validation of a tool to measure screen media use on participants of any age against a ‘gold standard’ or comparable objective measure. The COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) was used to assess the criterion validity. Four authors reviewed the titles in two rounds and extracted data. Results Twenty-nine articles were included in the review. Studies measured TV, computer, mobile device and social media site screen media use through: self or parent report, direct or video observation, computer and mobile device use tracking programs, and through other novel devices such as wearable devices and cameras. Correlations of self or parent report of screen media with the gold standard were lower than correlations of technology-based measures, and video observation with the gold standard. The COSMIN criterion validity ratings ranged from poor to excellent; most of the studies received a global score of fair or poor. Conclusions Technology based validated tools that more directly measure screen use are emerging that have been validated against a gold standard for measuring screen use. However, practical, objective measures of diverse types of screen media use that have been tested on diverse populations are needed to better understand the impact of screen media use on the development and physical and mental health of children and adults.

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Section: Resultsmentioning

confidence: 99%

Validated assessment tools for screen media use: A systematic review

et al. 2023

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“…The players of the marketing are aware of the concept that changes in viewer routine and usage patterns are the essential rules to redefine the business model for the next coming years; due to this, the demand for user behavior, experiences, and usage patterns are growing [62]. Different approaches such as computer vision, machine learning, recording and observing in the lab environment, human interaction, surveys, and content analysis have been used to measure the viewers' behavior patterns [63][64][65][66][67][68]. The data collected through the logging system is more robust and authentic than the qualitative research studies such as surveys and interviews [58].…”

Section: Viewers Watching Behaviorsmentioning

confidence: 99%

Smart TV-Based Lifelogging Systems: Current Trends, Challenges, and the Road Ahead

Khan

Khusro

Alam

2021

Information and Knowledge in Internet of Things

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The phenomena of lifelogging can be described as "to digitally record the people's daily-life activities and events in varying amounts of detail" [1]. The lifelogging systems use various tools, applications, and devices, such as desktop computers, laptops, smartphones, wearable smart devices, biometric devices, and process the data for explaining the individual's daily-life activities [2]. This recorded information can offer the potential to mine or infer knowledge about life experiences and used for a variety of purposes including contextual recommendation, e-commerce, monitoring, memory augmentation, linking, summarization, contextual retrieval, browsing, the judicial system, searching, adaptive user interface, and analyzing user profiles [3]. Usually, these personalized devices captured a single user's events to help predict and find out different patterns, such as healthcare services, social network services, intelligent mirror services, and recommendation services. Moreover, a user must be actively involved during the lifelogging [4].Lifelogging is a form of pervasive computing. Users can digitally record, capture, store, archive, and process the individuals' complete life experiences in the form of multimodal through different sensors [5]. The main idea of lifelogging to enhance the way people record and exchange their data, information, communication with others, and log into tools or devices [6]. Besides, the concept of lifelogging, to gain knowledge and data about someone, is just like automated biography. However, the lifelogging applications/tools of these devices have been used for single-user life experiences. For example, lifelogging applications and computer tools, such as Latent [7], have their own perspective of live experiences. However, these

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“…In this work, we focus on activity recognition from visual lifelogs. In contrast with egocentric videos, they cover longer time periods with a low temporal resolution, hence being suitable for several applications of assistance technology [2]- [6]. Nevertheless, most of the approaches described above cannot be used on visual lifelogs because motion and gaze based features cannot be reliably estimated on such data.…”

Section: B Activity Recognition From Egocentric Videosmentioning

confidence: 99%

“…As a consequence, activity recognition from wearable cameras has several important applications as assistive technology, in particular in the field of rehabilitation and preventive medicine. Examples include self-monitoring of ambulatory activities of elderly people [2], [3], monitoring patients suffering dementia [4], [5], determining sedentary behavior of a user based on their spent time watching TV [6].…”

Section: Introductionmentioning

confidence: 99%

Activities of Daily Living Monitoring via a Wearable Camera: Toward Real-World Applications

2020

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Activity recognition from wearable photo-cameras is crucial for lifestyle characterization and health monitoring. However, to enable its wide-spreading use in real-world applications, a high level of generalization needs to be ensured on unseen users. Currently, state-of-the-art methods have been tested only on relatively small datasets consisting of data collected by a few users that are partially seen during training. In this paper, we built a new egocentric dataset acquired by 15 people through a wearable photo-camera and used it to test the generalization capabilities of several state-of-the-art methods for egocentric activity recognition on unseen users and daily image sequences. In addition, we propose several variants to state-of-the-art deep learning architectures, and we show that it is possible to achieve 79.87% accuracy on users unseen during training. Furthermore, to show that the proposed dataset and approach can be useful in real-world applications, where data can be acquired by different wearable cameras and labeled data are scarcely available, we employed a domain adaptation strategy on two egocentric activity recognition benchmark datasets. These experiments show that the model learned with our dataset, can easily be transferred to other domains with a very small amount of labeled data. Taken together, those results show that activity recognition from wearable photo-cameras is mature enough to be tested in real-world applications. INDEX TERMS Daily activity recognition, visual lifelogs, domain adaptation, wearable cameras.

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Watching the TV Watchers

Cited by 8 publications

References 40 publications

Validated assessment tools for screen media use: A systematic review

Validated assessment tools for screen media use: A systematic review

Smart TV-Based Lifelogging Systems: Current Trends, Challenges, and the Road Ahead

Activities of Daily Living Monitoring via a Wearable Camera: Toward Real-World Applications

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