Wearable biosensors attract significant interest for their capabilities in real-time monitoring of wearers’ health status, as well as the surrounding environment. Sensor patches are embedded onto the human epidermis accompanied by data readout and signal conditioning circuits with wireless communication modules for transmitting data to the computing devices. Wearable sensors designed for recognition of various biomarkers in human epidermis fluids, such as glucose, lactate, pH, cholesterol, etc., as well as physiological indicators, i.e., pulse rate, temperature, breath rate, respiration, alcohol, activity monitoring, etc., have potential applications both in medical diagnostics and fitness monitoring. The rapid developments in solution-based nanomaterials offered a promising perspective to the field of wearable sensors by enabling their cost-efficient manufacturing through printing on a wide range of flexible polymeric substrates. This review highlights the latest key developments made in the field of wearable sensors involving advanced nanomaterials, manufacturing processes, substrates, sensor type, sensing mechanism, and readout circuits, and ends with challenges in the future scope of the field. Sensors are categorized as biological and fluidic, mounted directly on the human body, or physiological, integrated onto wearable substrates/gadgets separately for monitoring of human-body-related analytes, as well as external stimuli. Special focus is given to printable materials and sensors, which are key enablers for wearable electronics.

Data generated from sources such as wearable sensors, medical imaging, personal health records, pathology records, and public health organizations have resulted in a massive information increase in the medical sciences over the last decade. Advances in computational hardware, such as cloud computing, Graphical Processing Units (GPUs), and Tensor Processing Units (TPUs), provide the means to utilize these data. Consequently, many Artificial Intelligence (AI)-based methods have been developed to infer from large healthcare data. Here, we present an overview of recent progress in artificial intelligence and biosensors in medical and life sciences. We discuss the role of machine learning in medical imaging, precision medicine, and biosensors for the Internet of Things (IoT). We review the most recent advancements in wearable biosensing technologies that use AI to assist in monitoring bodily electro-physiological and electro-chemical signals and disease diagnosis, demonstrating the trend towards personalized medicine with highly effective, inexpensive, and precise point-of-care treatment. Furthermore, an overview of the advances in computing technologies, such as accelerated artificial intelligence, edge computing, and federated learning for medical data, are also documented. Finally, we investigate challenges in data-driven AI approaches, the potential issues that biosensors and IoT-based healthcare generate, and the distribution shifts that occur among different data modalities, concluding with an overview of future prospects.

An ultra-low power embedded CMOS temperature sensor based on serially connected subthreshold MOS operation is implemented in a 0.18 μm CMOS process for passive RFID food monitoring applications. Employing serially connected subthreshold MOS as sensing element enables reduced minimum supply voltage for further power reduction, which is of utmost importance in passive RFID applications. Both proportional-to-absolute-temperature (PTAT) and complimentary-to-absolute-temperature (CTAT) signals can be obtained through proper transistor sizing. With the sensor core working under 0.5 V and digital interfacing under 1 V, the sensor dissipates a measured total power of 119 nW at 333 samples/s and achieves an inaccuracy of + 1/-0.8°C from - 10°C to 30°C after calibration. The sensor is embedded inside the fabricated passive UHF RFID tag. Measurement of the sensor performance at the system level is also carried out, illustrating proper sensing operation for passive RFID applications.

In this paper, we describe the design, modeling, fabrication, and optical characterization of the first micropolarimeter array enabling full Stokes polarization imaging in visible spectrum. The proposed micropolarimeter is fabricated by patterning a liquid-crystal (LC) layer on top of a visible-regime metal-wire-grid polarizer (MWGP) using ultraviolet sensitive sulfonic-dye-1 as the LC photoalignment material. This arrangement enables the formation of either micrometer-scale LC polarization rotators, neutral density filters or quarter wavelength retarders. These elements are in turn exploited to acquire all components of the Stokes vector, which describes all possible polarization states of light. Reported major principal transmittance of 75% and extinction ratio of 1100 demonstrate that the MWGP’s superior optical characteristics are retained. The proposed liquid-crystal micropolarimeter array can be integrated on top of a complementary metal-oxide-semiconductor (CMOS) image sensor for real-time full Stokes polarization imaging.