Ecosystem services (ES), commonly defined as the benefits people get from ecosystems, are key components in improving human well-being. However, as land utilization transitions from forest and agricultural land to urban areas and industrial complexes, the continuous provision of ES is affected. To ensure sustainable development, conservation programs should be implemented that consider both the stakeholders' well-being while also maintaining ecosystem health and integrity. Consequently, to improve the strategic implementation of conservation programs, it is critical to understand stakeholders' preferences.Using an online survey, we elicited South Carolina residents' preference in prioritizing the target ecosystems and ecosystem services in the state. The results identified that the priority ecosystem service is water quality regulation. However, the residents' preference for water quality regulation does not discredit the importance of maintaining the continuous water supply provision. In terms of ecosystem preference, residents indicated that the forest ecosystem is the priority ecosystem to be conserved, particularly for younger residents, respondents with high income, and those in the midland and the upstate. This could be attributed to the forest's effect on the ecosystem services that these respondents receive, particularly towards water-related ecosystem services. Understanding the residents' preference provides information that could improve the state water plans and other potential policy implications to land use-land cover planning and landscape sustainability management. Ecosystems ranking; Ecosystem services ranking; Stakeholder approach; Conservation planning; Preference analysis; Ranking analysis This reciprocal relationship is the fundamental basis of Social-Ecological Systems (SES) or coupled human-environment systems (CHES) . Social-The basic foundation of the ES-based approach is that human and ecological well-being are tightly connected to the sustainable management of resources . Apart fImprovement of ES provides a variety of societal benefits. For instance, the application of green spaces and green infrastructures improves the urban environment while also contributing to flood mitigation, water quality improvement, and microclimate provision. These regulating ecosystem services contribute to human health by lowering human exposure to contaminated floodwaters, removing toxicants, trapping contaminants, and mitigating extreme temperatures . Socio-cIn contrast, land-use changes that favor urban expansion and industrialization negatively affect ecosystems, resulting in degradation and decline of ecosystem services . While iOther research endeavors focus on evaluating residents' Willingness to Pay (WTP) to support water quality improvement . A conseDespite the growing interest in adopting an ES-based management approach , their iStakeholders'This study is an effort to fill this gap in the literature. Specifically, we aim to examine South Carolina (SC) residents' preferences for what type of ecosystem and which ecosystem service should be targeted for the implementation of conservation programs. Furthermore, we evaluate the factors that affect their preferences.South Carolina is selected for several reasons. First, the state of South Carolina has abundant surface water sources. This is due to the state's geographic location, topography, and natural land cover. Seventy percent (70%) of the state's water source comes from the rivers and streams, while groundwater provides 30% of the SC water sources . The surDespite the importance of the state's ecosystem services, to the best of our knowledge, there is no study that has evaluated the residents' preference for conserving these services. Following the previous studies that linked the residents' preference to prioritizing water quality improvement as an ecosystem service , we hypoHowever, while water supply is critically important, other aspects of the ecosystem services such as water quality improvement could be left unchecked. Due to the continuously changing land use-land cover (LULC) from increased urban and residential areas, water quality is affected through an increase in non-point source pollution, unsustainable agricultural activities, urbanization, forest degradation, and landscape fragmentation . TherefoFurthermore, while state water plans are focused on regulating water supply and demand, the determination of values and what to measure as the value of an ecosystem are subjective interpretations and can be arbitrary . Further22.12 land area. The majority of its land use is composed of forest land (36%)South Carolina lies in the Southeast Region of the US, with approximately 83,000 kmSouth Carolina has four river-basin networks : SavannaTo understand the stakeholders' perception towards ecosystems and their services within the state, we surveyed 1500 households across SC using the online survey platform Qualtrics in 2019. The online survey was utilized as data collection because, as of 2017, most SC residents (79%) have access to the internet . A simplFurthermore, to consider the household's geographic location, the zip codes provided by the respondents were used to calculate the centroid of the zip code area through ArcGIS. This became a representation of the respondents' household location. The geographic location was used to correlate the respondents' characteristics and the proximity of their household to nearby environmental attributes such as stream quality and the presence of protected areas.2.2The survey instrument consisted of four sections. Section The survey instrument was pre-tested with 32 residents of SC. The pre-testing evaluated the initial knowledge and perception of the respondents towards ecosystem services and conservation programs. Also, the pre-testing provided final inputs to the list of the commonly known ecosystem and ecosystem services in the area resulting in a more robust questionnaire specifically designed for South Carolina residents. Lastly, the pre-testing evaluated the wording, timing, etc., of the survey instrument.The survey instrument was reviewed and approved by the Clemson University Institutional Review Board (IRB) to ensure that ethical guidelines on research activities involving human subjects are followed. The IRB approval number is IRB2018 – 139.2.3Respondents were provided a list of ecosystems and ecosystem services as their options to choose from. The list was based on a focus group discussion workshop conducted as part of the study's preliminary activities. Each respondent was asked to rank the options according to their preferred prioritization, considering a limited implementation budget for conservation programs to improve or enhance these services. This method was also done for different ecosystems to elicit the priority ecosystems according to respondents' preferences .Table 1Lij = Rank given for the ith option by the jth respondentRj = Number of variables ranked by the jth respondentNHenry Garrett's ranking technique was utilized to analyze the overall ranked preference. Garrett's ranking technique is used primarily to determine the collective rank of options using a score value . The ranith option. The rank scores for each option i are added to get the overall value of scores. Eventually, the mean value of scores is calculated by dividing the overall rank scores by the number of respondents. The mean value of scores is ranked highest to lowest to determine the hierarchy of options (The percent position estimated is converted through Garrett's table (Appendix 1) to determine the total rank score of the  options .2.4A maximum likelihood regression analysis was used to examine the respondents' characteristics that could have a statistically significant effect on their top-ranked options. Furthermore, the regression analysis provides information on which among the options will respondents likely select based on their differing characteristics. Since the nature of the dependent variable is categorical, the likelihood model that was used is a Multinomial Logistic regression or Multi-Logit regression .Pij is the estimated likelihood of choosing the option j for respondent i, or in the case of the study, the priority ecosystem or ES of respondent i. Furthermore, the numerator and the denominator depict the odds ratio of the chosen option in comparison to others. With α as a constant coefficient while βkj is a vector of coefficients corresponding to the vector of attributes Xkji. Attribute X could be any characteristic or attributes that have a significant contribution to the respondent's decision. The list of attributes used in the models is summarized in The Multi-Logit regression is a non-linear regression that deals with multiple categorical situations. The model assumes that the options presented to a decision-maker are mutually exclusive, hence not correlated with each other. The model estimates the likelihood of choosing one option over other options. Because the options among the ecosystems and ecosystem services are unordered, the Multi-Logit model analyzes the primary question of “What is the respondent's priority ecosystem and ES among the list of options?”. The Multi-Logit regression analyzes if a specific option is “more or less preferred” in comparison to another option. The equation of the regression is as follows:The satisfaction rating and preferences towards an ecosystem or ecosystem service were included in the exogenous variables. To simplify the categories as inputs to the regression model, the satisfaction rating was consolidated and reclassified into a dummy variable, taking a value of 1 (satisfied) or 0 (otherwise). Respondents who answered 4 or 5 in their satisfaction rating was reclassified into 1, while the other ratings were classified into 0.Following previous studies linking the demographic factors and its influence on environmental values and preferences , demograProximity to monitored ecosystems was included to represent a possible distance-effect of the quality of these ecosystems to the preference of the respondents. Monitored ecosystems are ecosystems that are regularly monitored and managed by authorities or landowners as indicators for environmental health. For this study, we focused on impaired streams - streams that did not meet the water quality standards and at least not open for public access due to water quality issues; water bodies such as lakes, large rivers, and ponds that are evaluated as with good quality ; and pro2.5The respondents' impression of the general state of their environment was also elicited using a 5-point Likert scale satisfaction rating (1 being the lowest and 5 being the highest). Survey participants were asked four general questions about aspects of the environment: (1) satisfaction with the overall quality of water, (2) satisfaction with the abundance or amount of water accessible to their household, (3) satisfaction with the quality of air within their area, and (4) satisfaction with the overall state of the environment in their area. This question could serve as a feedback mechanism for conservation managers and professionals to understand how residents perceive the current state of the ecosystem and ecosystem services. This could indicate their awareness on the state of the environment and influence their preference for deciding which ecosystem and ecosystem services should be prioritized.Since the respondents' satisfaction rating is highly localized, for visualization purposes, the satisfaction rating of each respondent was averaged per county to represent the overall mean satisfaction rating within the county. This captures the heterogeneity of the respondents' perceptions across the state. Furthermore, the county satisfaction ratings' median was utilized to measure the central tendency of the overall satisfaction per environmental attribute.33.1The demographic characteristics of the sample are reported in Results show that, in terms of educational attainment, most of the respondents have some college degree. Furthermore, the number of respondents with a bachelor's degree or higher resembles the statistics of the state and national population. In terms of annual household income, the sampling distribution is closely similar to the state and national household income distribution. Overall, the results of the demographic characteristics indicate that the sampled respondents represent the demographic characteristics of the population.The high frequency of unemployed respondents is not uncommon in online surveys since they can use online surveys as an extra income source . Further3.2The results of the satisfaction ratings are reported in Furthermore, the mean satisfaction rating by county was mapped in Although satisfaction ratings are not cardinal values, the result indicates that SC residents are satisfied with the amount of water they can access and the quality of air within their area. On the other hand, while the perceived satisfaction with water quality and overall quality of the environment is lower than the other two characteristics, this could serve as a baseline on the residents' perception. Therefore, future interventions can use these baseline satisfaction ratings to validate the program's effectiveness or further investigate possible issues and opportunities that could affect the residents' satisfaction.3.33.3.1Results of the Garrett ranking analysis (Appendix 6) are shown in Another notable result from the rank analysis is the rank between “air quality” and “wildlife and habitat conservation.” Although, as reflected in the satisfaction rating, respondents seem to be pleased with the state of air quality, it is also almost tied up with wildlife and habitat conservation. This goes to show that SC residents also place a high priority on the conservation of wildlife. One possible reason for this observation could be a socio-cultural attribution of wildlife-associated recreational activities in SC. This can also be commonly observed particularly in the southern region of the United States. Since wildlife-associated recreation generates economic benefits for the state of SC , this plSince the rank analysis identified water quality as the priority ecosystem service, we analyzed the possible factors which lead to this preference. Using the Multi-Logit model, we compared respondents' likelihood to choose water quality over water supply and other “non-water related” ES.The model evaluated the respondents' likely choice of priority ecosystem service between the baseline priority ES - water quality - and two other alternatives: water supply and other ES. 3.3.1.1Results in 3.3.1.2Comparing the resident's preference between water quality and other ES showed more variables affecting their choices, namely: satisfaction rating to water quality, socio-economic factors such as age and income, proximity to the nearest impaired stream, and if residents' household is located in the upstate region.Similar to the water supply, respondents that are satisfied with the quality of water are 38% more likely to prioritize other ES. This could imply that only when respondents are satisfied with water quality will they be more likely to prioritize other ES. This follows the results of previous studies showing that residents prioritize the improvement of water quality .Meanwhile, socio-economic covariates suggest that older respondents have a higher likelihood to prioritize water quality. Particularly, as respondents increase their age by a year, the likelihood that they will choose water quality as compared to other ES increases by 1%. This could possibly be associated with house ownership. Older respondents are typically homeowners (70% of the respondents), where access to adequate water quality is an essential component in owning a house in a specific area.Furthermore, in terms of income levels, results show that households with higher income levels are more likely to prioritize water quality than other ES. Particularly, households with an annual income of $50,000 to $99,999 are 19% more likely to prioritize water quality as compared to households with income lower than $50,000. Moreover, households with an annual income of more than $100,000 are 64% more likely to prioritize water quality as compared to households with income lower than $50,000. This could be associated with the cost of accessing a good quality of water. In recent years, households install filtration systems or simply buy bottled water to ensure high water quality for consumption . HousehoIn terms of the proximity to monitored ecosystems, only the distance to impaired streams showed a statistically significant effect. In this case, a 1-kilometer increase in distance between the respondent's household from the nearest impaired stream implies that these respondents are 18% more likely to choose other ES to be prioritized rather than water quality. This could be because respondents living farther from an impaired stream do not see or are not aware of an impaired stream's negative impact. Hence, the likelihood of prioritizing water quality over other ES also decreases.Residents living in the upstate region are 38% more likely to prioritize other ES compared to those in the low country or coastal areas. This could be attributed to the satisfaction rating of the upstate residents to water quality. Overall, 87% of the respondents from the upstate gave a satisfactory rating to the water quality, while 85% for the low country. Since more residents in the upstate are satisfied with the water quality, this could be the reason why they are more likely to choose other ES as compared to low country/coastal residents.Overall, the result for the intercepts in both comparisons showed to be statistically significant favoring water quality. This indicates that suppose all other factors are constant, respondents are more likely to prioritize water quality than water supply or other ES.3.3.2As with the ecosystem service preference analysis, we analyzed respondents' preference towards prioritization of the ecosystem. Similarly, understanding these preferences towards priority ecosystems can narrow the appropriate conservation program recommendation for targeting the preferred ecosystem service. As with the ES ranking analysis, the same methodology was used for the priority ecosystem (Appendix 8), and the result of the rank analysis is shown in The hierarchy showed that the forest ecosystem is the main priority for respondents. The analysis also indicated a very small difference in preferences between rivers/lakes and farm/agricultural land. Despite the tight rank score difference of the next best alternatives, it is still clear that the top priority ecosystem is directly related to the improvement of water-related ecosystem services. This result was consistent with the stakeholders' preference towards the priority ecosystem service discussed in the previous section.The results of the Multi-Logit regression for the priority ecosystem preference is shown in Among all the covariates in the results shown in 3.3.2.1Results presented in 3.3.2.2Since one of the major beneficiaries of ecosystem services is agriculture, we also compared the respondents' prioritization between forest ecosystems and agroecosystems. Agriculture is also the third-ranked priority ecosystem in the earlier rank analysis in Results in In terms of the distance to monitored ecosystems, results in On the other hand, respondents residing in the midland region are 38% more likely to prioritize the forest ecosystem as compared to those who reside in the low country. The midland region showed a low satisfaction rating for water quality relative to the other regions b. TherefLastly, the intercept also shows that holding all other factors as constant, residents are more likely to prioritize forest ecosystems by 58% as compared to the agriculture ecosystem.3.3.2.3In comparison to “other ecosystems,” results in Residents from both midland and the upstate are 51%–56% more likely to choose the forest ecosystem for conservation as compared to residents from the low country. While the midland residents' preference could be attributed to their satisfaction rating to water quality, the upstate's preference could be attributed to the land cover. Since the topography of the upstate is hilly and mountainous, the majority of the land cover classified as forested area are in this region . TherefoOverall, the results reinforce the rank analysis in which the residents of SC prefer to prioritize forest ecosystems for conservation compared to other ecosystems. Essentially, respondents to our survey revere to prioritize the forest ecosystem because they are aware that it is the main source of the primary ecosystem services in SC which directly benefits them.4This study examined the priorities for conservation of ecosystems and ecosystem services for residents in South Carolina, USA. Since residents are typically the final recipients of ecosystem services, identification of priority ecosystem and ecosystem services from the public's outlook could help in the strategic implementation of conservation programs. Nevertheless, to the best of our knowledge, no study has previously investigated SC residents' preference regarding ES.Results showed that SC residents are likely to prioritize water-related ecosystem services, particularly the improvement to water quality regulation, while their least priorities are fishing and hunting. Although South Carolina residents are actively involved in fishing, hunting, and outdoor activities, water quality improvement still poses to be their top priority since water quality improvement also benefits other ecosystem services, including fishing and other outdoor activities. Similarly, this was also reflected in the respondents' mean satisfaction rating through their general impression of the current state of the environment in SC. The satisfaction rating to the water quality and the impression of the overall quality of the environment scored lowest as compared to water supply and air quality. While further investigation is still needed to determine the specific reason why this is the case, the results of this study could be used as a baseline for monitoring these characteristics.Furthermore, these results are consistent with the residents' preference to prioritize water quality regulation as the primary ecosystem service for conservation program targeting. However, their preferences towards water quality improvement do not discredit the importance of maintaining the continuous water supply provision, as was evident in the maximum likelihood analysis. Most covariates did not yield statistically significant evidence when comparing water quality and water supply as the priority ecosystem service. Therefore, as state resource managers continually update their water plans, including monitoring systems for water quality improvement would ensure that they address the challenge of meeting the water demand while also meeting the public's satisfaction standards for water quality.In terms of ecosystem preference, respondents to our survey indicated that the forest ecosystem is the priority ecosystem to be conserved. The reasons for this preference also align with their ecosystem service preference. Respondents are aware of the forest ecosystem's direct linkage to water-related ecosystem services; therefore, they opt to choose to conserve the ecosystem that also enhances their primary priority ecosystem service. Thus, in planning for conservation interventions, prioritizing the conservation programs for the forested land would reap more support and possible participation from the public.The prioritization ranking of SC residents also revealed their preferences towards the primary ecosystem and ecosystem service for conservation. Apart from the satisfaction ratings, socio-economic factors such as age and income also showed statistical evidence that affects the respondents' prioritization. Also, proximities to monitored ecosystems revealed to have a significant contribution in evaluating respondents' preferences. The regression result using the proximity of monitored ecosystems also showed that the quality of these ecosystems affects the residents' perception and prioritization criteria. For instance, residents who live farther from an impaired stream do not see the urgency of an improved water body hence will prioritize other ecosystem services more than water quality regulation.On the other hand, those who live in agriculture-dependent areas and near a good water body will prioritize agriculture ecosystem rather than forest ecosystems because of the availability of water that could be used for irrigation purposes. Furthermore, the geographic region of the respondents showed a statistically significant contribution affecting their preference. Since geographic regions have different landscape characteristics such as topography and land cover, this could also affect the residents' preference for conservation. Therefore, the results showed that perception and impressions of nearby ecosystems and their geographic location affect their preferences and prioritization. This analysis could be important in targeting the stakeholders that could be involved in supporting the conservation programs. For instance, since younger residents and residents with higher income are keen on forest conservation, designing sustainable financing mechanisms or user-fee mechanisms could be tailor-fitted to this group. Knowing the residents' priority ecosystem and ecosystem service for conservation is an essential initial step for conducting a WTP study for conservation planning activities and as an economic basis for developing sustainable financing mechanisms that will support conservation programs.The use of SC residents' perception, including satisfaction rating, to measure the public's general impression towards the environment served as a feedback mechanism. Ensuring that the public's satisfaction standards are met translates into public support, hence could increase the potential funding support for conservation programs. Understanding the results from their perception can draw up insights for crafting strategic implementation of conservation programs and further conservation studies. For instance, in 2013, in addressing the water supply problem, the state tapped the residents' ability to promote the efficient use of water through the “WaterSense” program . The proAs the state water plans are continuously being updated by the state agencies and the South Carolina Water Resources Center (SCWRC) to ensure that there is enough supply of water for everyone, this study showed that there should also be a focus on the water quality regulation and ecosystem conservation, particularly towards the forested land. Picking up from the results of this study, further research endeavors focusing on water-related ecosystem services in SC could provide better assessment and information about their conservation program priorities. Furthermore, knowing the stakeholders' priority ecosystem and ecosystem services can be used for designing specific valuation studies.Since the study was focused on residents as the main stakeholder, further research will be to look at other stakeholders' perspectives such as farmers, landowners, tourists, and businesses, which could provide more insights on the feasibility of implementing conservation programs. Also, since the study was conducted on an online platform, the results of this study are limited to inference regarding only residents with access to the internet. Although most residents across the state have internet access, a substantial number of residents are still without online access. Therefore, it is worth pursuing to examine the preferences of non-internet users on the matter. Moreover, while the scale of the study focuses only on SC residents, it will be interesting for future research to compare the residents' preferences across different states or regions. This comparison could provide a more comprehensive assessment of the decision-making factors that affect an individual's preference to prioritize an ecosystem or ecosystem service for conservation. Finally, in pursuing sustainability as defined in the World Commission on Environment and Development (WCED), future research and ES approaches should include a more diverse notion of social-ecological systems by making it centered towards the stakeholder while not compromising the ecosystem integrity. Therefore, future managers can draw insights from the results of this study to craft strategic implementation of conservation programs by incorporating the residents' preference.J. Carl Ureta: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Michael Vassalos, Marzieh Motallebi: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Robert Baldwin: Conceived and designed the experiments; Contributed reagents, materials, analysis tools or data.Joan Ureta: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.10.13039/100005825National Institute of Food and Agriculture/USDA (Award #: 2018-67020-27854) and the South Carolina 10.13039/100009171Natural Resources Conservation Service (Award #: NR184639XXXXG002).This work was supported by the Data will be made available on request.The authors declare no conflict of interest.No additional information is available for this paper.


In recent years, we have witnessed a growing adoption of serious games in telerehabilitation by taking advantage of advanced multimedia technologies such as motion capture and virtual reality devices. Current serious game solutions for telerehabilitation suffer form lack of personalization and adaptiveness to patients’ needs and performance. This paper introduces “RehaBot”, a framework for adaptive generation of personalized serious games in the context of remote rehabilitation, using 3D motion tracking and virtual reality environments. A personalized and versatile gaming platform with embedded virtual assistants, called “Rehab bots”, is created. Utilizing these rehab bots, all workout session scenes will include a guide with various sets of motions to direct patients towards performing the prescribed exercises correctly. Furthermore, the rehab bots employ a robust technique to adjust the workout difficulty level in real-time to match the patients’ performance. This technique correlates and matches the patterns of the precalculated motions with patients’ motions to produce a highly engaging gamified workout experience. Moreover, multimodal insights are passed to the users pointing out the joints that did not perform as anticipated along with suggestions to improve the current performance. A clinical study was conducted on patients dealing with chronic neck pain to prove the usability and effectiveness of our adjunctive online physiotherapy solution. Ten participants used the serious gaming platform, while four participants performed the traditional procedure with an active program for neck pain relief, for two weeks . Feasibility and user experience measures were collected, and the results of experiments show that patients found our game-based adaptive solution engaging and effective, and most of them could achieve high accuracy in performing the personalized prescribed therapies. As of late, the challenge of designing a highly engaging serious game has been a subject of interest to various fields including training, simulation, healthcare, and education, among others. Specifically, the direction towards creating a home-based individualized training plan for patients diagnosed with mental and/or physical disorders has attracted more interest due to the recent advances of technologies and techniques related to gamified telerehabilitation. Traditionally, physical therapy solutions present several challenges. First, the patient-to-therapist ratio for physical rehabilitation is generally unable to comply with the ideal guidelines for staffing assumptions . This usWe believe there are some unique features that can overcome the mentioned challenges, which are illustrated in Consequently, we introduce the “RehaBot” system to overcome the challenges presented by the traditional therapy solutions, and it differs from existing work in many ways: (i) RehaBot is comprehensive in that it was not designed for rehabilitating specific body parts, but rather it accepts various therapies to cover the whole body; (ii) in addition, RehaBot embeds virtual assistants to direct patients to perform the exercises correctly through 3D illustrations, while in case of failure, it indicates the joints/gestures that need improvement; (iii) furthermore, RehaBot is intelligent in assessing patients’ range of motions of various body parts and then adjust the game’s level of difficulty in real-time tailored to the patients’ abilities, so that they do not lose interest of the game due to unrealistic difficulty levels. A variety of upper limb and lower limb disabilities can be covered in this platform due to the use of motion caption sensors, such as the MS Kinect 2, which covers the body joints starting from head, neck, spine and upper body joints, to knees, foots, and ankles. However, in our evaluation we have focused on upper limb disabilities, including cervical spine diseases, and lower back pain, as they are very common among all ages and the access to patients dealing with such disorders is much easier than other types of disability.This paper extends our previous work introduced in ,5 by demRehaBot embeds real-time pattern and gesture recognition and different adaptation techniques to tailor to patients’ preferences and needs.The game engine incorporates three levels of adaptation: (1) an embedded virtual assistance, a rehab bot, that shows the users how to perform the exercises correctly; (2) a module of intelligent notifications that reads and represents the patient postures/movements in 3D, then if needed, adjusts the game to the appropriate level that ensures a better outcome; and (3) a dynamic correction module that takes into consideration both the game level of difficulty and the virtual assistant readings to produce a tailored set of exercises that are more appropriate to the patients’ abilities; thus, reducing the gaps between the current range of motions patients are able to perform and the ideal suggested ones.Advanced real-time and session-based analytics are also generated to allow for automatic learning of patient behavior over the different sessions.A fully-fledged system was developed with both therapist and patient dashboards. 3D motion capture and VR daemons will be running on patient’s front-end with a possibility to adapt game configuration and controls in a seamless way based on performance.It should be noted that while the system implements its functionality using the MS Kinect 2 and HTC VR headsets, these devices do not constitute an essential factor for the platform and can be replaced by other alternatives  if needed. Indeed, while motion sensors provide data about joint positions and vector orientations, VR devices report the position and orientation blueprints of the headset. Different case studies including upper or lower limb disorders can be considered to evaluate the performance of the proposed system, as our employed motion tracking device  can track 25 joints covering the full body. The presented clinical trials focus on patients with chronic neck pain with the aim of having a homogeneous group of patients. However, this study can be easily covering other common cases in upper limb disorders, such as lower back pain and scoliosis.The remainder of this paper is organized as follows. Recent studies explored the possibility of utilizing patient-centered telerehabilitation as an adjunctive therapy to overcome the challenges in conventional therapy solutions ,7. WithiSeveral challenges need to be addressed while designing serious game solutions in physical rehabilitation, as discussed in . These cThe pre-design phase consists in analyzing the needs, audience, best technologies to be used, goals and outcomes, and assessment tools for measuring the performance.The design phase covers the development of all necessary components in the serious game, while considering factors such as game interactivity, enjoyment, clarity, and user experience. We believe that adaptiveness of the serious game to patients’ needs and performance should also be taken into account at this stage.Finally, the evaluation phase, which consists in measuring the outcomes achievement and the user experience. With respect to physical rehabilitation, outcomes are related to skills acquired and range of motion improvement. Outcomes are considered as achieved when the patient performs the prescribed training correctly and with no side effect on his/her overall medical conditions. Measuring the user experience should also cover the satisfaction level and usability of the proposed serious game.Additional challenges related to the development of evaluation guidelines were also discussed in . The evahttps://www.stereolabs.com/zed/, and Orbbec https://orbbec3d.com. When integrated with algorithms of skeleton pose tracking and point cloud, these sensors can assess various types of physical functions and anatomy [https://www.vicon.com/products/camera-systems. However, unlike Microsoft Kinects [Several devices can be used to capture 3D motions and identify the physiological features from a rehabilitation session, such as Intel Realsense, Microsoft Kinect, StereoLabs Zed  anatomy ,23. Othe Kinects ,25, Vico Kinects . A recen Kinects  demonstr Kinects .‘1cybersickness”, mostly resulting from conflicting signals about the body’s orientation and motion [Virtual reality-based exergames for rehabilitation were proposed in several studies. The aim behind these studies is to identify the advantages and barriers perceived by experts to using an immersive Exergame as an adjunctive treatment to conventional therapy. VR-based serious games are designed using VR headsets, which are head-mounted displays (HMDs) integrating inertial measurement units (IMUs). The two branded VR headsets that are mostly used are Oculus Rift (in its three versions) and HTC Vive, in addition to other low-immersion solutions such as cardboards or Gear VR. The use of 3D motion tracking sensors and virtual reality headsets in rehabilitation has been investigated in ,30,31, ad motion . Checa ad motion  suggest d motion  also sugd motion . Having Games can be adapted in various ways to create a user experience that is both personalized and customized. In order to create an adaptive game, a dynamic process of adjustment of the game components, such as user interfaces and strategies, game mechanics, and difficulty levels, has to be embedded ,34. SomeNonetheless, there is one drawback associated with adopting a fully dynamic gaming environment in that it may not leave enough room for therapists to test their therapeutic plan in a controlled environment aside from other factors. Thus, the semi-dynamic approach enables therapists to adjust non-related game parameters, while leaving the adjustment of the game-related parameters to the system based on the performance of the patients. An example of a game that was designed using a semi-dynamic approach is the game of catching falling fruits , in whicAlthough the presented approaches consider adaptation techniques from different perspectives, none of them focused on automatically generating adaptive game configurations and embedding adaptive virtual characters from therapeutic instructions, which is a major concern in our work. Moreover, full body posture adaptive design and matching, as well as self-adaptivity of our rehab bots based on patient performance, were not previously discussed. In this work, we have also adopted a semi-automatic adaptation strategy whereby an expert can author a set of recorded postures along with a few non-game-related parameters , while the system takes that input and thoroughly generate new game configurations specifically designed to fit that particular patient.www.mirarehab.com/, JRS from Jintronix for Kinect-based rehabilitation www.jintronix.com, Vicon from the Oxford Instruments Group www.vicon.com/, MotionCare360 www.mobihealthnews.com/tag/motioncare360, and Doctor Kinetic doctorkinetic.com, are among remote physiotherapy solutions that employ relatively tangible and intangible sensors for tracking body joints and measuring therapy compliance. However, to the best of our knowledge, none of these systems embed dynamic techniques for designing personalized and self-adaptive serious games, such as the ones proposed in our Rehabot system.A market study demonstrates an increasing interest in remote physiotherapy solutions. A number of companies have implemented and presented various platforms for 3D motion capture with applications in different domains from sports, to entertainment, virtual reality, and physical rehabilitation. The progression of creating adaptive serious games for therapeutic purposes, also called exergames, has been accelerated after the advance monitoring devices surfaced. Exergames allow patients to take part in a customized, enjoyable, and immersive sessions, while conducting clinically validated training exercises. Adaptive exergames assess patients needs and capabilities to automatically generate personalized sets of training exercises. The challenges in developing adaptive serious games include (1) transforming recommended postures by an expert into a series of virtual assistants exhibiting those postures in the right place, time, and number of occurrences within the game environment; (2) implementing an accurate real-time posture mechanism that allows guiding the patient towards performing the right exercises and detecting the imperfect gestures within and over sessions; and (3) redefining the rehab bots’ behavior by adapting the game level of difficulty to suits patients needs and performance.“virtual coaching” into the gaming environment to ensure that patients are performing the exercises correctly. Such an integration is associated with three levels of adaptation techniques as mentioned above and described in Rehabot embeds adaptation techniques at different levels to tailor to the patient’ profile. The automatic adaptation requires integrating 3D motion capture sensors and virtual reality head-mounted displays (HMDs), namely, the Microsoft Kinect 2 and HTC Vive, are used in this platform, thus leveraging real-time monitoring of patients’ gestures and activities. Nonetheless, the tracking system of body postures in Kinect presents noisy data and is sometimes unstable, especially when multiple joints appear in front of each other ,43,44. CIn this phase, the accuracy of the Kinect data has undergone several filtering stages in order to achieve the best performance. It is worth mentioning that the Kinect device might show inconsistent data, especially when two body joints appear on top of each other. In this case, the hidden joints’ positions might be predicted in a wrong way. The filtering process aimed at smoothing these inconsistencies by removing unreliable data streams, or by applying extrapolation between previous positions of the same joint in order to predict the right next position. Different filtering algorithms for improving joint position estimation of Kinect have been proposed, such as, low-pass filtering, double exponential filtering, and Kalman filter ,44. Due This process shows a better stability and reasonable results even in highly dynamic situations with multiple joint interference. Algorithm 1 describes our developed filtering process. The algorithm takes the Kinect frames, joints names and their distances to parents, and joint historical positions and orientations. Typically, we consider the last frames in a given temporal range for historical positions and orientations, so that we can detect the pattern and movement evolution for each joint. This algorithm uses the double exponential filter in its first phase, then performs extra calculations and validation to produce the updated frame as an output in the second phase. The detailed steps are highlighted as follows.t in order to estimate the best value for the joint position at the instant In the first phase, each frame should undergo a double exponential smoothing to ensure the series of data for joint positions exhibit some form of trend without outliers (line 3). This smoothing filter takes the historical datasets for joint positions and orientations up to a certain instant The second phase consists in evaluating the estimated values obtained from the double exponential filtering, by considering two main factors: the distance between each joint and its parent, and the maximum angular velocity for each joint. The set of distances between joints is determined statically, whereas the maximum rotation is calculated for each joint based on its motion nature and the historical records. Lines 5–10 highlight this process for each joint. First, the algorithm determines the joint parent and calculates the distance difference between the two joints in the current frame data; then, it evaluates the mismatch with the static distance between the joint and its parent Algorithm 1: Kinect Data Smoothing Algorithm.The output of this algorithm is the new set of frames with updated predicted series of values. Only values of joint positions and orientations that are not trusted, are marked with a low confidence level, which is taken into account in posture matching as described in To start the exergame, a therapist inputs a set of correct postures for the corresponding patient to practice. The system then identifies a set of joints that are used with each inputted posture and starts monitoring them. In order to identify the joints, the system compares the correct posture with the human body in an idle position, and identified joints are given more weights as opposed to other unidentified body joints. Algorithm 2: Posture Matching AlgorithmA posture matching technique is developed to calculate the closeness indicator with the ideal posture displayed by the virtual character, so that patients’ joints are compared against their ideal counterparts following the specifications prescribed by the therapist. The posture matching technique is described in Algorithm 2. The algorithm takes, as inputs, the values of t represents the appearance timestamp of the virtual bot (Line 2).The matching process launches when the patient reaches the next virtual bot posture range. The matching process calculates all the frames that are recorded within the The system returns all joint positions and their respective directions for each frame in the form of 3d vectors relative to each bone. As demonstrated in The system also computes for each frame the overall matching percentage between all bones from the frame of the patient and their corresponding ones from the registered posture of the virtual assistant. The matching percentage is calculated by dividing the 3d ratio of rotational deviation over the interval of range of motion. For instance, Finally, for each frame, the system calculates the final score and compares it with the score of the correct posture that comes from the virtual assistant (Line 12). Then, the best frame within range is selected and compared to the threshold value  (Line 14). If the score is greater than the threshold value, it is considered a success; otherwise, the system will suggest guiding instructions to help the patients improve their performance.Algorithm 3: Intelligent Alerting Algorithm.In order to provide the user with real-time guidance, Rehabot takes advantage of the closeness indicator computed previously and recommends multimodal instructions towards improving the patient performance while exercising. Computing such alerting instructions is done by analyzing the positions and orientations of the whole body joints. As a result, the system can determine the deviation from the ideal posture at a high level, but also can estimate detailed joint deviations, so that the patient can understand exactly if something goes wrong. Estimating the deviation in direction and 3D angular rotation of each joint with respect to the virtual bot in real-time is challenging. An example instruction that is expected would be “move your left wrist up/right by 45 degrees”. Algorithm 3 highlights the different steps towards generating real-time guidance and feedback on current performance.We evaluate the posture matching between the virtual bot and the patient posture in real-time. If the closeness indicator representing the overall posture deviation is less than a specified threshold , then the system starts a detailed investigation and reports the least performing gestures.For each joint in the detected posture frame, the system determines the deviated angle on the 3D axis between the parent bone of the patient and the corresponding one from the virtual bot, then the 3D angle is adjusted by removing the generated offset from the rotation of the user bone, thus realigning joint directions between the user and the bot, and removing any initial offset of the parent bone (Lines 2–3).Slerp function.Lines 4–6 explain the process for computing the direction of movement vectors for both the patient and the virtual assistant. The system then spherically interpolates the rotation between both vectors, and calculates the straightest and shortest paths between its quaternion end points This quaternion path is usually referred to as the “rectification” vector There, we apply the same technique on up/down directions, so that we can generate relevant instructions in order to improve the patient’s gestures for each exercise (Lines 8–9). Generally, to avoid an overwhelming information load, instructions are focused on the three least performing gestures when evaluating the posture in real-time. Displayed instructions include the joint description, desired direction , and angle in degrees.Different groups of gestures are evaluated separately over several occurrences of gestures of the same type, and recommendations are generated by the system in real-time to improve the patient performance. However, in case the overall performance has been improving after several attempts, the virtual assistant acts as an adaptive character that can adjust itself to match to patient capabilities. This can be done in a reverse way to what has been described in Algorithm 3. Indeed, Rehabot takes advantage of the sequence of 3D angle differences for all body joints This automatic verification of patient performance is performed after a minimum of three attempts for each group of gestures. If the patient is unable to perform as expected after those attempts, the system decides to automatically decrease the difficulty level for the assessed posture or group of gestures to maintain a convenient level of difficulty. On the other hand, and upon the successful completion of a group of gestures, an upgraded level of difficulty is generated by creating a complementary set of gestures until reaching the best performance for a given exercise as prescribed by the therapist. Therefore, virtual bots are adapted by generating updated postures that are slightly superior or inferior with respect to the previous postures based on patient performance. This helps in keeping the gaming environment enjoyable, while pushing the patient towards a better performance.This section demonstrates the system architecture overview and discusses how its two types of users—therapists and patients—can interact with it. The aim of this platform is to produce a serious game for physical rehabilitation that is proactive, adaptive and personalized. As shown in Create and manage exercises through groups of gestures.Select single or multiple joints.Record a set of postures to be performed by the patientSelect categories of groups to be performed for therapy.Select number of reps for each movement in a set or select a recursive time-based set.Assign therapies to existing patients.Monitor session-based patient performance.Suggest updated gestures based on current performance.After the completion of each session, the system generates a report to the therapists which includes the percentages of the success rate, improvement, and fail attempts compared to other previous sessions.In order to access the patient’s dashboard, users have to log in first. The system has a profile for each patient, which saves all the prescriptions and exercises assigned to each individual by the therapist. Upon successful log in, the selected game is generated automatically with all the embedded exercises for that particular patient see . To playReceive notifications of newly assigned therapiesSelect and play several games after logging inChoose whether to use the VR headset Perform required exercises by matching postures from the virtual assistant, and by taking the multimodal instructions into considerationNotify the patient about the current status of successful or failed sets at a specific time or after each sessionDisplay incomplete therapy stepsIt is worth noticing that the VR headset has been made discretionary to permit patients, who might feel discomforted when exposed to a completely virtual environment. A couple of side effects can be observed on a few patients, which are alluded to as “cyber sickness”, which includes dizziness, headache, disorientation, and postural instability affecting the patient’s overall performance . Our sysWhen the game starts, various background processes/daemons start gathering Kinect frames on various body joints, direction data, and VR orientation. Data are continuously stored on the cloud to allow for expert monitoring, as well as real-time and sessions analytics. Different types of data are generated over time: (i) precalculated posture data that is generated based on the therapist inputs, including the defined parameters for each gesture and group of gestures, as well as the recorded postures for each therapeutic exercise, and (ii) the real-time patient data generated from both devices, which are saved with a high-frequency sensor producing up to 30 frames per second, following the specifications of the Kinect 2. Some of these frames can be dropped depending on the level of details required in the analysis.RehaBot embeds a component for online game adaptations, which we call an intelligent game zone. These adaptation techniques are reflected in the algorithms described in Another sub-component of the game zone is the intelligent alerting responsible for generating instructions  for each matching process as illustrated in One extra step beyond the posture matching and intelligent alert is the assessment of the overall exercise and individual gestures during one session, or multiple. The objective of the automatic correction component is to analyze each group of gestures and suggest adaptations to the difficulty level of that group with the aim of reaching the targeted performance. For example, a failure to achieve the target for a given exercise over several attempts will result in gradually decreasing the exercise difficulty, so that the target can be met by the patient in the next occurrences. Once the right posture is successfully recorded over three attempts, the component suggests a new set of exercises within the same group with a higher difficulty level until accomplishing the full set. A successful record on the full set allows for an adapted game strategy and scene with a more advanced level of exercises to be integrated, thus escalating the challenge to be met.Because RehaBot is designed as an adjunctive treatment that helps physiotherapist follow the patient progress in real-time and over sessions, instantaneous and session-based analytics are considered as a core component in the system. A deeper understanding and assessment of a patient’s performance can definitely help in drawing better conclusions and tailoring the game parameters to suit the patient’s behavior over sessions. Among many factors, the system monitors the response time in each exercise, body posture correctness and joints’ range of motion, thus maintaining an appropriate game difficulty level. Moreover, a modified or upgraded set of gestures will be proposed for the next sessions, while keeping a high standard of entertainment and personalization to achieve a rich and effective user experience. The mining techniques are used to search for consistent patterns as well as systematic relationships between the different input variables. For example, by knowing the user profile , and the physical challenges they are facing currently , the therapist can define a few parameters on which our system can be based to enrich the game scenario with personalized configurations and adaptive exercises. The goal of the data mining is data prediction. For instance, we can use the mining techniques to predict improvement on a timeline by following a calculated schedule of exercises over sessions. Other measurements include (i) weekly- and monthly-based performance indicators; (ii) physical effort required and level of pain while performing the game-based exercise over a session, a week, or a month; (iii) effort vs. improvement analysis; (iv) VR vs. non-VR-based assessment; (v) reaction time and time to accomplish a given exercise; and (vi) number of practiced vs. cleared game levels, among others.We have developed an e-health platform that offers several advantages over traditional systems for therapy design and monitoring. Within the platform, two applications have been developed to address the needs of therapists and patients. Both store their data and analysis on the same cloud environment. To use the application at home, patients need a computer linked to the internet, a Kinect device, and, only if desired, a VR headset. The patient logs in, selects the game, and starts playing. The therapist, on the other hand, accesses the authoring interface using a similar equipment to design, record, and assign personalized exercises to each patient. Particularly, the authoring interface enables the therapist to select the joints that need to be focused on using a model of a human body anatomy and then utilizes the Kinect sensor to record the appropriate postures. Both therapists and patients can replay any session in a 3D representation. The patient mainly uses this feature to check the ideal therapy postures recorded by the therapist, while the therapist can view the complete motions performed by the patients during the session. Moreover, both therapists and patients are able to visualize current or previous session’s performance improvement metrics plotted on a live graph.Using the Unity 3D game engine, this adaptive game has been created. Although the algorithm has been developed to record data captured by the Kinect Xbox One and the HTC Vive headset, the game engine can be easily modified to support other 3D capturing devices of motion like the Intell Real Sense devices. The adaptation algorithm is continuously utilizing the JASON formatted precomputed data generated by the virtual assistant as well as the real-time data generated during the therapy session to create the next appropriate set of exercises/gestures based on the performance of the patient. The system gathers two types of real-time data: (1) the game data, and (2) the output of the 25 joints captured in each Kinect frame and the 3D angular rotation recorded by the Kinect devices. Kinect data consist of a sequence of frame IDs associated with time stamps. There are a number of properties for each frame, including the vector direction between any two adjacent joints  diagnosed with non-specific mechanical neck pain were recruited  to evaluate the intrinsic properties of the adaptive gaming platform, while four more patients were recruited to perform the traditional procedure with an active program for neck pain relief. The duration of the training program was two weeks  for all patients. Recruited patients, with an age ranging between 18 and 50 years, were all office workers (with an average of at least six hours sitting in front of a computer screen), as studies have demonstrated that the highest annual prevalence of neck pain is shown among office workers ,50. To dStretching and strengthening exercises include lateral flexion of the upper part of the trapezius, ipsilateral flexion and rotation for the scalene, and flexion for the extensor muscles, holding each movement for 10 s see b. These Flexion: a movement by which the chin attempts to touch the chest. The normal cervical range of motion for flexion is 80 to 90 degrees.Extension: starts with both shoulders relaxed and the head in a neutral position, then by moving the head to look up towards the ceiling (ROM up to 70 degrees).Lateral Flexion: happens when moving your head from one side to another, while trying to bend your neck to bring your ear down towards your shoulder. This movement is equal on both sides. The ROM is between 20 to 45 degrees on both sides.Rotation: starts from the center by looking forward, then rotates to look over you shoulder, then returns back to the center .Exercise sets 1 and 2 were assigned randomly to two groups of patients, and each group was evaluated based on the assigned set and by using the same evaluation criteria. Each exercise was automatically injected and repeated 5 times during a game session. Finally, a neck straightening exercise was performed by returning the head 5 times for 3–5 s. Patients were advised to perform the stretching program 5 times a week, with a single session taking about 8–10 min to perform.The first phase is the experimental evaluation, which consisted in requesting the therapists to assess the effectiveness of the platform and to finalize the different metrics to monitor the patient performance in real-time and on session basis. Following on from the first phase, the platform was updated and made ready for a detailed evaluation of the platform.Q1.How efficient is the platform to perform its intended tasks?Q2.How effective is the platform in fulfilling its intended use?Q3.What is the user perceived value (satisfaction level) about the usability of the platform?The evaluation phase is designed to answer three main questions:Both quantitative and qualitative methods are used to evaluate the effectiveness and usability of our system. In quantitative methods, different metrics were studied and monitored to analyze the quality of improvement of the patient, such as, the visual analogue scale (VAS) before and after exercising, and the range of motion, among others. The aim of these metrics is to assess the performance, reliability, correctness, and effectiveness of the system. Other metrics also include the following.-Movement of the joints: it represents the therapy distribution impact over the body distinct joints. During the therapy session, the positions of each moving joint of the patient’s body are recorded. In fact, the Kinect device generates up to 30 frames per second, each frame contains the joint position and orientation in 3D axes. By taking the sequence of frames into account, one can identify the joint motion, orientation, and speed, thus providing accurate measurements regarding the range of motion and improvement of motor controls.-Incorrect Body Gestures: One example of the incorrect body gestures is moving the forearm to practice a gesture that is supposed to be performed using the wrist. The system records the number of times an incorrect gesture is performed associated with a time stamp.-Reaction Time: it is the time period between the instruction appearance and its implementation.-Range of Motion (ROM): the system records the minimum, maximum, and average ROM of each therapy gesture along with the full session of the therapy. Measuring the ROM is done by recording the minimum and maximum position for a given joint and for each performed gesture. For example, if the monitored exercise is the neck lateral flexion (moving the neck to the right and left), the ROM will be determined from the recorded frames, starting from the initial neck position to the most flexed position as performed by the user. Then, the difference between the two vectors is calculated to determine the maximum ROM.-Speed: It is defined as the velocity of the patient’s moving gestures during the game.-Pattern Matching: it is defined as the accuracy of the matched patterns of each exercise and the full session.-Success Rate: it is defined as ratio of the number of completed therapy sessions over the number of collected objects during the game.-Error Rate: it is defined as the total number of therapy session that are not performed correctly over the number of total completed sessions.-Hits: it is defined as the number of obstruction hits.-Interest: it is defined as the engagement level in the game (how fast the patient gets bored of playing the game).-Other Metrics: We are also calculating various other metrics that are needed by the therapist. These include the angular speed, angular rotation, and acceleration, which help in reporting the muscle power. The Visual Analog Scale (VAS) pain score is also determined before and after each session by filling a standard form by the patient for comparison purposes.A qualitative evaluation has been conducted to measure the system’s usability and users’ satisfaction. Two separate sets of questions were prepared to be used appropriately with the therapists and patients. Each has questions about users’ perceived value of usability, adaptiveness, efficiency, enjoyability, fatigue, efficiency, level of motivation, and mental effort needed to use the system. The next section demonstrates the results of the qualitative questionnaires and feedback of the users.Our objective lies in enabling patients to achieve and maintain the correct cervical alignment and sagittal balance. This requires performing stretching and balance activities for a sufficient period of time, while maintaining an accurate level of difficulty created and carefully adjusted for each exercise.System efficiency is evaluated using several metrics including joint-to-joint vector analysis for a given gesture, posture matching accuracy, gesture duration , exercise completeness, and percentage of hits for attractive and repellent objects . Some of the metrics can be collected and analyzed from raw motion sensor data , while other metrics are developed with samples of patient data over a session or multiple sessions, such as the mean time, duration, and joint range of motion evolution.Moreover, we designed an experiment to measure the accuracy of the posture matching algorithm by comparing its performance in detecting the right posture and detailed user gestures dynamically. For that purpose, we compared the original approach that uses the basic body tracking available in the Kinect 2 without filtering, against the approach that is based on the double-staged filter described in Algorithm 1. On the other hand, we evaluated the average percentage of correct gestures performed by patients over different sessions. For comparison with the traditional procedure, we selected four patients out of ten from those who are testing the gaming platform, and which have the closest conditions to the patients performing the traditional program. Three males and one female were assigned the exercise set number 2 in both groups see b. Both gIn order to measure the usability and adaptiveness of the system, the ten patients who used our platform were also requested to fill out different questionnaires. Three types of self-administered, close-ended questionnaires were designed and distributed to the users. All questionnaires were written in English and it was considered that all users were capable of understanding the language. System Usability Scale Questionnaire (SUS) standard criteria were considered in the first questionnaire, which aims at assessing the learning curve and confortability while using RehaBot. Another specialized questionnaire was designed to measure the effectiveness of the adaptive platform on users’ performance. Results are highlighted in The virtual assistant guides me to do the right posture. Q2. The automatically generated set of exercises exactly reflect my needs. Q3. The system understands and adapts to my capabilities and performance. Q4. The multimodal instructions help me to understand my mistakes. Q5. The instructions provide me with clear suggestions on how to improve my gestures. Q6. The system helps me to be more effective. Q7. I can recover from mistakes quickly and easily. Q8. The adaptive virtual assistant adapts the posture difficulty, and this makes me feel more confident.Finally, All plots demonstrate satisfaction results on a scale from 0 to 5. In general, most metrics show satisfactory results, although the learning curve was mentioned as an important factor that could affect the initial sessions performance, and this can be improved by increasing the number of training sessions for both therapists and patients.Both types of users demonstrated a high level of confidence in utilizing RehaBot and believed it can play a significant role in remote rehabilitative therapies. The motivation and engagement of participants while using our serious games was significant, which allowed for longer periods of practice. As a result, improvements in alignment has been associated with improved outcome of the treated neck muscles. We also took into consideration the recommendations suggested by the therapists and participants with respect to the user interface and the admin dashboard, as well as the “how” alerts and instructions displayed on the patient’s dashboard.Furthermore, when comparing the results of efficiency and correctness between traditional therapy and using our system, both groups showed close performance. However, all patients agreed that performing such exercises at home using our platform would be much more convenient, as it is more engaging and ensures the correctness of performed gestures. Therefore, our findings support the feasibility and impact of using an adjunctive treatment with online adaptive rehabilitation, thus augmenting usual care for persons with chronic neck pain in a clinical context with potential similar role in continuity of other upper limb rehabilitation.This study investigated the feasibility and effectiveness of adaptive serious games developed for body rehabilitation for patients with musculoskeletal disorders. Although the results presented are promising and provide initial evidence on the impact of the approach in online rehabilitation, the experiments were limited to rather a small sample size and only on patients with chronic neck pain disease. Therefore, effectiveness and validity of the approach will have to be further tested on a larger-scale randomized controlled trial, and on other upper limb disorders, as well as for lower limb rehabilitation.We present an adaptive serious game platform for patients with musculoskeletal disorders, referred to as “RehaBot”. This platform allows experts to remotely prescribe personalized therapies by only recording a screenshot of posture required and the exercise duration. RehaBot digitizes those postures and creates virtual characters, called rehab bots, that are injected within the serious game environment. These bots dynamically act as an adaptive coach and adapt themselves gradually according to patient’s performance. RehaBot presents an intelligent alerting and auto-correction system that adapts the level of game difficulty while controlling game configurations and objects in real-time. RehaBot embeds advanced AI and pattern matching techniques to build adaptive and personalized serious games, also referred as exergames. These exergames not only allow for an entertaining experience, but also pave the way for an effective training and rehabilitation. The prototype has been developed, and a clinical study was conducted with a number of patients and under the supervision of three physiotherapy consultants. To the best of our knowledge, RehaBot is the first fully-fledged, low-cost online platform for helping patients with physical disabilities, while naturally embedding fully adaptive assistant within the gaming framework for tracking and guiding patients’ progress over therapy sessions. This platform can also be applied to fitness training domain, where athletes can perform personalized and adaptive training with the help of the embedded virtual coach.


Giant molecular clouds (GMCs) and their stellar offspring are the building blocks of galaxies. The physical characteristics of GMCs and their evolution are tightly connected to galaxy evolution. The macroscopic properties of the interstellar medium propagate into the properties of GMCs condensing out of it, with correlations between e.g. the galactic and GMC scale gas pressures, surface densities and volume densities. That way, the galactic environment sets the initial conditions for star formation within GMCs. After the onset of massive star formation, stellar feedback from e.g. photoionisation, stellar winds, and supernovae eventually contributes to dispersing the parent cloud, depositing energy, momentum and metals into the surrounding medium, thereby changing the properties of galaxies. This cycling of matter between gas and stars, governed by star formation and feedback, is therefore a major driver of galaxy evolution. Much of the recent debate has focused on the durations of the various evolutionary phases that constitute this cycle in galaxies, and what these can teach us about the physical mechanisms driving the cycle. We review results from observational, theoretical, and numerical work to build a dynamical picture of the evolutionary lifecycle of GMC evolution, star formation, and feedback in galaxies. The processes of star formation and feedback happen at cloud scales , and the large-scale accretion flows acting on galactic scales , the subjective classification of clouds required by some methods, and the difficulty of extragalactic observations at sufficient resolution, have hindered progress on this question for a long time.It remains unclear what processes govern cloud formation, evolution and collapse in galaxies, from the diffuse atomic gas, to the dense molecular cores in which stars form. These processes cannot be observed directly as their durations are well in excess of a human lifetime and indirect methods need to be used. Early CO observations of the nearby spiral galaxy M51 revealed the presence of a molecular cloud population between the spiral arms of the galaxy, suggesting that these structures are not transient but rather live for The physical mechanisms driving cloud dispersal are also still debated. Are clouds mainly dispersed by stellar feedback or under the effect of dynamical processes , or are both mechanisms contributing significantly to the dispersal? In the case where stellar feedback plays the major role in destroying the molecular clouds, are early feedback mechanisms (such as photoionisation or stellar winds) rapidly inhibiting star formation or are the clouds only efficiently dispersed after the first SN explosions ? Also in this case, measuring the duration of the successive phases of the cloud lifecycle is a crucial step. Resolving these questions will bring important insight on how the ISM in the neighbourhood of a young stellar region is shaped by stellar feedback, and more generally, how stellar feedback affects galaxy properties.The answers to all of the above questions are critical to determine the resulting SFE of molecular clouds and how this efficiency depends on the environment such as galactic structure and other galaxy properties . The above considerations clearly show that the field is now making steps towards a dynamical (rather than static) view of star formation and feedback in galaxies. To make the link between this small-scale cloud lifecycle and the larger scale galactic environment, the next question will then be to describe the coupling between the cloud-scale processes of star formation and feedback and the galactic baryon cycle in terms of mass flows. Looking forward, we will need to combine the answers to these questions to construct a comprehensive, galaxy-wide description of star formation.These questions have been notoriously difficult to address, especially due to the lack of observational constraints. The latest state-of-the-art facilities, such as the Atacama Large Millimeter/submillimeter Array (ALMA), the NOrthern Extended Millimeter Array (NOEMA), and the MUSE spectrograph on the Very Large Telescope (VLT), are pushing the sensitivity and resolution limits of observations, making it now possible to collect the required data in a statistically representative sample of galaxies, on the cloud scale. These observations are key to better understand the physical processes governing star formation and feedback in galaxies, as a function of the environment.In this review, we aim at describing the evolutionary cycle of molecular clouds. Clouds in the ISM are typically described as discrete entities, with boundaries defined based on the chemical state of the gas (atomic or molecular), density distribution, or gravitational potential (unbound or bound). However, it is not clear that these definitions are physically meaningful, due to the hierarchical structure of the ISM  properties of the molecular cloud population in galaxies in Sect. A promising approach to fully characterise and understand GMC formation is to look at the properties of the GMC population as a function of the environment, both within the same galaxy and across a large variety of galaxies. This approach to studying GMCs has been limited to our own Galaxy until recently, because it requires both high spatial and spectral resolution, high sensitivity, and the capability to cover a significant fraction of the galaxy area. All three requirements are impossible to achieve for external galaxies with single dish telescopes and can only be fulfilled by interferometric observatories. In particular, ALMA has been the step change that has enabled us to detect GMCs in a large set of galactic environments, well beyond the limits of the Local Group and in mass ranges comparable to those observed in our own Galaxy  along the magnetic field lines  events of the growth and destruction of clouds, and describe the long timescale evolution by the continuity equation of the differential number density If the contribution from cloud-cloud collisions is negligible , and especially when comparing observations and simulations, because the measurements can differ significantly due to fundamental differences in the definition  one in which star formation is slow  and efficient (i.e. a large fraction of the GMC is turned into stars), or (2) one in which star formation is rapid  and inefficient (i.e. only a few percent of the GMC is turned into stars). The roles played by stellar feedback and magnetic fields are critical in the distinction between these two models.One hypothesis is that the low SFE and SFR measured in galaxies result from slow star formation, and that the efficient conversion of gas into stars happens on timescales much longer that the free-fall time  diffusion  evolutionary phase  dynamical time implies that GMCs in nearby galaxies on average do not undergo evolutionary cycles without massive star formation. The reason is that the methodology applied to measure the timescales in Fig. ii regions, Kawamura et al.  and galaxy-scale processes . The ongoing growth of the spatial dynamic range spanned by simulations has recently made it possible to follow the galactic processes driving convergence  and dispersal (e.g. shear) down to resolving star formation and feedback in individual GMCs  dynamical time , after which it is truncated by early stellar feedback from massive stars (mostly from photoionisation and stellar winds), resulting in low star formation efficiencies of up to a few percent . With large observational surveys and comprehensive numerical simulations that cover a wide parameter space of galactic environments at high spatial resolution, the community is very close to obtaining a systematic census of how the GMC lifecycle changes with the galactic environment, how it connects inflow and outflow processes in the ISM, and how it feeds the galactic baryon cycle.In this review, we have described the characteristics of the GMC population and of the GMC lifecycle. We have shown that observationally measuring the durations of the successive phases of the evolutionary cycle of molecular clouds and star formation, from cloud assembly to cloud collapse and dispersal allows us to identify the relevant physical mechanisms at play, on the cloud scale in nearby disc galaxies. New theoretical developments combined with recent observations show that molecular clouds can be seen as the building blocks of galaxies. The cycle between molecular clouds and young stellar regions is rapid, driven by dynamics, self-gravity, and early stellar feedback (e.g. photoionisation and stellar winds), which disperses the clouds within a few Myr after the onset of massive star formation. In addition, this cycle is not universal, but the physical mechanisms controlling the different phases of this process likely depend on the environmental conditions. We have shown in particular that cloud lifetime may be set by the galactic dynamical timescale at high kpc-scale gas surface densities, whereas at low kpc-scale gas surface densities, GMCs appear to decouple from the galactic dynamics and their lifetime is regulated by internal dynamical processes.To comprehensively constrain the relative roles of these mechanisms, and determine quantitatively how they depend on galactic structure and properties, future high-resolution, high-sensitivity, multi-wavelength observations across a large range of environments will be necessary, from the most quiescent , to the most star-forming , also probing the particular case of high-redshift galaxies. This is only now becoming possible, thanks to the large recent and future observatories  enabling multi-wavelength surveys of galaxies at the cloud-scale  scale resolutions, even in large cosmological volumes (see e.g. Fig. 1 in Nelson et al. This dynamical vision of star formation and feedback in galaxies can be extended to larger scales. The next challenge is to characterise the physical processes driving the mass flows coupling the small-scale molecular cloud lifecycle to the galactic-scale baryon cycle, as a function of the environment. Eventually, combining all of these different elements will allow us to construct a multi-scale description of star formation across cosmic history.