9. Body-Worn, Ambient, and Consumer Sensing for Health Applications

Currently, healthcare is delivered using a doctor-centric model. While this model was appropriate for its time, it is not be scalable into the future. The world’s population is getting older. In 2010, there were approximately 0.5 billion people over the age of 65; this is projected to increase to over 1.5 billion by 2050 (Suzman et al., 2011), due to increases in life expectancy. The prevalence of lifestyle diseases, like hypertension, diabetes, and obesity, are also increasing significantly. Consequently, the amount of money spent on healthcare continues to rise and is fast approaching the point where western economies can no longer afford healthcare. In 2010, the US spent a total of 2.6 trillion US dollars (17.92 percent of GDP) on healthcare, as shown in Figure 9-1. This contrasts worryingly with 5.2 percent of GDP spent on healthcare in 1960. The Congressional Budget Office has estimated that healthcare spending will reach 25 percent of GDP by 2025, 37 percent by 2050 and 49 percent of GDP by 2082, if healthcare spending continues to follow current trends (Fodeman et al., 2010).

Demands on healthcare resources will continue to grow: the longer we live, the higher the incidence of age-associated chronic diseases such as COPD and cognitive decline. Scientific advances will allow conditions such as cancer, previously considered to be acute or terminal, to be treated as chronic diseases instead. Without changes to the way healthcare is delivered, some 25 percent of a typical western country’s population may have to be involved in healthcare delivery by 2040 (Vavilis et al., 2012). Therefore, there is a pressing need to invent a different way of delivering care while reducing the already unsustainable healthcare costs that plague virtually every major Western government.

Modern lifestyles are seen to play an ever-increasing role in people’s health. Non-communicable diseases such as coronary heart disease, hypertension, diabetes, and some cancers (such as lung cancer) have clinically proven correlations with lifestyle choices. These “lifestyle diseases” are becoming the most significant causes of death (Colvin, 2012). Factors that influence lifestyle diseases include sedentary lifestyles, obesity, stress, high fat/sugar/salt/processed meal diets, cigarette smoking, and high levels of alcohol consumption. People are looking to technology, primarily in the area of activity monitoring, to provide more awareness of our sedentary lifestyles. Sensing technologies and supporting software can be used to encourage people to develop habits that predispose them to better health in later adult life (Corocoto, 2011). A wide variety of products, including Nike Fuel and Fitbit, already aim to make people more active, and provide mechanisms to keep them engaged in fitness over the longer term. We discuss the sports and recreational sensing area in more detail in Chapter 10.

Incidences of disease, whether lifestyle-related or due to aging, are forcing us to look at the way we currently deliver healthcare, and to consider how it should be done in the future. Modifying the healthcare delivery model will embrace a multi-faceted approach and require changes such as the following:

However, healthcare is an extremely conservative market in terms of ICT adoption, especially when it comes to large-scale systematic change. It is also a market that requires equipment to be proven and certified before adoption, which translates into a long process for new products coming to market (the regulation and certification process is discussed in Chapter 6). Finally, it is an industry that demands a lot from suppliers, including the ability to provide product liability in key healthcare situations.

The healthcare management business has long been an activity-driven or fee-for-service model that incentivizes activity over outcome (Chase, 2012). As the costs of hospital admissions continue to grow rapidly, a new emphasis on patient outcomes has started to emerge (Porter, 2010). Governments (Scher, 2011) and the private insurance industry are now starting to focus on the quality of patient outcomes, with hospitals not being reimbursed for patients who are readmitted within a specified period for the same problem. Sensing will play an important role in realizing this new model of care by providing quantitative measures of patient status in the clinical environment prior to discharge, and in the home after discharge. Insurance companies are also getting more interested in sensing, which can be used in the home to maintain the health and wellness of patients on a longitudinal basis, thus preventing or minimizing costly hospital admissions for acute events.

In the longer term, the use of health-related sensor data by insurance companies and similar organizations will undoubtedly raise significant social debate. Insurance companies already use sophisticated statistical models to estimate the future risk of clients based on their previous medical history and that of their families. The increasing availability of sensor data, especially genetic information, has the potential to quantify the impact of an individual’s genetics and behavioral risks. In principle, certain individuals could have premiums that are prohibitively expensive, or they could even become uninsurable. There is likely to be significant resistance ethically to using genetic factors to load insurance premiums, especially on the basis that people can’t do anything about the DNA-related risks they inherited from their parents. The real battle to be fought is likely to be related to modifiable behavior, for example, compliance with therapeutic regimes. If someone’s behavior negatively impacts the predicted cost, should others bear the burden through higher premiums? Sensed information will be central to this debate as it will enable behavior and compliance to be tracked.

With the availability of over-the-counter (OTC) diagnostic tests, direct-to-consumer genetic testing kits, physiological monitoring sensors, and fertility monitoring, there is a rapidly growing online trend toward the phenomenon of the “quantified self” and “life-logging.” Individuals may be driven by various motivations, such as endeavoring to stimulate discussion, seeking a cure, or experimenting with preventative measures for a known disease risk. They may choose to share this information socially, either in person or through online forums. These individuals are highly motivated to understand what the data is telling them and to utilize the data to improve their health outcomes. Part of that process is changing the conversation with an individual’s doctor, as outlined in Chapter 7. The extent of these data-driven discussions is somewhat limited. In areas such as fertility, the use of sensor data is becoming the norm, and is seen by the both the patient and clinicians as adding significant value to the consultation process. However, it will take time before the use of personal sensing information provided by individuals will be accepted in the clinical community. Issues such as data quality, duty of care, and relevance must be addressed to gain acceptance. Still, the first tentative steps in shifting this dynamic have started, driven in part by the use of sensors.

Healthcare is on the cusp of a significant change in the way we understand and treat disease in individuals. We are beginning to move away from empirical, “population-based” medicine to precise, “personalized” medicine. Driven by advances in “omic” technologies—namely genomics, transcriptomics, proteomics, and metabolomics—disease pathology is now starting to be defined at a molecular level. This molecular-level classification will mean that the optimum treatment can be selected according to the both the molecular behavior of the disease and the DNA of the individual. Sensing will play a key role in personalized medicine. Companion tests, based on biosensors, will determine if a patient can benefit from a genetically targeted drug treatment. And sensors will monitor the patient during the course of the treatment (for example, monitoring biochemical by-products associated with a drug’s targeted metabolic pathway). There is significant potential to radically alter our approach to disease diagnosis, prognosis, and therapeutic interventions over the coming decades.

Context plays an important role in determining the value of sensor data. For example, some sensor readings, taken in isolation, can have limitations. These limitations can be addressed at least in part by collecting supporting contextual information. What was the person doing when the measurement was collected? Where was she located? What were the local environmental conditions that could influence the measurement? Contextual information is particularly important when capturing physiological measurements, such as activity levels prior to heart rate measurements. Context can typically be gleaned using additional sensors, accelerometers, for example, to determine if the person is moving when a physiological measurement is taken.

Qualitative approaches, such as weekly health questionnaires, can also prove useful. This is commonly used in chronic disease management systems. However, the quality of self-reported information is dependent on the accuracy of response by the patient, which is difficult to determine. When collecting contextual data to support a clinical sensor reading or observation, it is also important to ensure that any data has the appropriate temporal resolution and spatial characteristics. If not matched correctly, additional information can add ambiguity rather than clarity. Contextual sensing information can generally be used in three ways:

Context can also play an important role in determining the security, privacy, performance, and accessibility requirements of sensor networks. This continues to be an area of active research, exemplified by new solutions such as AlarmNet from the University of Virginia (Stankovic et al., 2011).

Sensors can also be used to provide context to the success or failure of a treatment protocol. By knowing what happens between outpatient visits, a clinician can optimize an existing treatment protocol or develop a new protocol. Data from ambient and body-worn sensors can also be combined with context-detection algorithms to generate support messages. Such messages are generated at appropriate times to support a patient on a specific treatment protocol, for example to prompt to take medication, remind about exercise, or advise about the consumption of food and drink.

Sensing is pervasive in every aspect of hospital-based care, from the simplest digital thermometer to complex laser-guided surgical tools. Imaging sensors, such as x-ray, magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound provide doctors with non-invasive insight into the human body and how it operates. These sensors have radically transformed diagnostic medicine. In general medicine, these images allow doctors to pinpoint areas of injury or abnormality, perform minimally invasive surgery, and evaluate the success or failure of a procedure. In obstetric care, ultrasound allows doctors to monitor the developing fetus and identify any fetal or other abnormalities that could impact the health of the mother or baby.

Complex sensing devices are used by clinical pathologists in hospital laboratories every day to perform hematology, biochemistry, immunology, histopathology, and microbiology functions. These large, non-discrete sensors require careful sample preparation by skilled professionals to ensure optimum results. Sensors also play an important role in treatment technologies. They can sense events, such as missed heartbeats, that can be acted upon by clinicians or actuators. They can optimize drug delivery devices by identifying the optimum time to administer a drug. And they can continuously track a patient’s vital signs to ensure that a treatment, such as dialysis, is delivered safely. Imaging, implantable, and drug-delivery devices are all rich sources of sensor application, worthy of entire chapters in their own right. Given our space limitations, though, this book focuses on discrete, non-invasive monitoring and assessment technologies that can, or will soon, be applied in a home or community setting.

As mentioned, shifting demographic patterns are making the current reactive models of care unsustainable and compel several dramatic changes in the way healthcare is delivered. First, healthcare must become proactive and predictive to prevent costly acute health episodes. Second, healthcare must become individualized, rather than population-based, to ensure the optimum treatment is delivered. Third, the delivery of care must be decentralized from hospitals to the community and the home. Technology will play a key role in all these shifts. The first and second healthcare model shifts will be discussed later in this chapter, and this section will discuss the current and future state of community-based sensing.

Community care refers to the general practice doctor, the clinic, and nursing services in a community. In most Western countries, doctors and community clinics are the first point of contact with the healthcare system for normally healthy people. Community doctors and clinics provide routine medical services. They also act as triage services that refer those requiring further care to specialist clinics in hospitals. Community-based nursing provides hospice and long term care for people in their own homes. Nursing services can be privately or publically funded and the level of care provided is dependent on the patient’s condition and the payer’s resources.

A typical consultation with a community doctor lasts approximately 10 minutes, during which the doctor must listen to and record patient history, measure vital signs, and make a diagnosis. Given the time constraint, technology must be quick and easy to use. Currently, the only technology in a typical doctor’s office is an electronic health record (EHR) system, a digital thermometer, a digital blood pressure cuff, and various disposable test kits. This is very much in keeping with the reactive diagnostic model of care. Technology can be used more holistically to improve the quality and throughput of community care services:

Home healthcare delivery has been an area of significant effort over the last decade, both from commercial and research perspectives. This work has primarily focused on chronic disease management for conditions such as COPD, CHF, and diabetes. However, the initial focus has expanded to include additional areas, such as post-operative care, stroke rehabilitation, and well-being monitoring and alerting (with, for example, body-worn fall detectors). Clinical-grade sensing plays an important role in delivering reliable physiological and biometric monitoring of patients.

Clinically oriented home-care technologies can be classified into two groups: patient monitoring sensors and patient mobility sensors, as shown in Table 9-2. Patient monitoring sensors measure the physiological and biometric attributes of an individual and store or react to the data, as required by a supporting application. This form of sensing requires either direct contact with a human body or the collection of a sample, such as blood, for testing. Patient motion sensors, such as passive infrared (PIR) motion sensors (which can detect an individual entering or exiting a room) and pedometers (which count steps taken) observe actions. Behavioral patterns can be inferred from this data, and abnormal patterns can be used to trigger an alert. Mobility data can be sensed through direct contact with the human body, as for example, with a pedometer; or by using ambient sensors, such as PIRs, attached to the subject’s environment. Wearable motion sensors are in direct contact with the person and can therefore accurately measure the motion of that person regardless of location or the presence of others. Ambient solutions are ideal for situations in which the person may forget to attach a sensor, or attach it incorrectly. However, ambient sensors are mounted in fixed locations and can’t sense a person if that person is in a different location.

The focus on developing telehealth with clinical-grade sensing has been driven by the expected benefits these technologies could provide in terms of support and supervision, without requiring a clinical professional to be present. This in turn would lessen healthcare costs for both providers and individuals by reducing the time associated with in-person consultations, redundant doctor’s visits, and providing early detection and reaction to adverse health events. Additionally, sensor technologies may also have the ability to notify caregivers to changes in behavior patterns or physical state that may go unreported or unnoticed by the patient but that may represent early warning signs of an impending health event. Despite the expected benefits, remote patient monitoring through telehealth remains an area of continuing debate. Studies show inconsistent results in patient benefits, expected cost savings, and so forth. For example, some studies have shown measurable improvements in patient outcomes (Miguel et al., 2013); others have been neutral (Cox et al., 2012); while others reported no meaningful impact (Cartwright et al., 2013). The area of telehealth is still relatively immature and will continue to evolve over the next decade. As we embrace and better understand personalized medicine, knowledge of how and which patients will benefit from this technology will improve. So will our understanding of when it is not appropriate for an individual. Realization of the actual benefits will also require structural changes within the healthcare domain. With a data-driven approach to remote patient monitoring, it will take time to realize the benefits and to identify weakness in the granularity, scope and context of patient measurements. Despite the on-going debates on telehealth, its use is projected to increase six-fold to 1.8 million by 2017. In 2012, it was estimated that, globally, 308,000 patients with chronic disease utilized telehealth solutions. These were primarily post-acute patients who had been discharged from hospitals to their own homes. The expected rise in telehealth users by 2017 will be driven by the following factors:

The availability of affordable, clinical-grade sensing for home use continues to grow. An important enabler has been the continued miniaturization and decreased power consumption of MEMs sensing devices, coupled with the integration of either wired or wireless standards-based interfaces. MEMs-based pressure sensors, for example, can be found in a variety of blood pressure monitors. They are also found in applications that require drug absorption by inhaling aerosols through positive-pressure ventilation. Delivery aerosols are increasingly being used for the treatment of cancers, AIDS, diabetes, and asthma in home environments. The key to successful operation of these devices is the ability of the MEMS pressure sensors to detect inhalation pressures as low as 0.018 psi and to accurately and rapidly switch the delivery mechanism on and off in response. In addition, MEMS pressure sensors can be used to sense the person’s inhalation patterns and in turn adjust the pattern of delivery to maximize the benefit.

Body-temperature sensing is another area that has benefitted from the availability of low cost and accurate infrared sensors. The most common form of this sensor is the tympanic ear thermometers, shown Figure 9-3. These sensors work on the principle that the eardrum emits infrared radiation. Using an algorithm, the amount of received IR radiation is converted into a temperature that can be displayed via a digital display. This form of temperature sensing has proven very popular due to its ease of use, relatively low cost, and accurate results.

As we have already discussed, individuals have a growing interest in proactively managing their own health. This is leading to increased adoption of self-care diagnostic test kits for home use. Various motivations drive the utilization of test kits, including self-empowerment of individuals, convenience, privacy, anonymity, the “worried well” effect, and financial considerations. A proactive consumer philosophy has developed over the last few decades and is now percolating into the personal health domain, driven in part by easier access to test kits that are available off-the-shelf at pharmacies and online. In-home diagnostic test kits have been available for less than four decades. In 1977, the first over-the-counter early-pregnancy test appeared. At the time, there was significant professional and public controversy surrounding the test (Pray, 2010). Many were concerned about potential societal impacts, particularly on younger women. That discussion has now been largely confined to the history books and has moved on to debates about the availability of tests for disease, infection, and blood chemistry.

As the popularity and general availability of self-care health test kits have grown, voices of concern or even alarm have increased. If used correctly, FDA-approved test kits should be as accurate as a laboratory tests, which are conducted under controlled conditions. However, the manner in which the test is performed at home can vary significantly, due to poor instructions related to sample collection and data interpretation. This variability can result in misleading readings, false positives (such as for the prostate specific antigen, or PSA), and give a false sense of security (for example, with a colon cancer screening). Experts argue that home tests can often be a waste of money. Clinicians will most likely repeat the tests an individual has carried out at home once they present themselves at the doctor’s office (NHS, 2011).

Cholesterol-level kits are among the most popular home test kits currently on the market. However, results vary significantly, especially if subjects are not fasting when they take the test. Another common problem is poor adherence to the sampling instructions. In cholesterol testing, people tend to squeeze or “milk” a finger to get blood onto the test strip or into the well, which may affect the accuracy of the results.

In some circumstances, the scientific value of particular tests is highly debatable. Food allergies may cause life-threatening shock reactions, such as severe breathlessness and tongue swelling, while symptoms of food intolerances range from mild discomfort to diarrhea. An allergic food reaction results in an immunological response in the form of IgE antibody production. Food allergy test kits are designed to detect and quantify this antibody-based immunological response. Some experts argue that these results are questionable and are often dependent on the intensity of the allergic reaction. Food intolerance test kits are also available, which apply the same immunological response principle to identify food intolerances, although food intolerance does not elicit a full-blown allergic reaction. There is little or no scientific support for this form of testing. A simple trial and error approach with the suspected foods is the normal course of action prescribed by most experts for identification of food intolerance.

Home testing suffers from a lack of legislation and corresponding regulation, as discussed in Chapter 6. In the US, only some tests have FDA clearance. Some tests can be certified by other professional bodies. For example, cholesterol tests can be certified by the Cholesterol Reference Method Laboratory Network (CRMLN). Other tests have no certification or peer-reviewed science to back them up. The lack of regulation and consistent quality assurance will continue to bring confusion and uncertainty to the area of home monitoring for many years to come. In the interim, some de facto certification of tests will occur on an ad-hoc basis or through utilization of crowd-sourced determination of efficacy.

Some experts believe that affordable tests don’t provide enough information to be truly useful. Others think this is a limited and somewhat vested view by the medical establishment. As we head down the road of patient-centric healthcare, it can be argued that considerations valued by patients, such as convenience and privacy, will gain greater weight—especially when it comes to people having basic access to healthcare. The value of any testing, despite potential flaws, has to be weighed against no testing and no understanding of potential risks (Carrell, 2012).

There are three sensing techniques commonly used for home testing kits:

A variety of organizational, technical, and end-user challenges and drivers currently exist for successful sensor utilization in the healthcare domain. Demographic changes, technology innovation, and the cost of reactive healthcare are driving the adoption of in-home and in-community health monitoring. Yet many barriers remain, such as the organizational changes required within healthcare systems to realize the value and utility of sensor technologies. There are also key challenges to be addressed, such as usability of the sensor technology to ensure patient compliance. Despite these challenges, there is growing momentum behind the adoption of sensor technologies based on their ability to provide new and potentially lower-cost models of care. These models of care are also more adaptable to the needs and lifestyle choices of patients. We will now look at the key drivers and challenges in more detail.

Current trends indicate that the future of health sensing will be a convergence of many areas—health, technology, fashion, clothing and lifestyle. As health consumerism continues to grow, people will expect their medical devices, particularly non-prescribed monitoring devices, to be aesthetically pleasing. Many healthy people want to keep track of their health status on a continuous basis through easy to use, non-intrusive, fashionable devices. This growing group of people ranges from those who proactively manage their health, to the “worried well” who are concerned about the latest health scare in the media. Patients are becoming more aware of the sensors and services that have emerged in the wellness and fitness domain. In the future, they will likely demand similar capabilities for their prescribed healthcare therapies. This will also lead to a rebalancing of the dynamic between patients and healthcare professionals. Many will view this as a positive development as it will empower individuals to have a more active role in the decision making process with respect to their health. People are becoming better educated and, as a result, are questioning and demanding more of their healthcare providers. Patients will want choices and will play a more active role in the decision-making process with respect to their health and issues associated with it.

The next decade will bring acceleration in the use of personalized medicine, as it moves toward becoming standard practice in healthcare. This will be driven by the utilization of “omics” technologies, where genomic information will be used to determine the most effective course of treatment for an individual. It will move us away from population-centered statistics, where we look at the average effect of treatment, and toward tailoring treatments and continuously modifying that treatment base on the biological data of the individual. That approach is commonly known as theranostics, which focuses on targeted drug therapies and companion diagnostic tests to advance personalized medicine. Testing forms an integral part of it, as immediate reactions to taking a new medication must be detected and the patient treatment can then be optimized based on the test results. Sensing will play a central role in this approach, ranging from determining which drug treatment will be most effective for an individual’s biology, to monitoring its efficacy.

From a technology perspective, sensing will be heavily influenced by increased integration of devices through the continued evolution of MEMS. The size of sensors will continue to shrink, enabling to them to be used in form factors that match the needs and expectations of patients. New epidermal sensing technologies—such as electronic skin patches, or “smart skin,” where the sensor is literally tattooed onto the patient’s skin—will enable clinicians to monitor vital size in almost any location without the need for prodding or poking. This technology will form part of the new world of cyber-physical systems, where the physical world and the cyber world are connected in a seamless, natural way. Sensing platforms based on the “smart band-aid” form factor are the first significant step on this journey (Fangmin et al., 2012, Hoi-Jun et al., 2010).

There will also be exciting developments in the use of smartphones as health platforms. Their pervasiveness and familiarity will see smartphones gain greater adoption as healthcare sensing and management devices. There is already growing interest among the major smartphone players, including Samsung and Apple, and Telco providers, in delivering future devices and services. Initial product offerings will be based on a combination of existing sensors, innovative smartphones, and cloud-based software. For example, an Israeli company called LifeWatch has an Android-based smartphone product (shown in Figure 9-9) that has integrated blood glucose monitoring, EKG/ECG, body temperature, percentage body fat, blood oxygen saturation levels, and stress-level sensors. Innovative product offerings will be developed as new discrete sensors emerge and the integrated sensing capabilities of smartphones continue to evolve. These products will include apps and services to provide continuous, always-connected sensing in any location required by the lifestyle of an individual. This will in turn drive the growth of cloud services and features such as security, privacy, configurable access rights, and integrated electronic health records.

The development of new drugs is slow and costly. Still, our understanding of how diseases affect individuals at a molecular level due to subtle variations in or genetic and biochemical make-up is growing at a significant pace. This knowledge is driving requirements for many new drugs that are designed for specific genetic markers and biochemical pathways. The current models of clinical trials will have to change in order reduce costs, support assembly of cohorts with specific genetic characteristics, and expedite the output of results demonstrating efficacy. Approximately USD 25 billion is spent on trials annually in the US alone, accounting for around 60 percent of the development costs. Sensing will play a greater role in clinical trials by providing extensive quantitative measures of a subject’s physiological and biochemical well-being. Data-quality issues, one of the leading issues currently in clinical trials, will be reduced by using sensors to continuously monitor and automatically report data during home monitoring. The data will be stored in electronic health records on cloud infrastructures that enable real-time demonstration of clinical effect and the identification of potential side effects. This will lead to shorter clinical trials, as substantial evidence with respect to efficacy can be demonstrated during the trial, thus expediting the licensing process. Sensor-based physiological monitoring is already an emerging reality in trials of cardiac drugs, where 24-hour continuous EKG/ECG monitoring is becoming normal practice. The increase in sensor adoption for home monitoring during clinical trials should lead the way to faster, better-characterized and a cheaper drug development process.

The use of sensing technologies is already attracting growing interest from insurance and national services providers as a means to prevent serious health events through proactive monitoring programs or for post-discharge monitoring of patients. In the US, over 30 percent of Medicare patients discharged from the hospital are readmitted with 90 days, in what is sometimes referred to as the revolving-door syndrome. There is a reassessment underway of how medical care is reimbursed with a move toward pay-for-performance. Medicare has now modified its reimbursement policy to penalize care providers when a patient with a chronic disease is readmitted within 30 days of discharge (CMS, 2013). Sensing technologies will play a greater role in assessing the physiological and kinematic status of patients to ensure they are fit for discharge. The use of body-worn and ambient sensing to monitor a patient post discharge will become normal clinical practice. This type of monitoring will be further catalyzed as we move towards more stringent and frequent requirements on compliance reporting for patients with various disorders and conditions.

Sensing, coupled with other ICT capabilities, such as smartphones, clouding computing, and healthcare data analytics, can offer many potential benefits to society. However, the adoption of these technologies and what their data can reveal about the current or future health status of an individual will have ethical and societal aspects. These aspects must be addressed in a transparent and balanced manner to ensure trust among the monitored and the data consumer. We are on the cusp of an exciting era, where sensing, combined with other technologies, will make a meaningful impact on the health and well-being of individuals and society at large.

In this chapter we looked at how sensor technologies play a key role in the continuum of healthcare, ranging from the hospital environment, to the community, to home-based monitoring. A variety of factors—including an aging population, moving to proactive healthcare, personalized healthcare delivery, and greater involvement by patients in their health and well-being—are driving this sensor-based, proactive health monitoring approach. We reviewed how sensing is already playing an important role in chronic disease management, investigative monitoring, home rehabilitation, mobility monitoring and consumer sensing. The integration of sensor and ICT technologies into mobile form factors such as smartphones is leading to the development of a new wave of products and services. This evolution is helping to address patient expectations that technology can adapt as required to their lifestyle choices and needs. Finally, we looked at the future of sensing and how it will help deliver new models of care.

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