Real-time food intake classification and energy expenditure estimation on a mobile device

© 2015 IEEE.Assessment of food intake has a wide range of applications in public health and life-style related chronic disease management. In this paper, we propose a real-time food recognition platform combined with daily activity and energy expenditure estimation. In the proposed method, food recognition is based on hierarchical classification using multiple visual cues, supported by efficient software implementation suitable for realtime mobile device execution. A Fischer Vector representation together with a set of linear classifiers are used to categorize food intake. Daily energy expenditure estimation is achieved by using the built-in inertial motion sensors of the mobile device. The performance of the vision-based food recognition algorithm is compared to the current state-of-the-art, showing improved accuracy and high computational efficiency suitable for realtime feedback. Detailed user studies have also been performed to demonstrate the practical value of the software environment

Targeted, structured text messaging to improve dietary and lifestyle behaviours for people on maintenance haemodialysis (KIDNEYTEXT): Study protocol for a randomised controlled trial

Introduction Managing nutrition is critical for reducing morbidity and mortality in patients on haemodialysis but adherence to the complex dietary restrictions remains problematic. Innovative interventions to enhance the delivery of nutritional care are needed. The aim of this phase II trial is to evaluate the feasibility and effectiveness of a targeted mobile phone text messaging system to improve dietary and lifestyle behaviours in patients on long-term haemodialysis. Methods and analysis Single-blinded randomised controlled trial with 6 months of follow-up in 130 patients on haemodialysis who will be randomised to either standard care or KIDNEYTEXT. The KIDNEYTEXT intervention group will receive three text messages per week for 6 months. The text messages provide customised dietary information and advice based on renal dietary guidelines and general healthy eating dietary guidelines, and motivation and support to improve behaviours. The primary outcome is feasibility including recruitment rate, drop-out rate, adherence to renal dietary recommendations, participant satisfaction and a process evaluation using semistructured interviews with a subset of purposively sampled participants. Secondary and exploratory outcomes include a range of clinical and behavioural outcomes and a healthcare utilisation cost analysis will be undertaken. Ethics and dissemination The study has been approved by the Western Sydney Local Health District Human Research Ethics Committee-Westmead. Results will be presented at scientific meetings and published in peer-reviewed publications. Trial registration number ACTRN12617001084370; Pre-results

Validation of a recommender system for prompting omitted foods in online dietary assessment surveys

Recall assistance methods are among the key aspects that improve the accuracy of online dietary assessment surveys. These methods still mainly rely on experience of trained interviewers with nutritional background, but data driven approaches could improve cost-efficiency and scalability of automated dietary assessment. We evaluated the effectiveness of a recommender algorithm developed for an online dietary assessment system called Intake24, that automates the multiple-pass 24-hour recall method. The recommender builds a model of eating behavior from recalls collected in past surveys. Based on foods they have already selected, the model is used to remind respondents of associated foods that they may have omitted to report. The performance of prompts generated by the model was compared to that of prompts hand-coded by nutritionists in two dietary studies. The results of our studies demonstrate that the recommender system is able to capture a higher number of foods omitted by respondents of online dietary surveys than prompts hand-coded by nutritionists. However, the considerably lower precision of generated prompts indicates an opportunity for further improvement of the system

Feasibility and acceptability of telehealth coaching to promote healthy eating in chronic kidney disease: A mixed-methods process evaluation

Objective To evaluate the feasibility and acceptability of a personalised telehealth intervention to support dietary self-management in adults with stage 3-4 chronic kidney disease (CKD). Design Mixed-methods process evaluation embedded in a randomised controlled trial. Participants People with stage 3-4 CKD (estimated glomerular filtration rate [eGFR]15-60 mL/min/1.73 m 2). Setting Participants were recruited from three hospitals in Australia and completed the intervention in ambulatory community settings. Intervention The intervention group received one telephone call per fortnight and 2-8 tailored text messages for 3 months, and then 4-12 tailored text messages for 3 months without telephone calls. The control group received usual care for 3 months then non-tailored education-only text messages for 3 months. Main outcome measures Feasibility (recruitment, non-participation and retention rates, intervention fidelity and participant adherence) and acceptability (questionnaire and semistructured interviews). Statistical analyses performed Descriptive statistics and qualitative content analysis. Results Overall, 80/230 (35%) eligible patients who were approached consented to participate (mean±SD age 61.5±12.6 years). Retention was 93% and 98% in the intervention and control groups, respectively, and 96% of all planned intervention calls were completed. All participants in the intervention arm identified the tailored text messages as useful in supporting dietary self-management. In the control group, 27 (69%) reported the non-tailored text messages were useful in supporting change. Intervention group participants reported that the telehealth programme delivery methods were practical and able to be integrated into their lifestyle. Participants viewed the intervention as an acceptable, personalised alternative to face-face clinic consultations, and were satisfied with the frequency of contact. Conclusions This telehealth-delivered dietary coaching programme is an acceptable intervention which appears feasible for supporting dietary self-management in stage 3-4 CKD. A larger-scale randomised controlled trial is needed to evaluate the efficacy of the coaching programme on clinical and patient-reported outcomes. Trial registration number ACTRN12616001212448; Results

CoachAI: A Conversational Agent Assisted Health Coaching Platform

Poor lifestyle represents a health risk factor and is the leading cause of morbidity and chronic conditions. The impact of poor lifestyle can be significantly altered by individual behavior change. Although the current shift in healthcare towards a long lasting modifiable behavior, however, with increasing caregiver workload and individuals' continuous needs of care, there is a need to ease caregiver's work while ensuring continuous interaction with users. This paper describes the design and validation of CoachAI, a conversational agent assisted health coaching system to support health intervention delivery to individuals and groups. CoachAI instantiates a text based healthcare chatbot system that bridges the remote human coach and the users. This research provides three main contributions to the preventive healthcare and healthy lifestyle promotion: (1) it presents the conversational agent to aid the caregiver; (2) it aims to decrease caregiver's workload and enhance care given to users, by handling (automating) repetitive caregiver tasks; and (3) it presents a domain independent mobile health conversational agent for health intervention delivery. We will discuss our approach and analyze the results of a one month validation study on physical activity, healthy diet and stress management

Narrative Review: Food Image Use for Machine Learnings’ Function in Dietary Assessment and Real Time Nutrition Feedback and Education

Technology has played a key role in advancing the health and agriculture sectors to improve obesity rates, diseasecontrol, food waste, and overall health disparities. However, these health and lifestyle determinants continue to plague theUnited States population. While new technologies have been and are currently being developed to address these concerns, they may not be practical for the general population. Utilizing machine learning advancement in food recognition using smartphone technology may be a means to improve the dietary component of nutrition assessments while providing valuable nutrition feedback. This narrative review was conducted to assess the current state of the literature on nutrition technology using image recognition for practical applications, while also proposing theoretical uses for the technology to improve quality of life through dietary feedback