Summary
A systematic review of randomized controlled trials was conducted to evaluate the effectiveness of eHealth interventions for the prevention and treatment of overweight and obesity in adults. Eight databases were searched for studies published in English from 1995 to 17 September 2014. Eighty-four studies were included, with 183 intervention arms, of which 76% (n = 139) included an eHealth component. Sixty-one studies had the primary aim of weight loss, 10 weight loss maintenance, eight weight gain prevention, and five weight loss and maintenance. eHealth interventions were predominantly delivered using the Internet, but also email, text messages, monitoring devices, mobile applications, computer programs, podcasts and personal digital assistants. Forty percent (n = 55) of interventions used more than one type of technology, and 43.2% (n = 60) were delivered solely using eHealth technologies. Meta-analyses demonstrated significantly greater weight loss (kg) in eHealth weight loss interventions compared with control (MD −2.70 [−3.33,−2.08], P < 0.001) or minimal interventions (MD −1.40 [−1.98,−0.82], P < 0.001), and in eHealth weight loss interventions with extra components or technologies (MD 1.46 [0.80, 2.13], P < 0.001) compared with standard eHealth programmes. The findings support the use of eHealth interventions as a treatment option for obesity, but there is insufficient evidence for the effectiveness of eHealth interventions for weight loss maintenance or weight gain prevention.

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eHealth interventions for the prevention and treatment of overweight and obesity in adults: a systematic review with meta-analysis.

In this work, we investigate the use of deep learning for distortion-generic blind image quality assessment. We report on different design choices, ranging from the use of features extracted from pre-trained convolutional neural networks (CNNs) as a generic image description, to the use of features extracted from a CNN fine-tuned for the image quality task. Our best proposal, named DeepBIQ, estimates the image quality by average-pooling the scores predicted on multiple subregions of the original image. Experimental results on the LIVE In the Wild Image Quality Challenge Database show that DeepBIQ outperforms the state-of-the-art methods compared, having a linear correlation coefficient with human subjective scores of almost 0.91. These results are further confirmed also on four benchmark databases of synthetically distorted images: LIVE, CSIQ, TID2008, and TID2013.

On the use of deep learning for blind image quality assessment

Image-Assisted Dietary Assessment: A Systematic Review of the Evidence

Improvements in life expectancy achieved by technological advancements in the recent decades have increased the proportion of elderly people. Frailty of old age, susceptibility to diseases, and impairments are inevitable issues that these senior adults need to deal with in daily life. Recently, there has been an increasing demand on developing elderly care services utilizing novel technologies, with the aim of providing independent living. Internet of things (IoT), as an advanced paradigm to connect physical and virtual things for enhanced services, has been introduced that can provide significant improvements in remote elderly monitoring. Several efforts have been recently devoted to address elderly care requirements utilizing IoT-based systems. Nevertheless, there still exists a lack of user-centered study from an all-inclusive perspective for investigating the daily needs of senior adults. In this paper, we study the IoT-enabled systems tackling elderly monitoring to categorize the existing approaches from a new perspective and to introduce a hierarchical model for elderly-centered monitoring. We investigate the existing approaches by considering the elderly requirements at the center of the attention. In addition, we evaluate the main objectives and trends in IoT-based elderly monitoring systems in order to pave the way for future systems to improve the quality of elderly’s life.

Internet of things for remote elderly monitoring: a study from user-centered perspective

Recent advances in mobile devices have made profound changes in people's daily lives. In particular, the impact of easy access of information by the smartphone has been tremendous. However, the impact of mobile devices on healthcare has been limited. Diagnosis and treatment of diseases are still initiated by occurrences of symptoms, and technologies and devices that emphasize on disease prevention and early detection outside hospitals are under-developed. Besides healthcare, mobile devices have not yet been designed to fully benefit people with special needs, such as the elderly and those suffering from certain disabilities, such blindness. In this paper, an overview of our research on a new wearable computer called eButton is presented. The concepts of its design and electronic implementation are described. Several applications of the eButton are described, including evaluating diet and physical activity, studying sedentary behavior, assisting the blind and visually impaired people, and monitoring older adults suffering from dementia.

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eButton: A Wearable Computer for Health Monitoring and Personal Assistance

A wearable computer, called eButton, has been developed for evaluation of the human lifestyle. This ARM-based device acquires multimodal data from a camera module, a motion sensor, an orientation sensor, a light sensor and a GPS receiver. Its performance has been tested both in our laboratory and by human subjects in free-living conditions. Our results indicate that eButton can record real-world data reliably, providing a powerful tool for the evaluation of lifestyle for a broad range of applications.

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Designing a wearable computer for lifestyle evaluation

The aim of research on the no-reference image quality assessment problem is to design models that can predict the quality of distorted images consistently with human visual perception. Due to the little prior knowledge of the images, it is still a difficult problem. This paper proposes a computational algorithm based on hybrid model to automatically extract vision perception features from raw image patches. Convolutional neural network (CNN) and support vector regression (SVR) are combined for this purpose. In the hybrid model, the CNN is trained as an efficient feature extractor, and the SVR performs as the regression operator. Extensive experiments demonstrate very competitive quality prediction performance of the proposed method.

No-reference image quality assessment based on hybrid model

Anthropometric measurements, such as the circumferences of the hip, arm, leg and waist, waist-to-hip ratio, and body mass index, are of high significance in obesity and fitness evaluation. In this paper, we present a home based imaging system capable of conducting anthropometric measurements. Body images are acquired at different angles using a home camera and a simple rotating disk. Advanced image processing algorithms are utilized for 3D body surface reconstruction. A coarse body shape model is first established from segmented body silhouettes. Then, this model is refined through an inter-image consistency maximization process based on an energy function. Our experimental results using both a mannequin surrogate and a real human body validate the feasibility of the proposed system.

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Anthropometric body measurements based on multi-view stereo image reconstruction

Segmentation by weighted aggregation and perceptual hash for pedestrian detection

We have previously shown that food portion size can be measured using a wearable camera. Since the pictures of food are taken unintentionally in this case, a wide-angle lens is necessary to obtain a larger field of view. As a result, there is a considerable distortion in food images. This paper investigates an image undistortion method to improve the accuracy in food portion size estimation. We use a division model to represent the distortion effect of the wide-angle lens. Our experiment indicates that this method reduces the estimation error significantly.

Jie Li

Papers

Designing a wearable computer for lifestyle evaluation

No-reference image quality assessment based on hybrid model

Anthropometric body measurements based on multi-view stereo image reconstruction

Segmentation by weighted aggregation and perceptual hash for pedestrian detection

Distortion correction in wide-angle images for picture-based food portion size estimation