A Java-Based Agent Platform for Programming Wireless Sensor Networks

n/

Gait-based identification for elderly users in wearable healthcare systems

Abstract The increasing scope of sensitive personal information that is collected and stored in wearable healthcare devices includes physical, physiological, and daily activities, which makes the security of these devices very essential. Gait-based identity recognition is an emerging technology, which is increasingly used for the access control of wearable devices, due to its outstanding performance. However, gait-based identity recognition of elderly users is more challenging than that of young adults, due to significant intra-subject gait fluctuation, which becomes more pronounced with user age. This study introduces a gait-based identity recognition method used for the access control of elderly people-centred wearable healthcare devices, which alleviates the intra-subject gait fluctuation problem and provides a significant recognition rate improvement, as compared to available methods. Firstly, a gait template synthesis method is proposed to reduce the intra-subject gait fluctuation of elderly users. Then, an arbitration-based score level fusion method is defined to improve the recognition accuracy. Finally, the proposed method feasibility is verified using a public dataset containing acceleration signals from three IMUs worn by 64 elderly users with the age range from 50 to 79 years. The experimental results obtained prove that the average recognition rate of the proposed method reaches 96.7%. This makes the proposed method quite lucrative for the robust gait-based identification of elderly users of wearable healthcare devices

SPINE: A domain-specific framework for rapid prototyping of WBSN applications

n/

Just Drink a Glass of Water? Effects of Bicarbonate-Sulfate-Calcium-Magnesium Water on the Gut-Liver Axis

Background and Aim: Fonte Essenziale (R) water is a bicarbonate-sulfate-calcium-magnesium water, low in sodium, recognized by the Italian health care system in hydropinotherapy and hepatobiliary dyspepsia therapy. We wanted to explore its effects on the gut-liver axis and microbiota in non-alcoholic fatty liver disease patients.Patients and Methods: We considered enrollment for 70 patients, of which four were excluded. We finally enrolled 55 patients with ultrasound-documented steatosis (SPs+) and 11 patients without it (SPs-). They then drank 400 ml of water for 6 months in the morning on an empty stomach. Routine hematochemical and metabolic parameters, oxidative stress parameters, gastrointestinal hormone levels, and fecal parameters of the gut microbiota were evaluated at three different assessment times, at baseline (T0), after 6 months (T6), and after a further 6 months of water washout (T12). We lost, in follow-up, 4 (T6) and 22 (T12) patients.Results: Between T0-T6, we observed a significant Futuin A and Selenoprotein A decrease and a GLP-1 and PYY increase in SPs+ and the same for Futuin A and GLP-1 in SPs-. Effects were lost at T12. In SPs+, between T0-T12 and T6-12, a significant reduction in Blautia was observed; between T0-T12, a reduction of Collinsella unc. was observed; and between T0-T12 and T6-12, an increase in Subdoligranulum and Dorea was observed. None of the bacterial strains we analyzed varied significantly in the SPs- population.Conclusion: These results indicate beneficial effects of water on gastrointestinal hormones and hence on the gut-liver axis in the period in which subjects drank water both in SPs- and in SPs+

a neuro fuzzy fatigue tracking and classification system for wheelchair users

With the elderly and disabled population increasing worldwide, the functionalities of smart wheelchairs as mobility assistive equipment are becoming more enriched and extended. Although there is a well-established body of literature on fatigue detection methods and systems, fatigue detection for wheelchair users has still not been widely explored. This paper proposes a neuro-fuzzy fatigue tracking and classification system and applies this method to classify fatigue degree for manual wheelchair users. In the proposed system, physiological and kinetic data are collected, including surface electromyography, electrocardiography, and acceleration signals. The necessary features are then extracted from the signals and integrated with a self-rating method to train the neuro-fuzzy classifier. Four degrees of fatigue status can be distinguished to provide further fatigue and alertness prediction in the event of musculoskeletal disorders caused by underlying fatigue

Influence of diabetes mellitus on inflammatory bowel disease course and treatment outcomes. A systematic review with meta-analysis

Diabetes Mellitus (DM) may occur in IBD and influence the disease progression

Using cloud-assisted body area networks to track people physical activity in mobility

This paper describes a novel BSN-based integrated system for detecting, monitoring, and securely recording human physical activities using wearable sensors, a personal mobile device, and a Cloud-computing infrastructure supported by the BodyCloud platform. An integration with a smart-wheelchair system is also presented. BSNs are a key enabling technology for the revolution of personal-health services and their integration with Cloud infrastructure can effectively supports the diffusion of such services in our daily life. Many of these personal-health systems - regardless of their final aim - are based, use or are supported by contextual information on user's physical activity (body posture, movement or action) being performed. This work, hence, aims at providing a basic physical activity service that is capable of supporting personal, mobile-Health applications with real-time activity recognition and labeling both on the personal mobile device and on the Cloud

Human Behavior-based Personalized Meal Recommendation and Menu Planning Social System

The traditional dietary recommendation systems are basically nutrition or health-aware where the human feelings on food are ignored. Human affects vary when it comes to food cravings, and not all foods are appealing in all moods. A questionnaire-based and preference-aware meal recommendation system can be a solution. However, automated recognition of social affects on different foods and planning the menu considering nutritional demand and social-affect has some significant benefits of the questionnaire-based and preference-aware meal recommendations. A patient with severe illness, a person in a coma, or patients with locked-in syndrome and amyotrophic lateral sclerosis (ALS) cannot express their meal preferences. Therefore, the proposed framework includes a social-affective computing module to recognize the affects of different meals where the person's affect is detected using electroencephalography signals. EEG allows to capture the brain signals and analyze them to anticipate affective toward a food. In this study, we have used a 14-channel wireless Emotive Epoc+ to measure affectivity for different food items. A hierarchical ensemble method is applied to predict affectivity upon multiple feature extraction methods and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is used to generate a food list based on the predicted affectivity. In addition to the meal recommendation, an automated menu planning approach is also proposed considering a person's energy intake requirement, affectivity, and nutritional values of the different menus. The bin-packing algorithm is used for the personalized menu planning of breakfast, lunch, dinner, and snacks. The experimental findings reveal that the suggested affective computing, meal recommendation, and menu planning algorithms perform well across a variety of assessment parameters