2. Application of AI and Smart Sensing in Healthcare
This section discusses the technologies used in physical rehabilitation. Additionally, there is emphasis on the application of AI and smart sensing in healthcare, particularly recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and gated recurrent unit networks (GRUs).
Health monitoring forms a critical component of patient care in hospitals and rehabilitation centers. Access to accurate and reliable health data is paramount to ensure effective diagnosis, treatment, and rehabilitation. Unfortunately, there are a multitude of challenges that can compromise the integrity of these data. These challenges may arise from human error, patient compliance concerns, time constraints, financial limitations, technological issues, and environmental factors. To provide optimal care for their patients, healthcare providers must address these challenges head-on.
One way to overcome these challenges is through the adoption of non-intrusive smart sensing systems. These systems leverage cutting-edge technologies such as smart carpets and wearable sensors to monitor various health parameters, including position and gait characteristics. This approach minimizes human error and maximizes patient compliance by providing a non-intrusive and convenient method for health monitoring.
The use of ambient assistive living (AAL) technologies incorporating Internet of Things (IoT) and artificial intelligence (AI) holds great potential in healthcare. These technologies offer intelligent learning and support systems that can enhance patient care, improve outcomes, and promote independence. By leveraging smart buildings and wearable devices, healthcare providers can monitor patients remotely, collect real-time health data, and analyze the data using machine learning algorithms. This allows for early detection of health issues, personalized treatment plans, and timely interventions. AAL technologies also provide non-intrusive and convenient methods for health monitoring, reducing the burden on caregivers and maximizing patient compliance. With the ability to analyze patterns and insights from collected data, these technologies enable more informed decision-making and facilitate collaborative approaches to patient care. By integrating AAL technologies into healthcare systems, the quality of care can be enhanced, leading to improved patient outcomes and increased efficiency in healthcare delivery 
In addition to real-time monitoring, these smart sensing systems often incorporate machine learning (ML) algorithms to analyze the collected data. This allows the systems to uncover patterns and insights that may not be readily apparent, thus enhancing the value of the health data. The analyzed data can then be stored in the cloud where they are easily accessible to both physical therapists and patients. This facilitates a collaborative approach to patient care in which both the healthcare provider and the patient are kept informed and can actively participate in the treatment and rehabilitation process.
Furthermore, real-time dashboards provide a comprehensive analysis of the user’s gait and balance. This information is crucial for developing personalized training plans with tailored exercises, which can lead to better rehabilitation outcomes. By utilizing non-invasive smart sensing technology, healthcare facilities can more effectively monitor their patients’ health and enhance their physical rehabilitation plans.
2.2. Technology and Physical Rehabilitation
In recent years, the incorporation of technology into physical rehabilitation has emerged as an important field of study and development, with numerous cutting-edge solutions being introduced to improve rehabilitation practices. These technological advancements have enabled the development of novel rehabilitation systems and tools, such as wearable sensors, robotics, and immersive virtual environments, providing patients and therapists with new opportunities for more effective and engaging therapeutic interventions. As a result, the intersection of technology and physical rehabilitation continues to evolve, offering hopeful prospects for the future of patient care and the overall improvement of rehabilitation outcomes. To demonstrate the efficacy of using technology in rehabilitation, Rintala et al. conducted a study on six different databases from 2000 to 2018 on interventions in physical functioning in stroke and concluded that technology-based distance physical rehabilitation interventions may be comparable to traditional treatments in stroke 
Mukhopadhyay et al. 
conducted a comprehensive review on the deployment of wearable sensors for healthcare applications, providing valuable insights into this rapidly evolving field. The paper emphasizes the critical role of wearable sensors in the microelectronics industry, particularly in monitoring physiological movements and signals. The authors discuss the various fabrication techniques used to develop these sensors, emphasizing the integration of communication modules for signal transmission. The review covers the entire life cycle of wearable sensors in the biomedical sector, from their fabrication in controlled environments to their integration with signal-conditioning circuits for specific applications. In addition, the paper provides a comparative analysis of sensing products currently on the market, allowing readers to evaluate their performance. Mukhopadhyay et al. also demonstrate the integration of sensing prototypes with the Internet of Things (IoT), leading to the development of fully functional sensorized systems. Finally, the review addresses the challenges that exist within current wearable systems and proposes potential solutions to overcome them. This paper is a comprehensive resource for researchers and practitioners interested in the advancements and future directions of wearable sensor technologies in healthcare 
Several nursing home facilities implemented smart sensing systems to accomplish the physical health monitoring of patients. In their opinion, the smart floor technology is an alternative to wearables or cameras, providing privacy and independence for residents and increasing the opportunity for smart personal engagement and monitoring while maintaining resident privacy 
2.3. Motivation Enhancement Systems for Physiotherapy
In contrast to traditional rehabilitation, projects utilizing gamification and serious games demonstrate a promising approach to enhance patient motivation and engagement during physical rehabilitation sessions. Users experience increased enjoyment, greater decision-making freedom, reduced physical demand and anxiety, and decreased pressure, all while improving their physical performance. These innovative strategies emphasize the positive aspects of rehabilitation, fostering a more engaging and effective recovery process 
. Games should provide clear feedback, text, and audio instructions, so that the patient can be informed on how to achieve goals 
. Some more intrusive solutions have been implemented with these objectives in scope, such as VR, MR, and AR systems, and have proven beneficial in upper- and lower-limb motor function, balance and gait 
. Highly intrusive systems, which necessitate users to wear equipment such as goggles or multiple sensors attached to their body, may have an adverse effect on user experience. These systems can potentially discourage users by causing discomfort and, in some instances, even pain.
Virtual environments are transforming healthcare by delivering immersive experiences for a variety of applications such as physical rehabilitation, mental health, medical training, and patient education. These computer-generated simulations supplement traditional practices, resulting in better patient outcomes and more personalized healthcare solutions. The role of virtual environments in healthcare is expected to grow as technology advances.
According to a study conducted by Gumaa and Youssef in 
, the use of VR technology proved to be effective in the treatment of chronic neck pain, shoulder impingement syndrome, rheumatoid arthritis, knee arthritis, ankle instability, and post-anterior cruciate reconstruction and not effective or inconclusive in the treatment of fibromyalgia and back pain as well as knee arthroplasty. This leads to the conclusion that the use of this technology produces excellent outcomes in a variety of diseases.
Several VR applications are gaining popularity in the research field. Madeira et al. introduced PhysioMate, a software project. The system is a rigorous games-based physical rehabilitation system. PhysioMate is intended to encourage physical activity and fight physical and cognitive deterioration, acting as a supplement to physical therapists’ work, with or without their live supervision. Initially designed to aid in the rehabilitation of wheelchair-bound patients, the system employs the Microsoft Kinect sensor to detect and monitor patients’ upper-body movements while immersing them in a virtual situation via VR. PhysioMate uses game mechanics to improve the rehabilitation process by selecting five basic essential movements for balance training and motor coordination. The system is unobtrusive because users are not needed to wear anything or be connected to anything 
. However, the problem with this type of system is that there is no continuous tracking of users, and data collected may be insufficient without supervision of a physiotherapist. There are several uses of VR and sensors, as described in 
There are distinctions between VR and AR, as stated in the introduction section. AR may be more suitable or easier to implement in physiotherapy because the user is aware of their surroundings and can visualize the real world, thereby avoiding accidental collisions or falls. With this idea in mind, there are several research applications that use AR or MR. For example, Monge et al. demonstrated an innovative AR environment coupled with smart sensors capable of obtaining physiological parameters from the user in real-time by displaying an AR scenario and a gamification punctuation system 
; the system encouraged users to engage in the exercises. Paredes et al. also showed a similar system 
for gait rehabilitation using a structure sensor, an apple watch, and an RFduino Microcontroller-based motion sensor. Thus, an unobtrusive system, such as the work presented in 
, is preferable because the sensory part is part of the environment floor and does not require the patient to be physically attached, although the user still has to wear googles in order to visualize and interact with the environment, so it is not an optimal solution yet. Finally, a study performed by Gil et al. concluded that the use of augmented reality in physical rehabilitation has promising results, though as it is still a new technology, exact conclusions will be obtained soon 
2.4. Smart Sensing in Healthcare
By combining sensors and intelligent algorithms to collect and evaluate real-time patient health data, smart sensing is transforming healthcare. This technology offers personalized and proactive care, improving patient outcomes while reducing the strain on healthcare resources. Smart sensing applications in healthcare are anticipated to grow as the technology advances, opening up new avenues for more efficient healthcare systems. Obtaining accurate data and filtering out irrelevant information is a problem that smart sensors seek to address. Physiological parameters like heart rate variability (HRV) can reveal tension and emotional state, but they must be computed using photolethysmography (PPG) sensor or electrocardiogram (ECG) data. Smart sensors obtain precise data by running complex mathematical algorithms directly on the sensor’s microcontroller creating a smarter sensor.
New techniques and materials are constantly being developed, and wearable sensing has been successfully deployed in everyday life. By combining sensors on smart watches or bands, users can now analyze heart rate during the day, monitor sleep, and much more.
E-textiles are being used in physical rehabilitation with different applications. Several works discuss the potential benefits of smart textiles, such as wearable sensors and textile-based rehabilitation systems, in improving patient outcomes and enhancing the rehabilitation process. Some of the works present several approaches to e-textile use in the field of physical rehabilitation 
SensFloor® is a smart floor equipped with gait recording technology that provides useful data for health status assessments in neurology, geriatrics, and rehab. The data gathered by SensFloor® includes speed, average number of steps, and other relevant information which can be analyzed to recognize asymmetric and unstable gait patterns. The data is structured and saved for medical records, and according to FutureShape, SensFloor® is also suitable for rollators and other walking supports.
In addition to SensFloor®
, other technologies such as e-textiles are being used to improve gait analysis and indoor location monitoring. Lauterbach et al. introduced the smart floor system as an alternative to users carrying sensors or devices, and it relies on e-textiles for conductive measurements. However, the use of pressure sensors can provide more critical information about foot plantar force distribution during gait 
. As demonstrated by Monge et al. using a set of insoles embedded with an optical fiber sensor network that employs fiber Bragg gratings (FBGs). The system provides feedback to physiotherapists on the patient’s gait and motion patterns, with high accuracy and sensitivity. The device also comes with companion software tools that make gait analysis easier for the therapist. Although this proposed device provides a more accurate and sensitive approach to gait analysis than traditional systems, including the SensFloor®
, it utilizes non-unobtrusive sensing since it requires the user to wear a set of hardware devices, which can affect the results in a rehabilitation scenario 
Sensors have become increasingly important in the field of healthcare in recent years. Many sensors are now Internet-connected and feature advanced processing, allowing them to provide smart functions that go beyond simple measurements. This has allowed for the unobtrusive, real-time monitoring of people’s lives as well as the collection of massive amounts of data in a natural setting. Studies have shown that using smart sensors in health research has high potential, but a deeper understanding of their underlying concepts is required to fully realize that potential. Garatva et al. provides a theoretical, non-technical overview of the fundamental concepts of smart sensors in mobile health sensing, with a focus on those that are currently embedded in smartphones. It provides a brief overview of available sensors, proposes a broad taxonomy, and discusses what can be sensed and how health can be predicted using sensor data. The paper also discusses current challenges, future research perspectives, and potential clinical applications, while providing examples of research projects based on smartphone sensors 
Unobtrusive systems such as Microsoft Kinect show promising results in incorporating the concepts of gamification and serious games to several rehabilitation and healthcare areas 
, from using hand gestures for elderly care 
to post-stroke rehabilitation 
. However, other non-hands-free similar devices, such as Nintendo Wii, showed several injuries related to overuse or incorrect use of handheld controller, amongst them tendinopathy and hand lacerations 
Another type of unobtrusive system is the sensing floor, which can be a alternative to wearable sensors. The PLEINAIR project developed outdoor park equipment called “outdoor smart objects” (OSO) which encourages physical activity and active lifestyles. OSO is a smart and sensitive flooring with pressure sensors and visual feedback for an interactive user experience. The project utilized distributed intelligence and connected the OSOs to the Cloud infrastructure. Challenges were addressed regarding pressure sensitivity and system scalability. Cocconcelli et al. further explored unobtrusive sensors in the form of smart sensing floors in their work 
2.5. AI in Healthcare
AI has shown tremendous potential in a wide range of applications, and healthcare is no exception. In recent years, the integration of AI in healthcare has revolutionized various aspects of patient care, diagnosis, and treatment. This section will discuss significant applications of AI in healthcare, more specifically on AAL applications:
Activity recognition and monitoring: AI-powered activity recognition systems can automatically identify and analyze the activities of individuals in their living environment. These systems typically use sensors, cameras, or wearable devices to collect data and employ machine learning algorithms to recognize and interpret the patterns of daily living 
. By monitoring the daily routines of individuals, AAL systems can detect unusual behaviors or changes in patterns, which may indicate health issues or potential risks, and alert caregivers or medical professionals accordingly.
Fall detection and prevention: Falls are a significant concern for the elderly, as they can lead to severe injuries, loss of independence, and a decline in overall health. AI-based fall detection and prevention systems can analyze sensor data from wearable devices, cameras, or floor sensors to identify fall-related events and trigger alarms or notifications to caregivers or emergency services 
. Furthermore, AI can also be employed to predict the risk of falling based on gait analysis, enabling the implementation of preventive measures to minimize the risk.
Cognitive assistance: Cognitive assistance systems utilize AI to support individuals with cognitive impairments or dementia by providing reminders, guidance, and personalized interventions to help them perform daily activities and maintain their cognitive abilities 
Telemedicine and remote patient monitoring: AI can enhance telemedicine and remote patient monitoring by analyzing data collected from various sensors, wearable devices, and medical equipment in order to provide accurate and timely health assessments, personalized feedback, and recommendations for patients and healthcare professionals 
. This enables the efficient and continuous monitoring of patients’ health, especially those with chronic conditions, reducing the need for frequent hospital visits.
In summary, the integration of AI in AAL is a promising avenue for enhancing the quality of life, safety, and well-being of individuals, particularly the elderly and those with disabilities. By providing continuous monitoring, personalized assistance, and timely interventions, AI-powered AAL solutions can enable individuals to maintain their independence, dignity, and health, while reducing the burden on caregivers and the healthcare system.