Abstract
In this chapter, we describe the existing AI devices aimed at PA monitoring and the most relevant AR software focusing on movement. Our review will present the latest findings, classified according to their accessibility and economic cost, and will describe the main characteristics, the basic instructions for use, and the practical applications of each of them. Within the domain of physical activity (PA), artificial intelligence (AI) has been applied primarily in the form of smart wearable devices, with the aim of improving people’s living standards. Through the use of wearables (Xiaomi Smart Band, Fitbit, Apple Watch) and cameras (Alfa AI, Zenia, Peloton Guide, AndroVideo, Sportvu 2.0), AI can monitor the user’s movements and physiological signals and can provide feedback about them. Augmented reality (AR) is now being employed to visualise, promote and motivate new forms of human movement. Although there are very innovative initiatives aimed at promoting the practice of PA in a virtual environment, very little is currently known about these environments. The use of AR has given rise to a myriad of proposals in all fields of motor research and development, ranging from psychomotor processes in children (FitnessMeter, AR Runner, DribbleUp) to educational/sports PA practice (Lü interactive playground, BEAM interactive floor, HADO), PA and health (PostureScreen, Complete Anatomy 2022), high-performance sport (My Jump Lab, Capture.U) and programs to promote PA in the elderly (Obie interactive projector, CyberCycle). There is software that uses both AI and AR, for example Alfa AI, Capture.U and CyberCycle. The use of these two technologies allows for the practice of PA in a new dimension, and enables personalised, real-time monitoring of a user’s activity through innovative exercises.
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1 Introduction
In recent decades, smart technology has become an essential element in the practice of physical activity (PA), as it can protect health, enable better decision making, and bring access to sport to more people. Numerous technological devices have been designed to promote health and the practice of PA, and advances in artificial intelligence (AI) and augmented reality (AR) have strong potential in terms of monitoring and motivating the practice of PA for every individual, regardless of their age and characteristics.
The aim of AI is to develop intelligent systems that mimic aspects of human behaviour, such as learning, perception, reasoning and adaptation (de Moraes et al. 2020). AI can be defined as the ability of a system to interpret and learn from external data and apply learning outcomes to achieve specific goals and solve problems through flexible adaptation (Wei et al. 2021). In the domain of PA and sport, AI enables the monitoring and recording of physiological data such as heart rate and energy expenditure, and the assessment of movement quality (Brickwood et al. 2019; Kristoffersson and Lindén 2022; Li et al. 2021). These types of information can promote the self-regulation of one’s own PA, goal setting and the creation of action plans (Li et al. 2021). In addition, AI can provide feedback and create personalised training based on recorded data (Kristoffersson and Lindén 2022; Wei et al. 2021).
Also, AR can incentivise PA practice through the application of game dynamics in interactive environments. Exercise enhanced by AR is considered a new approach to exercise that can prevent non-communicable diseases within a population (Ng et al. 2019). Furthermore, the use of AR devices creates increases in PA and the motivation of users (Kosa and Uysal 2022; Nekar et al. 2022).
In view of this, the use of this technology has the potential to improve the health and well-being of the world’s population. According to the WHO (2020), 28% of adults and more than 80% of adolescents have an insufficient level of PA. Physical inactivity is one of the main risk factors for chronic disease, mental health problems and increased adiposity (Cho et al. 2021; Li et al. 2021; WHO 2020). In order to combat this problem, new methods are being sought to encourage the practice of PA, such as the creation of programs or interventions aimed at its promotion (Creaser et al. 2021; Winand et al. 2020) and more concrete, accessible and attractive guidelines for the general population (Smith and Wightman 2021).
One of the key factors that can explain a lack of sustained PA over time is a lack of motivation (Winand et al. 2020). Other obstacles that have been found to prevent people from engaging in PA are social factors such as the cost of activities, the availability of transport, a lack of family support, and limited time availability (West et al. 2021). For this reason, there is a need to explore new technology-based intervention methods that are effective, accessible, and offer greater availability (Ni et al. 2019; Winand et al. 2020). Recent studies have claimed that technological interventions could lead to higher levels of PA and active participation in sport (Cho et al. 2021; Ni et al. 2019).
In order to bring this technology closer to the general population and to encourage its use in PA practice, we review and classify the AI devices intended for PA monitoring and the most relevant AR software focusing on movement and motor development. The latest findings are classified based on the type of device, its accessibility and the economic costs involved. We also describe the main features and basic instructions for use, as well as some of the practical applications of each device.
2 Artificial Intelligence Devices for Physical Activity Monitoring
AI may become very important in the field of PA, due to the relative difficulty of accurately analysing activity without expensive equipment. In conjunction with a lack of sports training of most people, this could lead to poor decision making that negatively affects their health (Wei et al. 2021). However, this can be avoided through the use of AI, which is capable of monitoring and supervising all training with the aim of giving information to the user and can even analyse all of the collected data to provide recommendations to the user on how to progress in their training (Ferguson et al. 2021; Wei et al. 2021). Furthermore, with these devices, the cost of personal trainers or gym memberships and the time spent travelling to fitness centres could be reduced (D’Amore et al. 2021; Oh et al. 2021).
Advances in this field allow sportspeople to improve their performance and avoid injury, thanks to the information provided by AI on breathing, heart rate and effort level. The most commonly used devices are wearables such as the Xiaomi Smart Band or Apple Watch. These devices synchronously record real-time physiological data such as daily steps, heart rate and energy expenditure (Brickwood et al. 2019). In addition to these, AI-enabled camera systems (smartphone or other specific devices) can be employed that are capable of collecting a wide range of biomechanical data from the user. In the following sections, we review the functionality of several types of devices and some of the most commonly used mobile applications offered for PA monitoring.
2.1 Wearables
Wearables are small portable devices that can take the form of accessories or clothing. They are designed to use modern information networks and various sensor technologies to collect, transmit and analyse the physiological and health parameters of the human body, in order to monitor the overall physical state of an individual (Nahavandi et al. 2022; Tang et al. 2020; Wei et al. 2021). Some of these parameters include energy expenditure, distance travelled, heart rate and sleep.
These devices can monitor the entire physical training process and can provide users with actionable advice through a big data analysis system. For example, wearables can accurately measure user-specific data during exercise, such as heart rate, and can issue warnings when a safe heart rate is exceeded. A diagram illustrating how the information collected by wearables is processed before being presented to the user is shown in Fig. 14.1.
Wearables are affordable, visually appealing, easy to use, and encourage an active lifestyle (Ferguson et al. 2021; Li et al. 2021; Tang et al. 2020). There is evidence that wearable devices are an effective way to increase steps and moderate-to-vigorous PA and can reduce sedentary behaviour in both young people (Creaser et al. 2021) and adults (Brickwood et al. 2019). Although there is a great variety of wearables, such as smart glasses or smart clothes, the most common are smart watches or smart bands, due to their accessibility and ease of use. In the following, we describe some current wearables, along with their main features and functions.
Xiaomi Smart Band: This model is one of the most accessible on the market. It has a wide variety of functionalities and sensors that allow it to monitor a multitude of biometric markers including heart rate, sleep quality, stress monitoring and breathing. From these data, the user can see a summary of his or her activity; however, for more detailed information on all metrics, the device must be linked to the Zepp Life app. It records all biometrics and provides feedback on sleep and workouts, and also offers video tutorials that aim to motivate the user and help to create a more active and healthy daily routine (Fig. 14.2).
This smart band model is designed for any user and aims to monitor some important variables for health.
Fitbit Charge 5: This has more functionalities and more sophisticated sensors than the Xiaomi Smart Band. Fitbit is able to perform electrocardiograms and to monitor electrodermal activity and oxygen saturation. In addition, this model has an integrated GPS, which is ideal for runners. This device also requires synchronisation with the Fitbit app to allow the user to see all of the relevant statistics and to access more functionalities; challenges, health and training programs are also provided (Fig. 14.3).
This smart band is more complete than the Xiaomi Smart Band, and its price is therefore higher. It is designed for more active users who want to monitor their health status with great accuracy.
Apple Watch Series 7: This smart watch has a large number of sophisticated biosensors, and a wide range of functionalities and applications that can be used without the need to link to a smartphone. It can be used to browse all the data collected, and various kinds of applications with a focus on the practice of PA and health can be installed (Fig. 14.4).
The Apple Watch is not a device with the sole purpose of monitoring activity and health, and also has many productivity and entertainment applications that can be accessed at any time from the wrist.
To choose the wearable that best suits the user’s needs, several aspects must be taken into account, such as the type of sensors included, the autonomy of the device, and its price. Table 14.1 shows the specifications and price of each of the wearables described above.
2.2 Artificial Intelligence Camera Systems
AI camera systems consist of cameras and computers that are capable of capturing and automatically identifying people and analysing their movements by comparing them with the information available to the AI, in order to provide feedback on exercise performance (Wei et al. 2021). Although the information in a video can help users to analyse their performance, users can correct and assess all their movements more accurately with the help of AI analysis. A diagram of how AI camera systems obtain and process data before presenting information to the user is shown in Fig. 14.5.
The AI system detects each individual and their body structure. Following this, the system collects information about the execution of each exercise and compares it with the references stored in its database. It then calculates the level of coincidence and locates any errors in the execution. The system is able to estimate the amount of PA performed by individuals and to calculate the calories consumed (Cruz et al. 2021). Moreover, users can browse all the statistics related to their training and observe which aspects can be improved.
AI camera systems have two major advantages: the first is the comfort they offer the user when performing movements, since these systems are not invasive or uncomfortable, while the second is the amount of information that can be displayed in image or video format, making it easy to understand by any user (Mabrouk and Zagrouba 2018). However, this type of system is very expensive, and is not very feasible to install in a sports venue. Nevertheless, in places such as gyms or fitness centres, access to such devices may be valuable for performance improvement and injury prevention. In addition, it should be noted that the accuracy of these systems can be reduced by changes in lighting, frequent alterations in the scene background, the type of clothing worn by the subject and other disturbances (Mabrouk and Zagrouba 2018; Wang et al. 2019). Overall, however, AI camera systems are good tools for encouraging and improving PA practice, both for beginners (cheaper devices) and for advanced athletes (more expensive devices).
In the field of PA, this technology is mainly used to control the execution of exercises and to monitor a wide range of statistics for the entire training process. In this chapter, we review four of the most widely used AI camera systems in use today, from the most accessible to the least.
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Alfa AI and Zenia are smartphone fitness apps that are capable of “replacing” personal trainers. These apps offer training sessions that monitor and provide real-time feedback on each exercise for optimal training. Alfa IA is dedicated to fitness exercises such as planks, push-ups and climbers, while Zenia focuses on yoga (Fig. 14.6). The biggest advantage of these apps is that they can be used anytime, anywhere. In addition, there are a wide variety of applications focused on PA tracking that are similar to these, with different functionalities and features, some of which are free and others are paid.
These applications are intended for personal use, and anyone can purchase them to start performing PA on a regular basis. For a more successful experience, personal motivation and some experience in sports practice are recommended. Both require monthly subscriptions starting at $5.
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Peloton Guide consists of a camera system connected to a television, which shows how to perform training, monitors the execution of movements, and provides feedback in real time (Fig. 14.7). This system provides recommendations for programs and workouts that can improve a training routine based on the data collected during each session. Another well-known example of a device that is similar to this technology is Kinect for Xbox.
This device is aimed at the general public, for personal use. It is not as accessible as a wearable, but it is capable of creating and presenting instructing workout routines while monitoring the execution of each exercise, in the same way as a personal trainer. Currently, it requires an outlay of approximately $300.
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AndroVideo’s AI camera systems detect the position of the user’s body, provide real-time posture correction, automatically record the user's condition, display post-workout recommendations, and create a history log (Fig. 14.8). This is device is aimed at users of gyms or fitness centres. It is similar to Peloton Guide but acts as an artificial assistant rather than a personal trainer. It focuses on posture correction, exercise guidance, and condition monitoring. Using this device, trainers and users can access their exercise history and their statistics, in order to adapt their training plan according to their needs and goals.
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SportVU 2.0 is a multi-camera system that collects high quality images of complex game situations, in order to provide accurate tactical (player positions) and physical (physical condition of each player) data. This system is used in elite sports such as the NBA and professional soccer leagues. Its function is to follow the ball and all of the players, and to provide statistics on their positions on the court in real time (Fig. 14.9). All of this information is intended to be used by teams to improve their performance. Using these systems, game broadcasts can show data about the game, such as the percentage of possession, the percentage of successful three-pointers, the maximum speed reached or the distance travelled by each player. However, this is a device aimed exclusively at elite teams with high budgets; for example, for use in the NBA, SportVU 2.0 charges about $100,000 per team.
3 Augmented Reality Software for Movement and Motor Development
AR is a technology that involves generating new images, sounds or text from digital information in a real physical environment, thus simulating an environment in which the artificial and the real are mixed (Gil et al. 2021). With AR, the surrounding world becomes interactive and digitally manipulable. The use of AR opens up a wide range of possibilities in fields of research related to movement and motor development, from psychomotor processes in children to the promotion of PA in the elderly. Its exponential growth in recent years has also allowed increasing numbers of people to access this technology through smartphones, computers and other devices (Nekar et al. 2022).
Through AR, children can develop their abilities to perceive, recognise and process various objects and situations; there is also the possibility of adding, removing or modifying virtual aspects with the aim of personalising the experience according to their needs and abilities (Soltani and Morice 2020). In addition, several studies have revealed that AR games can increase daily PA levels and time spent outdoors (Bonus et al. 2018; Kosa and Uysal 2022; Ruiz-Ariza et al. 2018) and can stimulate players’ development and cognition (Ruiz-Ariza et al. 2017; Soltani and Morice 2020).
AR systems feed augmented information to players’ sensory modalities to help them understand the real-world environment (Soltani and Morice 2020). Mobile AR games have the potential to make environmental exploration enjoyable (Winand et al. 2020) and to create positive life experiences (Bonus et al. 2018). There are currently a number of AR games on the market, the most famous example of which is Pokémon GO (Ruiz-Ariza et al. 2018). In this game, AR is used to project images of Pokémon onto images of the real environment, using a cell phone camera. In both youths and adults, increases in moderate PA and numbers of steps were detected up to seven months from its first use (Baranowski and Lyons 2020).
Existing evidence indicates that PA practice and motivation can be improved through AR devices (Gil et al. 2021). Training with AR can not only replace traditional training but can be even more effective in developing certain skills or abilities such as balance, strength or muscular endurance (Nekar et al. 2022). At the same time, the experience of AR games contributes to well-being, as being voluntarily present in a virtual environment can elevate mood, regulate emotions and provide a healthy escape (Kosa and Uysal 2022).
In the domain of high-performance elite sport, AR is used by athletes, coaches and researchers. This technology can mine biomechanical data to project simulations onto the users of the movements they are performing. All of the information provided is used for movement correction and to improve the athlete’s technique (Bolam et al. 2021). In addition, it can also be used as a validated assessment tool for various physical tests, such as jumping or running speeds (Bishop et al. 2022).
For the elderly, AR systems serve as a tool that can encourage rehabilitation through fun and entertainment (Jeon and Kim 2020). In studies of AR, seniors were interested and motivated by exercise, and exercise control was found to be easy for them (Jeon and Kim 2020). Table 14.2 briefly describes the main features of the AR applications and devices reviewed in this chapter, and they are described in more detail below.
3.1 Psychomotor Processes in Children
FitnessMeter: This is an advanced timing and measurement tool that can be used to perform various physical fitness tests. It is aimed at both coaches and physical education teachers, to allow them to evaluate their pupils and for personal use. It is capable of measuring speed, agility, jump height and general fitness through various tests: sprinting over 10 or 20 m, vertical jumping, the Course Navette test, and repetitions of push-ups, sit-ups or other exercises (Fig. 14.10).
AR Runner: This is an AR game which uses the camera to create a series of virtual control points over the terrain, which must be traversed as quickly as possible. All of the information is displayed on the phone screen (Fig. 14.11), and it is possible for a user to compete with other players from all over the world.
DribbleUp: Smart equipment such as soccer balls, basketballs, medicine balls and boxing gloves can be linked to a smartphone via the DribbleUp—Sports & Fitness app. The app collects metrics such as crossing speed, dribbling motion, resistance, repetitions and other variables, via the cell phone camera and equipment sensors (Fig. 14.12). The objective is to improve the user’s technique in different sports skills through exercises that are presented in the form of challenges, in the same way as a video game.
3.2 Educational/Sports Physical Activity
Lü interactive playground: Lü is an intelligent spatial environment that transforms a gymnasium or other indoor space into an interactive play area. It creates a virtual learning zone where young people can move and perform PA, in an attractive and innovative way.
This system consists of one or two video projectors, a computer, a 3D video camera, six static lights, two computerised lights and two loudspeakers. The complete system is able to project interactive games onto the walls, thus creating an interactive space in which young people can perform different innovative physical activities (Fig. 14.13). Lü offers various activities that encourage teamwork, competition and self-improvement through point systems. The different games that are available include subject matter such as mathematics, science, geography and foreign languages.
BEAM interactive floor: This is an interactive projection system that is focused on the floor, and allows several people to interact with it at the same time. It offers more than 200 educational games through which young people can learn via motion. BEAM can not only develop gross and fine motor skills, but also works on different aspects of cognition (memory, attention or perception). This system provides children and adolescents with the opportunity to explore, discover and interact through movement and fun (Fig. 14.14).
HADO: This is a new AR e-sport that can be played individually or in teams. Players are equipped with two smartphones: one is placed in a helmet to function as AR glasses, and the other is positioned in an armband to act as a motion sensor (Fig. 14.15). Players can see and interact with virtual elements added to the real world.
The main game offered by the HADO system is a competitive e-sport between two teams of three players. Each game lasts 80 s, and the team with the most points wins. Each team member must throw energy balls at the opposing team's players; if the opponent's body is hit, he or she loses one life cell. Each player has four life cells, and if all of these are lost, the other team gains one point. In addition to throwing energy balls, each player can create an energy shield for protection, and the balls can also be dodged. A bar is shown for each player, which indicates the level of energy available and limits the use of energy balls and shields (Fig. 14.16).
HADO is a very successful e-sport in Japan and is becoming increasingly widespread throughout the world. It is a new way of practicing sports that can be very attractive to the younger generation. It is an innovative device that illustrates the wide range of possibilities that AR can offer to the practice of PA, in terms of both promoting its practice and developing different motor skills.
3.3 Physical Activity and Health
PostureScreen: This is a smartphone app that is available for both Android and IOS. It is an objective assessment tool for posture and movement that is capable of presenting and automatically emailing the results to the patient. It also offers customised, safe and functional postural exercises through another application called WebExercises to which it is linked (Fig. 14.17).
The use of this app for postural assessment is supported by scientific studies, such as those by Boland et al. (2016) and Szucs and Brown (2018), which report that the app demonstrates strong reliability and validity and can be employed in both clinical and research settings.
Complete Anatomy 2022: This is a 3D anatomy mobile application platform with unique collaboration and learning tools. The user can interact with the model by selecting an element (muscle, organ, blood vessel etc.) and exploring and labelling the structures in real time. The program simulates the condition and details of the human body, allowing a user to explore human anatomy in depth using the cell phone or tablet as a “viewer” (Fig. 14.18).
The app has a set of tools that can cut out the layers that make up the body, allowing the user to explore the relationships between structures (muscular, skeletal, vascular), simulate injuries and pathologies, animate pain points in the model, and add labels directly to a selected structure.
3.4 High-Performance Sports
Capture.U: The Vicon application collects and analyses biomechanical information from the human body in real time, and displays a simulation overlaid on the real image. This allows the user to make informed decisions in different situations, such as on the playing field, on the track, in the pool, on the court or in the lab. For full data collection, the user is required to wear Blue Trident sensors.
This application has a visualisation mode based on AR, in which 2D, 3D or both types of visualisations are shown at the same time (Fig. 14.19). There is also the option to select a particular joint of interest to view its kinematic data, positions and opening angles.
Some studies, such as that of Bolam et al. (2021), report that this application can be used both in rehabilitation for movement correction and in high-performance sports to improve execution.
My Jump Lab: This is an IOS application that performs the function of a portable and validated performance evaluation laboratory. No additional accessories are required for its use. Simply by using the smartphone’s camera and sensors, the app is able to accurately record data on different physical skills (Fig. 14.20). With My Jump Lab, more than 30 tests can be performed, including jumping (counter movement jump, squat jump, repeated jumps, etc.), running and sprinting (contact/flight times, leg asymmetry, pronation/supination, etc.) and range of motion (dorsiflexion, neck extension/flexion, hip flexion and internal rotation, and shoulder flexion and internal rotation). This app is used by coaches, elite athletes, physiotherapists and researchers around the world. Its accuracy was validated by Bishop et al. (2022), who indicated that the app was a good measuring instrument for all the physical tests offered.
3.5 Promotion of PA in the Elderly
Obie interactive surface: This is a projector that can turn any floor, wall or table into an interactive surface. It uses sensors to detect movements such as bumps, taps, or shakes, and has more than 200 games to stimulate PA or cognitive skills (Fig. 14.21). The animated games are customised to different levels, and may include memory tests, musical contests, or speed coordination challenges.
CyberCycle: This is an interactive exercise bike that includes a virtual environment in which games, group rides, and competitions can be held to engage users in PA. A 19-inch HD monitor is used to immerse users in a virtual race, in which they ride through a 3D scenario (Fig. 14.22). This system is particularly suitable for the elderly, as it provides stimulation and keeps both body and mind fit.
4 Conclusions
AI and AR devices can encourage and enhance PA, as they increase motivation levels, monitor activity and optimise training. The use of either these technologies has great advantages for users, but the benefits are further enhanced by employing both simultaneously; this takes the practice of PA to a new level, as it allows for personalised real-time monitoring of activity via the big data processing enabled by AI and encourages PA with innovative activities offered through AR. For example, wearables can be used to monitor vital signs while exercising with AR systems (AR Runner, DribbleUp or Lü interactive playground), and the intensity with which the activities were performed can be checked at a later stage. Some of the devices and applications described above (Alfa AI, Zenia, Capture.U or DribbleUp) already use both technologies, where AI analyses the movements and presents feedback via AR, thus providing very complete information allowing the user to understand and correctly follow the instructions.
Emerging technologies such as AI and AR provide new opportunities for monitoring and promoting PA on a larger scale, for both personal and group use. Promoting the use of wearables and AR applications for smartphones or other devices could not only generate health and performance benefits at the individual level but could also improve the collective health of the population (Gal et al. 2018). Likewise, interventions aiming to promote PA can incorporate devices based on AI and AR, with the objective of expanding the possibilities and generating interest for a greater number of people.
Finally, as observed in this chapter, AI and AR are used in many fields of PA, from the promotion of PA at all ages to high-performance sports. In view of this, and due to the increasing accessibility of equipment with a multitude of highly reliable functionalities, the use of this technology has the potential to completely change the nature of PA and to create a more physically active society.
References
Baranowski T, Lyons EJ (2020) Scoping review of Pokémon Go: comprehensive assessment of augmented reality for physical activity change. Game Health J 9(2):71–84. https://doi.org/10.1089/g4h.2019.0034
Bishop C, Jarvis P, Turner A, Balsalobre-Fernandez C (2022) Validity and reliability of strategy metrics to assess countermovement jump performance using the newly developed my jump lab smartphone application. J Hum Kinet 83(July):185–195. https://doi.org/10.2478/hukin-2022-0098
Bolam SM, Batinica B, Yeung TC, Weaver S, Cantamessa A, Vanderboor TC, Yeung S, Munro JT, Fernandez JW, Besier TF, Monk AP (2021) Remote patient monitoring with wearable sensors following knee arthroplasty. Sensors 21(15):1–12. https://doi.org/10.3390/s21155143
Boland DM, Neufeld EV, Ruddell J, Dolezal BA, Cooper CB (2016) Inter- and intra-rater agreement of static posture analysis using a mobile application. J Phys Ther Sci 28(12):3398–3402. https://doi.org/10.1589/jpts.28.3398
Bonus JA, Peebles A, Mares ML, Sarmiento IG (2018) Look on the bright side (of media effects): Pokémon Go as a catalyst for positive life experiences. Media Psychol 21(2):263–287. https://doi.org/10.1080/15213269.2017.1305280
Brickwood KJ, Watson G, O’brien J, Williams AD (2019) Consumer-based wearable activity trackers increase physical activity participation: systematic review and meta-analysis. JMIR mHealth uHealth 7(4). https://doi.org/10.2196/11819
Cho I, Kaplanidou K, Sato S (2021) Gamified wearable fitness tracker for physical activity: a comprehensive literature review. Sustainability (switzerland) 13(13):1–15. https://doi.org/10.3390/su13137017
Creaser AV, Clemes SA, Costa S, Hall J, Ridgers ND, Barber SE, Bingham DD (2021) The acceptability, feasibility and effectiveness of wearable activity trackers for increasing physical activity in children and adolescents: a systematic review. Int J Environ Res Public Health 18(12). https://doi.org/10.3390/ijerph18126211
Cruz BS, Aguía K, Perdomo OJ (2021) Monitoring and evaluation of people in indoors and outdoors using deep learning. 78. https://doi.org/10.1117/12.2606334
D’Amore C, Reid JC, Chan M, Fan S, Huang A, Louie J, Tran A, Chauvin S, Beauchamp MK (2021) Smart technology vs. face-to-face physical activity interventions in older adults: a systematic review protocol. JBI Evid Synth 19(10):2801–2812. https://doi.org/10.11124/JBIES-21-00072
de Moraes, MHB, Ferreira DD, Ferreira AC, da Silva GR, Caetano AS, Braz, VN (2020) Use of artificial intelligence in precision nutrition and fitness. In: Debmalya B (ed) Artificial intelligence in precision health. Elsevier. https://doi.org/10.1016/b978-0-12-817133-2.00020-3
Ferguson T, Olds T, Curtis R, Blake H, Crozier AJ, Dankiw K, Dumuid D, Kasai D, Connor EO, Virgara R, Maher C (2021) Review effectiveness of wearable activity trackers to increase physical activity and improve health: a systematic review of systematic reviews and meta-analyses. Lancet Dig Health 4(8):e615–e626. https://doi.org/10.1016/S2589-7500(22)00111-X
Gal R, May AM, van Overmeeren EJ, Simons M, Monninkhof EM (2018) The effect of physical activity interventions comprising wearables and smartphone applications on physical activity: a systematic review and meta-analysis. Sports Med Open 4(1):1–15. https://doi.org/10.1186/s40798-018-0157-9
Gil MJV, Gonzalez-Medina G, Lucena-Anton D, Perez-Cabezas V, Ruiz-Molinero MC, Martín-Valero R (2021) Augmented reality in physical therapy: systematic review and meta-analysis. JMIR Serious Games 9(4):1–20. https://doi.org/10.2196/30985
Jeon S, Kim J (2020) Effects of augmented-reality-based exercise on muscle parameters, physical performance, and exercise self-efficacy for older adults. Int J Environ Res Public Health 17(9). https://doi.org/10.3390/ijerph17093260
Kosa M, Uysal A (2022) Effects of presence and physical activity on player well-being in augmented reality games: a diary study. Int J Hum-Comput Interact 38(1):93–101. https://doi.org/10.1080/10447318.2021.1925437
Kristoffersson A, Lindén M (2022) A systematic review of wearable sensors for monitoring physical activity. Sensors 22(2). https://doi.org/10.3390/s22020573
Li C, Chen X, Bi X (2021) Wearable activity trackers for promoting physical activity: a systematic meta-analytic review. Int J Med Inf 152(December 2020):104487. https://doi.org/10.1016/j.ijmedinf.2021.104487
Mabrouk A, Zagrouba E (2018) Abnormal behavior recognition for intelligent video surveillance systems: a review. Expert Syst Appl 91:480–491. https://doi.org/10.1016/j.eswa.2017.09.029
Nahavandi D, Alizadehsani R, Khosravi A, Acharya UR (2022) Application of artificial intelligence in wearable devices: opportunities and challenges. Comput Methods Programs Biomed 213(December). https://doi.org/10.1016/j.cmpb.2021.106541
Nekar DM, Kang HY, Yu JH (2022) Improvements of physical activity performance and motivation in adult men through augmented reality approach: a randomized controlled trial. J Environ Public Health 2022:3050424. https://doi.org/10.1155/2022/3050424
Ng YL, Ma F, Ho FK, Ip P, Fu KW (2019) Effectiveness of virtual and augmented reality-enhanced exercise on physical activity, psychological outcomes, and physical performance: a systematic review and meta-analysis of randomized controlled trials. Comput Hum Behav 99(September 2018):278–291. https://doi.org/10.1016/j.chb.2019.05.026
Ni MY, Hui RWH, Li TK, Tam AHM, Choy LLY, Ma KKW, Cheung F, Leung GM (2019) Augmented reality games as a new class of physical activity interventions? The impact of Pokémon Go use and gaming intensity on physical activity. Games Health J 8(1):1–6. https://doi.org/10.1089/g4h.2017.0181
Oh YJ, Zhang J, Fang ML, Fukuoka Y (2021) A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss. Int J Behav Nutr Phys Act 18(1):1–25. https://doi.org/10.1186/s12966-021-01224-6
Ruiz-Ariza A, Grao-Cruces A, Marques NE, Martínez-López EJ (2017) Influence of physical fitness on cognitive and academic performance in adolescents: a systematic review from 2005–2015. Int Rev Sport Exerc Psychol 10(1):108–133. https://doi.org/10.1080/1750984X.2016.1184699
Ruiz-Ariza A, Casuso RA, Suarez-Manzano S, Martínez-López EJ (2018) Effect of augmented reality game Pokémon GO on cognitive performance and emotional intelligence in adolescent young. Comput Educ 116:49–63. https://doi.org/10.1016/j.compedu.2017.09.002
Smith B, Wightman L (2021) Promoting physical activity to disabled people: messengers, messages, guidelines and communication formats. Disabil Rehabil 43(24):3427–3431. https://doi.org/10.1080/09638288.2019.1679896
Soltani P, Morice AHP (2020) Augmented reality tools for sports education and training. Comput Educ 155(June 2019):103923. https://doi.org/10.1016/j.compedu.2020.103923
Szucs K, Brown E (2018) Rater reliability and construct validity of a mobile application for posture analysis. J Phys Ther Sci 30(1):31–36. https://doi.org/10.1589/jpts.30.31
Tang MSS, Moore K, McGavigan A, Clark RA, Ganesan AN (2020) Effectiveness of wearable trackers on physical activity in healthy adults: systematic review and meta-analysis of randomized controlled trials. JMIR Mhealth Uhealth 8(7):1–13. https://doi.org/10.2196/15576
Wang Y, Cang S, Yu H (2019) A survey on wearable sensor modality centred human activity recognition in health care. Expert Syst Appl 137:167–190. https://doi.org/10.1016/j.eswa.2019.04.057
Wei S, Huang P, Li R, Liu Z, Zou Y (2021) Exploring the application of artificial intelligence in sports training: a case study approach. Complexity 2021. https://doi.org/10.1155/2021/4658937
West K, Purcell K, Haynes A, Taylor J, Hassett L, Sherrington C (2021) “people associate us with movement so it’s an awesome opportunity”: perspectives from physiotherapists on promoting physical activity, exercise and sport. Int J Environ Res Public Health 18(6):1–14. https://doi.org/10.3390/ijerph18062963
Winand M, Ng A, Byers T (2020) Pokémon “Go” but for how long? A qualitative analysis of motivation to play and sustainability of physical activity behaviour in young adults using mobile augmented reality. Managing Sport and Leisure 0(0):1–18
World Health Organization (2020) Physical activity. Retrieved from: https://www.who.int/news-room/fact-sheets/detail/physical-activity. Accessed 5 Oct 2022
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Solas-Martínez, J.L., Suárez-Manzano, S., De la Torre-Cruz, M.J., Ruiz-Ariza, A. (2023). Artificial Intelligence and Augmented Reality in Physical Activity: A Review of Systems and Devices. In: Geroimenko, V. (eds) Augmented Reality and Artificial Intelligence. Springer Series on Cultural Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-27166-3_14
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