Hybrid GaSb/Si swept-wavelength laser sensor technology for next generation wearable healthcare device platform

Spectral region beyond 1.7 mu m is particularly interesting for biomedical spectroscopic sensing applications due to the presence of strong and molecule-specific ro-vibrational overtone and combination absorption bands for a number of important analytes such as glucose, lactate, urea, human serum albumin among others. However, this spectral region has been largely unexplored for applications targeting wearable device technology due to the absence of commercially available semiconductor light source technology. In this work we report on recent progress in developing beyond-state-of-the-art GaSb-based swept-wavelength laser technology as a key building-block of the proposed spectroscopic sensor concept. To demonstrate the capability of the technology, we provide experimental data of in vitro sensing concentrations down to the normal physiological range and beyond for glucose, lactates, urea and bovine serum albumin. Furthermore, we provide initial experimental evidence of non-invasive in vivo sensing experiment with extracted absorbance signature of human serum albumin collected from the wrist and demonstrate a clear path towards sensing other analytes. Finally, to demonstrate the full potential of the spectroscopic sensor technology for the wearable device market, we present results of our initial effort to realize a complete spectroscopic sensor system-on-a-chip based on hybrid GaSb/Si material platform and manufactured using conventional 200 mm silicon-on-insulator CMOS technology process in a commercial high-volume foundry

AN EVALUATION OF SMART IMPLANTS IN ORTHOPEDIC SURGERY THAT ENHANCE PATIENT OUTCOMES

Implantable devices with both therapeutic and diagnostic functions are called smart implants. The health care system might save a great deal of money if smart implants are included into routine clinical practice. Applications for smart orthopaedic implants have been found for fracture fixation, spine fusion, hip and knee replacements, among other procedures. Thus far, pressure, force, strain, displacement, proximity, and temperature have all been measured from inside the body using smart orthopaedic implants. Through the integration of application-specific technologies with the implant, physical sensations can be measured. Improvements in implant design, surgical technique, and postoperative care and rehabilitation techniques have been made possible by data from smart implants. With very few exceptions, despite decades of research, smart implants are still not routinely used in clinical practice. This is mostly because integrating the most recent sensor technology requires the implants to be significantly modified. Even if the underlying technology for smart implants has advanced over the past few decades, major technological obstacles still need to be solved before smart implants are used in the majority of medical procedures. Future smart implants' sensors will be compact, straightforward, strong, and affordable, requiring little to no change to the way current implant designs are made. With technology developing so quickly, smart implants will soon be widely used. The secret to integrating smart implants into routine clinical practice is new sensor technology that reduces the need to modify current implants

A Bibliometric Survey of Smart Wearable in the Health Insurance Industry

Smart wearables help real-time and remote monitoring of health data for effective diagnostic and preventive health care services. Wearable devices have the ability to track and monitor healthcare vitals such as heart rate, physical activities, BMI (Body Mass Index), blood pressure, and keeps an individual notified about the health status. Artificial Intelligence-enabled wearables show an ability to transform the health insurance sector. This would not only enable self-management of individual health but also help them focus from treatments to the preventions of health hazards. With this customer-centric approach to health care, it will enable the insurance companies to track the health behaviour of the individuals. This can perhaps lead to better incentivization models with a lower premium to the health-centric customers. Health insurance companies can have better outreach with these customer-centric products. The area is exceptionally novel and shows potential for the research opportunities. Although the literature shows the presence of few works incepting the application of smart wearables in health insurance, it was found that the works are across sections of the society and extremely limited to regions and boundaries. Thus, a need for Bibliometric survey in the area of Smart Wearables in Health insurance is necessary to track the research trends, progress and scope of the future research. This paper conducts Bibliometric study for “Smart Wearables in Health Insurance Industry” by extracting documents of total 287 from Scopus database using keywords like wearables, health insurance, health care, machine learning and health risk prediction. The study is conducted since the last decade that is 2011-2020 for the research analysis. From the study, it is observed that application of wearables in health insurance are in a nascent stage and there is a scope for researchers, insurance, health care stakeholders to explore the used cases for a better user experience

Biometric behavior authentication exploiting propagation characteristics of wireless channel

Massive expansion of wireless body area networks (WBANs) in the field of health monitoring applications has given rise to the generation of huge amount of biomedical data. Ensuring privacy and security of this very personal data serves as a major hurdle in the development of these systems. An effective and energy friendly authentication algorithm is, therefore, a necessary requirement for current WBANs. Conventional authentication algorithms are often implemented on higher levels of the Open System Interconnection model and require advanced software or major hardware upgradation. This paper investigates the implementation of a physical layer security algorithm as an alternative. The algorithm is based on the behavior fingerprint developed using the wireless channel characteristics. The usability of the algorithm is established through experimental results, which show that this authentication method is not only effective, but also very suitable for the energy-, resource-, and interface-limited WBAN medical applications

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Privacy and Security Concerns Associated with mHealth Technologies: A Computational Social Science Approach

mHealth technologies seek to improve personal wellness; however, there are still significant privacy and security challenges. The purpose of this study is to analyze tweets through social media mining to understand user-reported concerns associated with mHealth devices. Triangulation was conducted on a representative sample to confirm the results of the topic modeling using manual coding. The results of the emotion analysis showed 67% of the posts were largely associated with anger and fear, while 71% revealed an overall negative sentiment. The findings demonstrate the viability of leveraging computational techniques to understand the social phenomenon in question and confirm concerns such as accessibility of data, lack of data protection, surveillance, misuse of data, and unclear policies. Further, the results extend existing findings by highlighting critical concerns such as users’ distrust of these mHealth hosting companies and the inherent lack of data control