Gjør som tusenvis av andre bokelskere
Abonner på vårt nyhetsbrev og få rabatter og inspirasjon til din neste leseopplevelse.
Ved å abonnere godtar du vår personvernerklæring.Du kan når som helst melde deg av våre nyhetsbrev.
Monitoring the human body is a key element of digital health science. Low-cost sensors derived from smartphones or smartwatches may give the impression that sensors are readily available; however, to date, very few of them are actually medical devices. Designing medical devices requires us to undertake a specific approach demanding special skills, as it concerns the integrity of the human body. The process is tightly framed by state regulations in order to ensure compliance with quality assessment, risk management and medical ethics requirements. This book aims to give biomedical students an overview on medical devices design. It firstly gives a historical and economical approach, then develops key elements in medical device design with reference to EU and US regulations, and finally describes sensors for the human body. The clinical approach is presented as the central element in medical device qualification and this offers a perspective on the use of numerical simulation, particularly since its continued growth in the USA; despite the fact that the approach is strictly limited by regulations.
From the characterization of materials to accelerated life testing, experimentation with solids and structures is present in all stages of the design of mechanical devices. Sometimes only an experimental model can bring the necessary elements for understanding, the physics under study just being too complex for an efficient numerical model. This book presents the classical tools in the experimental approach to mechanical engineering, as well as the methods that have revolutionized the field over the past 20 years: photomechanics, signal processing, statistical data analysis, design of experiments, uncertainty analysis, etc. Experimental Mechanics of Solids and Structures also replaces mechanical testing in a larger context: firstly, that of the experimental model, with its own hypotheses; then that of the knowledge acquisition process, which is structured and robust; finally, that of a reliable analysis of the results obtained, in a context where uncertainty could be important.
Abonner på vårt nyhetsbrev og få rabatter og inspirasjon til din neste leseopplevelse.
Ved å abonnere godtar du vår personvernerklæring.