**4. Wearable solutions for other physiological parameters**

There are no satisfactory wearable solutions that can provide continuous, stable, and reliable measurements for blood pressure and blood glucose at this stage [77]. Standard technology to monitor blood pressure requires an inflatable cuff to be pressurized, which may not suitable for continuous monitoring. Several approaches have been proposed for cuffless blood pressure measurement, such as arterial tonometry [78], measuring blood pressure over the radial artery by placing a pressure transducer on the wrist to capture the radial pulse waveform, or indirectly estimating blood pressure from pulse wave transit time (PTT) [79–81]. However, their consistency and reliability are still under investigation compared to the conventional method.

Currently, glucose-level measurements usually require a blood sample via the finger-pricking method. The so-called "minimally-invasive" approaches, using a disposable biosensor needle inserted under the skin on the abdomen to derive the glucose level in interstitial fluid, have been developed for continuous blood glucose monitoring. The invasiveness currently required is a high barrier to realize a practical wearable device. Many efforts targeted the field of noninvasive glucose-monitoring (NGM) techniques have been reported. Many NGM ap‐ proaches—namely reverse iontophoresis [82], impedance spectroscopy [83], electromagnetic sensing [84, 85], optical methods [86–90], and photoacoustic spectroscopy [91]—have been proposed. However, key challenges to apply these technologies to wearable blood glucose monitoring are the inherent lack of specificity behind these technologies, interference from other tissue components, and poor signal to noise ratio. Other studies have aimed to develop a glucose sensor on a contact lens to monitor the glucose level in tear fluid [92–96]. Google Inc. and the University of Washington have announced a prototype of "smart" contact lenses embedded with a fully integrated sensor with signal processing circuits and a wireless coil [96]. A drawback of this technique is the glucose concentration in tears is on the sub-mm level that is almost 10 times lower than the glucose concentration in blood. A microfabricated ampero‐ metric glucose sensor, prepared by immobilizing glucose oxidase (GOx) in a titania sol-gel layer [95], can enhance sensitivity at the same level as a glucose sensor can do directly in blood.

## **5. Summary**

The most representative in the smartphone-only category is the camera-based apps, which measure the cardiovascular blood volume pulse (BVP) generated by repeated, rhythmic heart contractions (that can be registered by photoplethysmogram (PPG)) using the embedded camera in the smartphone. Researchers have shown that pulse rhythm and phase information regarding the BVP waveform can be deduced from the brightness change in the red (R), green (G), or blue (B) channels [68]. Several approaches to deal with the motion artifacts in the camera signals have been proposed to improve the measurement accuracy. The MIT laboratory used the blind source separation (BSS) to separate RGB color channels into independent compo‐ nents, which demonstrated its ability to extract the HR with digital, off-the-shelf webcams in normal ambient lighting in the presence of a limited range of motion artifacts [71, 72] Fang *et al*. [73] uncovered the underlying PPG signal from a single-channel recording using the dynamic embedding technique followed by ICA. This method relies only on the inherent temporal dynamic of the single-channel signal, making it suitable for all kinds of cameras. Thus, the built-in camera in smartphones could easily double as a heart rate monitor. Camerabased apps were subsequently brought into being based on these methods. Azumio's Instant Heart Rate app [74] is one of the most popular health apps on the market, which uses the smartphone's built-in camera and flash to compute HR and update the number through placing the tip of one's finger on the camera for about 10 sec. Many apps with advanced algorithms have also been launched for noncontact measurement of heart and respiration rate, such as a Vital Signs Camera app developed by Philips Innovation [75], extracting HR from

On the other hand, some external sensors, wired or wirelessly connecting with a smartphone, are used for sensing cardiac signals. These sensors transmit raw data to the smartphone for processing and analyzing based on computational algorithms embedded on smartphones. One example of these significant achievements is the most recent FDA approved AliveCor Heart Monitor platform [76], which supports both iPhone and Android platforms. It has been designed as a smartphone case with finger electrodes that snaps onto the back of a smartphone to measure the single-channel ECG and wirelessly communicate with the app on the phone. With secure storage in the cloud, the data can be retrieved confidentially by users themselves

Documented clinical outcomes in the scientific literature with smartphone-based monitors is lacking at present. More work still needs to be done to examine the accuracy and sensitivity

There are no satisfactory wearable solutions that can provide continuous, stable, and reliable measurements for blood pressure and blood glucose at this stage [77]. Standard technology to monitor blood pressure requires an inflatable cuff to be pressurized, which may not suitable for continuous monitoring. Several approaches have been proposed for cuffless blood pressure measurement, such as arterial tonometry [78], measuring blood pressure over the radial artery

the changes in color of the face and RR from the motion of the chest.

**4. Wearable solutions for other physiological parameters**

or their physician anytime, anywhere.

20 Mobile Health Technologies - Theories and Applications

of the smartphone-based monitors.

The wearable technologies highlighted in this chapter can improve the accessibility and convenience of healthcare by bringing clinic and hospital quality monitoring to the point of need. The greatest potential of the continuous and ubiquitous monitoring with wearables might be in enhancing our understanding of the evolving process of poorly defined chronic conditions and allowing for more personalized or precise treatment. However, the perform‐ ance and usability of current technologies and systems according to larger, homogeneous population sets are currently lacking. The high-quality clinical evidence for the use of wearable systems in mHealth to improve chronic disease management and inpatient care is very limited. Future research should be aimed at high-quality clinical evidence related to the usability, accuracy, and robustness of wearable technologies. In addition, there are still many technical issues and limitations yet to be resolved to realize high robustness and reliability in long-term recordings. These include the lack of a full range of appropriate sensors, susceptibility to motion artifacts, battery life, lack of interoperability, security and privacy issues in data communication, the low reliability and poor specificity of cuffless blood pressure and nonin‐ vasive blood glucose-monitoring methods. Despite all the potential hurdles, we envision that there will be further evolvement and improvement in this field in the upcoming years.
