Ubiquitous blood pressure (BP) monitoring is needed to improve hypertension detection and control and is becoming feasible due to recent technological advances such as in wearable sensing. Pulse transit time (PTT) represents a well-known, potential approach for ubiquitous BP monitoring. The goal of this review is to facilitate the achievement of reliable, ubiquitous BP monitoring via PTT. We explain the conventional BP measurement methods and their limitations; present models to summarize the theory of the PTT-BP relationship; outline the approach while pinpointing the key challenges; overview the previous work towards putting the theory to practice; make suggestions for best practice and future research; and discuss realistic expectations for the approach.

Targeted cell delivery by a magnetically actuated microrobot with a porous structure is a promising technique to enhance the low targeting efficiency of mesenchymal stem cell (MSC) in tissue regeneration. However, the relevant research performed to date is only in its proof-of-concept stage. To use the microrobot in a clinical stage, biocompatibility and biodegradation materials should be considered in the microrobot, and its efficacy needs to be verified using an in vivo model. In this study, we propose a human adipose–derived MSC–based medical microrobot system for knee cartilage regeneration and present an in vivo trial to verify the efficacy of the microrobot using the cartilage defect model. The microrobot system consists of a microrobot body capable of supporting MSCs, an electromagnetic actuation system for three-dimensional targeting of the microrobot, and a magnet for fixation of the microrobot to the damaged cartilage. Each component was designed and fabricated considering the accessibility of the patient and medical staff, as well as clinical safety. The efficacy of the microrobot system was then assessed in the cartilage defect model of rabbit knee with the aim to obtain clinical trial approval.

For more than a century, it has been known that the body recoils each time the heart ejects blood into the arteries. These subtle cardiogenic body movements have been measured with increasingly convenient ballistocardiography (BCG) instruments over the years. A typical BCG measurement shows several waves, most notably the “I”, “J”, and “K” waves. However, the mechanism for the genesis of these waves has remained elusive. We formulated a simple mathematical model of the BCG waveform. We showed that the model could predict the BCG waves as well as physiologic timings and amplitudes of the major waves. The validated model reveals that the principal mechanism for the genesis of the BCG waves is blood pressure gradients in the ascending and descending aorta. This new mechanistic insight may be exploited to allow BCG to realize its potential for unobtrusive monitoring and diagnosis of cardiovascular health and disease.

High blood pressure (BP) is a major cardiovascular risk factor that is treatable, yet hypertension awareness and control rates are low. Ubiquitous BP monitoring technology could improve hypertension management, but existing devices require an inflatable cuff and are not compatible with such anytime, anywhere measurement of BP. We extended the oscillometric principle, which is used by most automatic cuff devices, to develop a cuff-less BP monitoring device using a smartphone. As the user presses her/his finger against the smartphone, the external pressure of the underlying artery is steadily increased while the phone measures the applied pressure and resulting variable-amplitude blood volume oscillations. A smartphone application provides visual feedback to guide the amount of pressure applied over time via the finger pressing and computes systolic and diastolic BP from the measurements. We prospectively tested the smartphone-based device for real-time BP monitoring in human subjects to evaluate usability (n = 30) and accuracy against a standard automatic cuff-based device (n = 32). We likewise tested a finger cuff device, which uses the volume-clamp method of BP detection. About 90% of the users learned the finger actuation required by the smartphone-based device after one or two practice trials. The device yielded bias and precision errors of 3.3 and 8.8 mmHg for systolic BP and −5.6 and 7.7 mmHg for diastolic BP over a 40 to 50 mmHg range of BP. These errors were comparable to the finger cuff device. Cuff-less and calibration-free monitoring of systolic and diastolic BP may be feasible via a smartphone.