How will the Internet of Things enable Augmented Personalized Health?

Internet-of-Things (IoT) is profoundly redefining the way we create, consume, and share information. Health aficionados and citizens are increasingly using IoT technologies to track their sleep, food intake, activity, vital body signals, and other physiological observations. This is complemented by IoT systems that continuously collect health-related data from the environment and inside the living quarters. Together, these have created an opportunity for a new generation of healthcare solutions. However, interpreting data to understand an individual's health is challenging. It is usually necessary to look at that individual's clinical record and behavioral information, as well as social and environmental information affecting that individual. Interpreting how well a patient is doing also requires looking at his adherence to respective health objectives, application of relevant clinical knowledge and the desired outcomes. We resort to the vision of Augmented Personalized Healthcare (APH) to exploit the extensive variety of relevant data and medical knowledge using Artificial Intelligence (AI) techniques to extend and enhance human health to presents various stages of augmented health management strategies: self-monitoring, self-appraisal, self-management, intervention, and disease progress tracking and prediction. kHealth technology, a specific incarnation of APH, and its application to Asthma and other diseases are used to provide illustrations and discuss alternatives for technology-assisted health management. Several prominent efforts involving IoT and patient-generated health data (PGHD) with respect converting multimodal data into actionable information (big data to smart data) are also identified. Roles of three components in an evidence-based semantic perception approach- Contextualization, Abstraction, and Personalization are discussed

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work.

Several unobtrusive sensors have been tested in studies to capture physiological reactions to stress in workplace settings. Lab studies tend to focus on assessing sensors during a specific computer task, while in situ studies tend to offer a generalized view of sensors' efficacy for workplace stress monitoring, without discriminating different tasks. Given the variation in workplace computer activities, this study investigates the efficacy of unobtrusive sensors for stress measurement across a variety of tasks. We present a comparison of five physiological measurements obtained in a lab experiment, where participants completed six different computer tasks, while we measured their stress levels using a chest-band (ECG, respiration), a wristband (PPG and EDA), and an emerging thermal imaging method (perinasal perspiration). We found that thermal imaging can detect increased stress for most participants across all tasks, while wrist and chest sensors were less generalizable across tasks and participants. We summarize the costs and benefits of each sensor stream, and show how some computer use scenarios present usability and reliability challenges for stress monitoring with certain physiological sensors. We provide recommendations for researchers and system builders for measuring stress with physiological sensors during workplace computer use

Pursuing precision in medicine and nutrition: the rise of electrochemical biosensing at the molecular level

In the era that we seek personalization in material things, it is becoming increasingly clear that the individualized management of medicine and nutrition plays a key role in life expectancy and quality of life, allowing participation to some extent in our welfare and the use of societal resources in a rationale and equitable way. The implementation of precision medicine and nutrition are highly complex challenges which depend on the development of new technologies able to meet important requirements in terms of cost, simplicity, and versatility, and to determine both individually and simultaneously, almost in real time and with the required sensitivity and reliability, molecular markers of different omics levels in biofluids extracted, secreted (either naturally or stimulated), or circulating in the body. Relying on representative and pioneering examples, this review article critically discusses recent advances driving the position of electrochemical bioplatforms as one of the winning horses for the implementation of suitable tools for advanced diagnostics, therapy, and precision nutrition. In addition to a critical overview of the state of the art, including groundbreaking applications and challenges ahead, the article concludes with a personal vision of the imminent roadmap.The financial support of PID2019-103899RBI00 (Spanish Ministerio de Ciencia e Innovación), and PMP22/00084, PI17CIII/00045, PI20CIII/00019 and PI22/00727 (AES-ISCIII) cofounded with FEDER funds Research Projects and the TRANSNANOAVANSENS-CM Program from the Comunidad de Madrid (Grant S2018/NMT-4349) are gratefully acknowledged. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.S