Ambient intelligence is a futuristic concept that makes explicit input and output data collection devices redundant in smart cities. Instead, data capturing and processing tools such as sensors, processors and actuators are embedded in everyday objects encountered by smart city inhabitants. Ambient intelligence, a concept related to pervasive computing, will exist in smart cities to add an added layer of functionality and convenience by adapting to user needs constantly. The embedded sensors and processors will be configured to collect contextual data from users, while AI-based tools will be deployed to draw inferences from the information collected to anticipate their future needs. Ambient intelligence makes pervasive computing more human-centric, a trait that is essential for healthcare. So, the growing involvement of ambient intelligence in healthcare will be highly beneficial for patients and healthcare experts. Already, smart cities have the technology in place to improve the quality of public health. Involving ambient intelligence in healthcare to a greater degree promises to vastly improve patient care in smart cities.
Ambient intelligence in healthcare mainly involves making regular hospital zones “intelligent” by using contactless sensors and machine learning algorithms to create physical spaces that are sensitive to human presence. As stated earlier, these “spaces” can communicate with humans on sensing their presence and collect their health-related data for diagnostic purposes.
Reducing Patient Waiting Time
As you know, a hospital has a limited number of doctors and nurses who can conduct health tests and diagnostics for patients. So, if there are a greater number of people visiting hospitals, they will have to wait for their turn outside a physician’s room while a doctor or a nurse performs health check-ups and tests on a patient inside. A health emergency such as the ongoing pandemic simply magnifies this by several degrees. In fact, the waiting times have risen for patients who need elective surgeries and other types of medical care since the beginning of the pandemic. In simple words, the long waiting time in hospitals has always been one of the more problematic aspects of healthcare for patients.
Ambient intelligence tools enable hospitals to conduct preliminary tests on patients autonomously. For instance, it is easy to picture the following scenario: An ambient intelligence sensor monitors the health condition of a waiting patient by dynamically monitoring their vitals. After collecting and closely assessing the details related to their body temperature, body fat, heart rate, pulse, cholesterol level and BMI index, a diagnosis report can be prepared autonomously. In the report, any information regarding a potential disease is specified clearly. What’s more, the AI-based tools involved in the network of ambient intelligence in healthcare also study the vitals before enlisting several diet and lifestyle recommendations in the report. These recommendations will contain prescribed medicines that need to be taken as well as any foods or habits that will have to be adopted or avoided by the individual to improve their health. Apart from creating a diagnostics report after evaluating waiting patients, ambient microphones in a physician’s hospital room can also create a medical report based on what the doctor tells their patient during and after performing a check-up. With assistance from Natural Language Processing, such ambient microphones can digitally determine the gist of the physician’s words to create the report as per the standardized medical format.
Automating Critical Care Support
Only those who are critically ill are admitted to the Intensive Care Unit (ICU) of a hospital. Such patients need to be closely monitored continuously. In case their health deteriorates, hospital authorities need to take adequate emergency measures to keep them alive. Most hospitals and clinics are understaffed, so critically monitoring every patient in ICUs may not be practically possible at all times. Ambient intelligence tools help fill such gaps in such instances.
Ambient intelligence in healthcare involves IoT-based tools such as temperature humidity sensors, blood pressure sensors and other monitoring devices and software applications. These devices can autonomously collect data and continuously update doctors regarding the vital statistics of critical patients. Additionally, such tools enable doctors located at remote locations to receive the information via the internet.
Monitoring Blood Glucose Levels
Wireless sensors form the crux of ambient intelligence in healthcare. Such data collection devices simplify the process of autonomously capturing information about a patient’s blood sugar and glucose levels. Wireless Sensors in an ambient intelligence network allow hospitals to continuously and discretely monitor the blood sugar statistics. Patients are made to wear bio-compatible wearable devices for the purpose. Additionally, sensors implanted in a patient’s body identify the glucose level in their blood.
The data captured and transmitted by the implant is sent to a telemeter that receives the information before sending it to doctors and patients in the form of cell phone messages. As you can see, wireless sensors, wearable health monitoring devices and implants form a potent combination to continuously capture the blood sugar information of patients. Based on the readings of such devices that are a part of a network of ambient intelligence in healthcare, doctors can recommend medication, therapy, food and lifestyle changes that can be made to help patients fight diabetes, thyroid diseases and other similar blood sugar-based ailments.
Apart from these applications, IoT, thermal vision cameras and AI can also be used to check infected zones—for example, the surfaces in which infectious viruses are found can be cleaned and decontaminated. Additionally, thermal vision cameras are also highly useful to monitor crowded areas and track down individuals who may be carrying a contagious disease à la COVID-19. Such persons may have a higher body temperature than normal due to fever caused by the disease. They can be isolated so that they do not end up spreading the disease to others in the crowd.
The Main Challenges of Implementing Ambient Intelligence in Healthcare
While the use of ambient intelligence in healthcare is mostly beneficial for patients in smart cities, there are certain problematic aspects of the technology that will need to be resolved before it can be adopted extensively in healthcare. Mainly, the challenges are either technological or ethical.
The first technological challenge is the diagnosis and care in complicated scenarios. For instance, there may be instances in which sensitive data collection devices in an ambient intelligence network collect the vital health stats of an individual and create a diagnostic report bearing the name of another individual. Additionally, devices that capture visual data will be unable to collect visual information in blindspots.
The second technological challenge involves the integrity of the data captured and used for analysis in an ambient intelligence network. Hospitals employing this technology will need to use specialized tools to verify the information collected so that incorrect diagnosis, medication and treatments are not recommended or carried out.
While the technology-based challenges need to be resolved, the ethical challenges hold far more weight in arguments against the implementation of ambient intelligence in healthcare. Firstly, patients may not be entirely comfortable with “intelligent spaces” that collect their information without their knowledge and prior consent. In a way, collecting their data represents a violation of their privacy. Additionally, the problem of AI-based bias in healthcare also rears its ugly head whenever the intersection of AI and healthcare is talked about.
Ambient intelligence can be helpful for elderly care and critical patient monitoring. Regarding the limitations, one can hope that with technological advancements, the technology will become much more refined and ready for extensive real-world implementation.