Artificial Intelligence (AI) has the potential to revolutionize health informatics and provide numerous benefits to the healthcare industry. Some ways AI can be used in health care include:

Medical imaging:

AI algorithms can be used to analyze medical images such as CT scans, X-rays, and MRIs to detect diseases and abnormalities that would be difficult for human radiologists to detect.

Diagnosis and treatment planning:

AI algorithms can help doctors diagnose diseases by analyzing patient data and medical records. They can also help with treatment planning by suggesting personalized and evidence-based treatment options.

Electronic health records:

AI can help manage EHRs by extracting relevant information from patients’ medical records, reducing errors, and improving data accessibility.

Clinical decision support:

AI algorithms can provide real-time decision support to healthcare providers by analyzing large amounts of data and providing relevant and up-to-date information.

Predictive analytics:

AI algorithms can analyze patient health data, including demographic information, lifestyle habits, and medical history, to predict potential health problems and suggest preventive measures.

Wearable devices have the potential to revolutionize healthcare by enabling more personalized and continuous monitoring of patients, leading to better health outcomes and lower healthcare costs. These devices are usually equipped with the sensors belonging one of the following two categories:

1. Motion Sensors:

Such sensors are equipped in smartphones and smartwatches. They are used to measure the motions of a body part where they are placed on.

• Accelerometer: This sensor produces a three-dimensional sequence that specifies acceleration forces (including gravity) acting on the smartphone’s x, y, and z axes. Its sampling rate is usually around 200 Hz.
• Gyroscope: This sensor produces a three-dimensional sequence that presents angular velocities on the x, y, and z axes. Its sampling rate is also 200 Hz.
• Gravity: This sensor produces a three-dimensional sequence that represents gravity forces on the x, y, and z axes. Its sampling rate is also 200 Hz.
• Linear accelerometer: This sensor produces a three-dimensional sequence that specifies acceleration forces (excluding gravity) on the x, y, and z axes. It is also sampled at 200 Hz.
• Magnetometer: This sensor produces a three-dimensional sequence that describes intensities of the earth’s magnetic field along the x, y, and z axes. Such intensities are useful for determining the smartphone’s orientation. It samples the data from 100 Hz to 200 Hz.

2. Physiological Sensors:

These sensors are used to measure the physiological data of the subjects. Purpose built medical devices are equipped with these sensors.

• Respiration:This sensor measures the respiration rate. It detects chest or abdominal expansion/contraction, and outputs a respiration signal. It is usually worn using a comfortable and flexible length-adjustable belt. It can be sampled at around 400 Hz.
• Electrodermal Activity (EDA): This sensor measures the galvanic skin response, i.e., the change in electrical conductivity of skin in response to sweat secretion. It is also sampled around 400 Hz.
• Electrocardiography (ECG): This sensor records the electrical impulses through the heart muscle, and it can also be used to provide information on the heart’s response to physical exertion.
• Electromyography (EMG): This sensor is used to assess the electrical activity associated with muscle contractions and respective nerve cells, which control them. It is placed on the subject’s abdomen above the belly button and is also sampled at 400 Hz.
• Photoplethysmogram (PPG): This sensor measures blood volume pulse, which can be used to derive heart rate and inter-beat interval. It is mostly sampled at 1 Hz.
• Infrared Thermopile: This sensor records skin temperature. It is usually sampled at 5 Hz.
• Electrooculography (EOG): These sensors are usually present in smart-glasses. They can track not only where we look, but how often we blink and even whether we are about to relax or fall asleep. Electrooculography electrodes placed in three locations on the glass frames. These electrodes can track blink duration and eye movements in different directions. In JINS MEME smart-glasses, EOG data is sampled at 20 Hz.

Recording and analyzing a person’s physical activities and physiological data can aid medical practitioners in making more informed decisions regarding the diagnosis and treatment of his or her diseases with personalized medications or nutrition. The aforementioned sensor technology gives us more and more ways to measure the health and behavior of people, not only in the laboratory but also in daily life. When analyzed with contemporary machine learning techniques, this multimodal sensor data can form the basis for health-related decisions.

Wearable sensor technology is an emerging technology in healthcare that enables continuous monitoring of patients’ vital signs and physical activity. Here are some ways wearable sensors can be used in healthcare:

1. Continuous monitoring of vital signs:

Wearable sensors, such as smartwatches and fitness trackers, can continuously monitor patients’ heart rate, blood pressure, and oxygen saturation, giving healthcare providers real-time insight into patients’ health. Continuous monitoring can help in the following domains:

• Early detection of health issues: By continuously monitoring patients’ vital signs and physical activity, wearable sensors can help detect early signs of health problems, such as changes in heart rate or blood pressure, and alert healthcare providers to potential issues.
• Improved chronic disease management: Wearable sensors can help patients with chronic diseases, such as diabetes, better manage their condition by monitoring their blood glucose levels and alerting them to potential problems in real time.

• Increased physical activity: Wearable sensors can motivate patients to increase their physical activity by tracking and providing feedback on their progress.

• Remote patient monitoring: Wearable sensors allow healthcare providers to monitor their patients remotely, reducing the need for in-person visits and increasing the efficiency of care.

• Maternity monitoring: Wearable sensors can collect real time biometric data during the pregnancy to avoid complications in high risk patients. For example, wearable devices containing heart rate, blood pressure or ECG sensors can monitor the health of pregnant women and their fetuses.

2. Rehabilitation and physical therapy:

Wearables such as wearable exosuits and smart clothing can help patients with their rehabilitation and physical therapy. These devices provide feedback and guidance to help patients perform exercises correctly, monitor progress, and adjust treatment plans as needed.

3. Gait Analysis for Fall Detection:

Human locomotion is accomplished through a complex interaction of muscles, skeleton and nervous system. Possible impairments of one of these anatomical systems can be detected early on by experts using a gait analysis. Such an analysis can be performed using wearable sensors in which small, lightweight sensors are attached to the body to collect data on a person’s walking patterns. The sensors can be placed on different parts of the body, such as the feet, legs, hips, or trunk, and can measure various aspects of gait, including stride length, step width, cadence, and foot pressure.

Wearable sensors used for gait analysis typically include accelerometers, gyroscopes, and pressure sensors. These sensors can be incorporated into a variety of devices, such as smart shoes, insoles, or fitness trackers. The data collected by these sensors can be analyzed using software algorithms to provide insights into a person’s gait patterns.

4. Pain Detection and Monitoring:

Analyzing pain perception is of great importance in numerous medical applications. For example, pain, or especially the pain threshold, in physiotherapeutic treatments can not only determine the course or the result of the treatment, but also shape the structure and composition of the exercises right from the start. The quantitative assessment of one’s own pain is traditionally based on self-assessment using questionnaires. However, this method is not an option for patients who are unable to (objectively) communicate their pain.

5. Flow Experience:

Individual and team flow is a concept with great potential for promoting team effectiveness, but measuring and promoting it is a challenge. Traditional measures of team flow rely on self-assessment questionnaires that require a disruption in the team process. Artificial intelligence approaches, i.e. machine learning, provide methods for identifying an algorithm based on behavioral and sensory data, capable of determining team flow and its dynamics over time.

It is important to note that while wearable sensors have great potential to improve healthcare, there are privacy and security concerns with using this technology. It is important to ensure that data collected by wearable sensors is properly secured and protected to prevent unauthorized access.