The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care problems has received unprecedented attention in the last decade. The technique has recorded a number of achievements for unearthing meaningful features and accomplishing tasks that were hitherto difficult to solve by other methods and human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The innovation of DL is a developing trend in the wake of big data for data representation and analysis. DL is a type of machine learning algorithm that has deeper (or more) hidden layers of similar function cascaded into the network and has the capability to make meaning from medical big data. Current transformation drivers to achieve personalized health care delivery will be possible with the use of mobile health (mHealth). DL can provide the analysis for the deluge of data generated from mHealth apps. This paper reviews the fundamentals of DL methods and presents a general view of the trends in DL by capturing literature from PubMed and the Institute of Electrical and Electronics Engineers database publications that implement different variants of DL. We highlight the implementation of DL in health care, which we categorize into biological system, electronic health record, medical image, and physiological signals. In addition, we discuss some inherent challenges of DL affecting biomedical and health domain, as well as prospective research directions that focus on improving health management by promoting the application of physiological signals and modern internet technology.

Over the past few decades, tactile sensors have become an emerging field of research in both academia and industry. Recent advances have demonstrated application of tactile sensors in the area of biomedical engineering and opened up new opportunities for building multifunctional electronic skin (e-skin) which is capable of imitating the human sense-of-touch for medical purposes. Analyses have shown that current smart tactile sensing technology has the advantages of high performance, low-cost, time efficiency, and ease-of-fabrication. Tactile sensing systems have thus sufficiently matured for integration into several fields related to biomedical engineering. Furthermore, artificial intelligence has the potential for being applied in human-machine interfacing, for instance, in medical robotic manipulation, especially during minimally invasive robotic surgery, where tactile sensing is usually a problem. In this survey, we present a comprehensive review of the state of the art of tactile sensors. We focus on the technical details of transduction mechanisms such as piezoresistivity, capacitance, piezoelectricity, and triboelectric and highlight the role of novel and commonly used materials in tactile sensing. In addition, we discuss contributions that have been reported in the field of biomedical engineering, which includes its present and future applications in building multifunctional e-skins, human-machine interfaces, and minimally invasive surgical robots. Finally, some challenges and notable improvements that have been made in the technical aspects of tactile sensing systems are reported.