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2023
Conference article
Open Access
Towards adaptation of humanoid robot behaviour in serious game scenarios using reinforcement learning
Zedda E., Manca M., Paternò F.
Repetitive cognitive training can be seen as tedious by older adults and cause participants to drop out. Humanoid robots can be exploited to reduce boredom and the cognitive burden in playing serious games as part of cognitive training. In this paper, an adaptive technique to select the best actions for a robot is proposed to maintain the attention level of elderly users during a serious game. The goal is to create a strategy to adapt the robot's behaviour to stimulate the user to remain attentive through reinforcement learning. Specifically, a learning algorithm (QL) has been applied to obtain the best adaptation strategy for the selection of the robot's actions. The robot's actions consist of a combination of verbal and nonverbal interaction aspects. We have applied this approach to the behaviour of a Pepper robot for which two possible personalities have been defined. Each personality is exhibited by performing specific actions in the various modalities supported. Simulation results indicate learning convergence and seem promising to validate the effectiveness of the obtained strategy. Preliminary test results with three participants suggest that the adaption in the robot is perceived.Source: ALTRUIST 2022 - 2nd Workshop on sociAL roboTs for peRsonalized, continUous and adaptIve aSsisTance, pp. 93–99, Florence, Italy, 16/12/2022
Zedda E., Manca M., Paternò F.
Repetitive cognitive training can be seen as tedious by older adults and cause participants to drop out. Humanoid robots can be exploited to reduce boredom and the cognitive burden in playing serious games as part of cognitive training. In this paper, an adaptive technique to select the best actions for a robot is proposed to maintain the attention level of elderly users during a serious game. The goal is to create a strategy to adapt the robot's behaviour to stimulate the user to remain attentive through reinforcement learning. Specifically, a learning algorithm (QL) has been applied to obtain the best adaptation strategy for the selection of the robot's actions. The robot's actions consist of a combination of verbal and nonverbal interaction aspects. We have applied this approach to the behaviour of a Pepper robot for which two possible personalities have been defined. Each personality is exhibited by performing specific actions in the various modalities supported. Simulation results indicate learning convergence and seem promising to validate the effectiveness of the obtained strategy. Preliminary test results with three participants suggest that the adaption in the robot is perceived.Source: ALTRUIST 2022 - 2nd Workshop on sociAL roboTs for peRsonalized, continUous and adaptIve aSsisTance, pp. 93–99, Florence, Italy, 16/12/2022
See at:
ceur-ws.org 
| ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
An environment to collect personal memories of older adults and use them to personalise serious games with humanoid robots
Catricalà B., Ledda M., Manca M., Paternò F., Santoro C., Zedda E.
One of the goals of Ambient Assisted Living (AAL) solutions is to be able to stimulate the cognitive resources of older adults. An innovative way to address such stimulation is the use of serious games delivered through humanoid robots, as they can provide an engaging way to perform exercises useful for training human memory, attention, processing, and planning activities. This paper presents an approach to supporting cognitive stimulation based on personal memories. The humanoid robot can exhibit different behaviours using various modalities, and propose the games in a way personalised to specific individuals' requirements, preferences, abilities, and motivations, which can vary among older adults, and even dynamically evolve over time for the same person depending on changing user needs and health conditions. Using personal memories associated with facts and events that occurred in older adults life in the serious games can increase their engagement, and thus potentially reduce the cognitive training drop-out.Source: ALTRUIST 2022 - 2nd Workshop on sociAL roboTs for peRsonalized, continUous and adaptIve aSsisTance, pp. 44–54, Florence, Italy, 16/12/2022
Catricalà B., Ledda M., Manca M., Paternò F., Santoro C., Zedda E.
One of the goals of Ambient Assisted Living (AAL) solutions is to be able to stimulate the cognitive resources of older adults. An innovative way to address such stimulation is the use of serious games delivered through humanoid robots, as they can provide an engaging way to perform exercises useful for training human memory, attention, processing, and planning activities. This paper presents an approach to supporting cognitive stimulation based on personal memories. The humanoid robot can exhibit different behaviours using various modalities, and propose the games in a way personalised to specific individuals' requirements, preferences, abilities, and motivations, which can vary among older adults, and even dynamically evolve over time for the same person depending on changing user needs and health conditions. Using personal memories associated with facts and events that occurred in older adults life in the serious games can increase their engagement, and thus potentially reduce the cognitive training drop-out.Source: ALTRUIST 2022 - 2nd Workshop on sociAL roboTs for peRsonalized, continUous and adaptIve aSsisTance, pp. 44–54, Florence, Italy, 16/12/2022
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
An adaptive behaviour-based strategy for SARs interacting with older adults with MCI during a serious game scenario
Zedda E., Manca M., Paternò F., Santoro C.
The monotonous nature of repetitive cognitive training may cause losing interest in it and dropping out by older adults. This study introduces an adaptive technique that enables a Socially Assistive Robot (SAR) to select the most appropriate actions to maintain the engagement level of older adults while they play the serious game in cognitive training. The goal is to develop an adaptation strategy for changing the robot's behaviour that uses reinforcement learning to encourage the user to remain engaged. A reinforcement learning algorithm was implemented to determine the most effective adaptation strategy for the robot's actions, encompassing verbal and nonverbal interactions. The simulation results demonstrate that the learning algorithm achieved convergence and offers promising evidence to validate the strategy's effectiveness.Source: CHI2023 SARs: TMI - 2023: Socially Assistive Robots as Decision Makers: Transparency, Motivations, and Intentions, Hamburg, Germany, 28/04/2023
DOI: 10.48550/arxiv.2305.01492
Metrics:
Zedda E., Manca M., Paternò F., Santoro C.
The monotonous nature of repetitive cognitive training may cause losing interest in it and dropping out by older adults. This study introduces an adaptive technique that enables a Socially Assistive Robot (SAR) to select the most appropriate actions to maintain the engagement level of older adults while they play the serious game in cognitive training. The goal is to develop an adaptation strategy for changing the robot's behaviour that uses reinforcement learning to encourage the user to remain engaged. A reinforcement learning algorithm was implemented to determine the most effective adaptation strategy for the robot's actions, encompassing verbal and nonverbal interactions. The simulation results demonstrate that the learning algorithm achieved convergence and offers promising evidence to validate the strategy's effectiveness.Source: CHI2023 SARs: TMI - 2023: Socially Assistive Robots as Decision Makers: Transparency, Motivations, and Intentions, Hamburg, Germany, 28/04/2023
DOI: 10.48550/arxiv.2305.01492
Metrics:
See at:
arxiv.org | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
Biography-based robot games for older adults
Catricalà B., Ledda M., Manca M., Paternò F., Santoro C., Zedda E.
One issue in aging is how to stimulate the cognitive skills of older adults. One way to address it is the use of serious games delivered through humanoid robots, to provide engaging ways to perform exercises to train memory, attention, processing, and planning activities. We present an approach in which a humanoid robot, by using various modalities, propose the games in a way personalised to specific individuals' experiences using their personal memories associated with facts and events that occurred in older adults' life. This personalization can increase their interest and engagement, and thus potentially reduce the cognitive training drop-out.Source: CHI2023 SARs: TMI - 2023: Socially Assistive Robots as Decision Makers: Transparency, Motivations, and Intentions, Hamburg, Germany, 28/04/2023
DOI: 10.48550/arxiv.2305.19849
Metrics:
Catricalà B., Ledda M., Manca M., Paternò F., Santoro C., Zedda E.
One issue in aging is how to stimulate the cognitive skills of older adults. One way to address it is the use of serious games delivered through humanoid robots, to provide engaging ways to perform exercises to train memory, attention, processing, and planning activities. We present an approach in which a humanoid robot, by using various modalities, propose the games in a way personalised to specific individuals' experiences using their personal memories associated with facts and events that occurred in older adults' life. This personalization can increase their interest and engagement, and thus potentially reduce the cognitive training drop-out.Source: CHI2023 SARs: TMI - 2023: Socially Assistive Robots as Decision Makers: Transparency, Motivations, and Intentions, Hamburg, Germany, 28/04/2023
DOI: 10.48550/arxiv.2305.19849
Metrics:
See at:
ISTI Repository | CNR ExploRA
2023
Journal article
Open Access
Personalisation of humanoid robots: serious games for older adults based on biographical memories
Catricalà B., Manca M., Paterno F., Santoro C., Zedda E.
We present an approach to novel digital cognitive training through serious games able to adapt to personally relevant material from the older adult's life. The games are based on memories associated with the older adult's biography, thus making interactions personalised, relevant, and more engaging. The serious games are accessed through humanoid robots, which can make the training exercise more engaging because of their human-like behaviour.Source: ERCIM news 132 (2023): 28–29.
Catricalà B., Manca M., Paterno F., Santoro C., Zedda E.
We present an approach to novel digital cognitive training through serious games able to adapt to personally relevant material from the older adult's life. The games are based on memories associated with the older adult's biography, thus making interactions personalised, relevant, and more engaging. The serious games are accessed through humanoid robots, which can make the training exercise more engaging because of their human-like behaviour.Source: ERCIM news 132 (2023): 28–29.
See at:
ercim-news.ercim.eu | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
MCI older adults' user experience with introverted and extraverted humanoid robot personalities
Zedda E., Manca M., Paternò F., Santoro C.
This study aimed to investigate the impact of different personalities in humanoid robots for cognitive training scenarios with older adults with mild cognitive impairment (MCI). In particular, we have designed an application with two opposite personalities based on the Extraversion dimension of the Big Five Factors model. A user test with 16 Italian-speaking participants diagnosed with MCI aged 68+ was performed. The analysis of the data collected suggests that the robot's personality can have an effect on the engagement of such users and also found that participants can discriminate between the two personalities. Overall, the study highlights the importance of designing human-robot interactions considering personality-related aspects when considering MCI older adults.Source: CHITALY 2023 - 15th Biannual Conference of the Italian SIGCHI Chapter: Crossing HCI and AI, Turin, Italy, 20-22/09/2023
DOI: 10.1145/3605390.3605405
Metrics:
Zedda E., Manca M., Paternò F., Santoro C.
This study aimed to investigate the impact of different personalities in humanoid robots for cognitive training scenarios with older adults with mild cognitive impairment (MCI). In particular, we have designed an application with two opposite personalities based on the Extraversion dimension of the Big Five Factors model. A user test with 16 Italian-speaking participants diagnosed with MCI aged 68+ was performed. The analysis of the data collected suggests that the robot's personality can have an effect on the engagement of such users and also found that participants can discriminate between the two personalities. Overall, the study highlights the importance of designing human-robot interactions considering personality-related aspects when considering MCI older adults.Source: CHITALY 2023 - 15th Biannual Conference of the Italian SIGCHI Chapter: Crossing HCI and AI, Turin, Italy, 20-22/09/2023
DOI: 10.1145/3605390.3605405
Metrics:
See at:
ISTI Repository | dl.acm.org 
| CNR ExploRA
2023
Journal article
Open Access
Older adults' user experience with introvert and extravert humanoid robot personalities
Zedda E., Manca M., Paternò F., Santoro C.
Humanoid robots can be an effective tool for the cognitive training of older adults. For this purpose, it is important that their interaction be engaging. In this study, we investigate whether proposing robots exhibiting extraverted or introverted personalities can improve user experience. In particular, we have designed and implemented a set of multi-modal cues for such personality traits, which have been exploited in an application proposing typical exercises for cognitive training through a Pepper robot. We report on a user test with 24 older adults (65 +), which provided interesting and positive feedback regarding how the robot personalities have been exhibited and their impact on the experience of such users.Source: Universal access in the information society (Internet) (2023). doi:10.1007/s10209-023-01054-2
DOI: 10.1007/s10209-023-01054-2
Metrics:
Zedda E., Manca M., Paternò F., Santoro C.
Humanoid robots can be an effective tool for the cognitive training of older adults. For this purpose, it is important that their interaction be engaging. In this study, we investigate whether proposing robots exhibiting extraverted or introverted personalities can improve user experience. In particular, we have designed and implemented a set of multi-modal cues for such personality traits, which have been exploited in an application proposing typical exercises for cognitive training through a Pepper robot. We report on a user test with 24 older adults (65 +), which provided interesting and positive feedback regarding how the robot personalities have been exhibited and their impact on the experience of such users.Source: Universal access in the information society (Internet) (2023). doi:10.1007/s10209-023-01054-2
DOI: 10.1007/s10209-023-01054-2
Metrics:
See at:
link.springer.com | ISTI Repository | CNR ExploRA
2023
Software
Unknown
Robot personalities module
Zedda E.
Description: An application designed to enhance cognitive stimulation among older adults by manipulating robot behaviours into a cooking game. The application incorporates a personality module that generates two robot personalities (extravert and introvert) designed and implemented to improve the Human-Robot Interaction (HRI) of older adults with cognitive impairments. Technologies used: Java, QiSDK, SQL Version: 5.0 CNR Link: https://giteas2i2s.isti.cnr.it/Human_in_Information_Systems_HIIS/Personality
Zedda E.
Description: An application designed to enhance cognitive stimulation among older adults by manipulating robot behaviours into a cooking game. The application incorporates a personality module that generates two robot personalities (extravert and introvert) designed and implemented to improve the Human-Robot Interaction (HRI) of older adults with cognitive impairments. Technologies used: Java, QiSDK, SQL Version: 5.0 CNR Link: https://giteas2i2s.isti.cnr.it/Human_in_Information_Systems_HIIS/Personality
See at: CNR ExploRA
2023
Doctoral thesis
Unknown
Personalities in humanoid robots for cognitive training of older adults
Zedda E.
In the last decade, the ageing of the population is occurring worldwide, and ageing increases the degeneration in the cognitive and physical domains of older adults. For this reason, technologies to support older adults in trying to slow down the progression of cognitive impairment are becoming more and more important. In particular, humanoid robots with social skills are increasingly common in the real world. Although life expectancy is increasing, the quality of life is not necessarily doing so. Thus, we may find ourselves and our loved ones dependent and in need of another person to perform the most basic tasks, which has a strong negative psychological impact. As a result, social robots may be the definitive tool to improve the quality of life by empowering people dependent on others and extending their independent living. In this context, humanoid robots can be an effective tool for the cognitive training of older adults, and to achieve this, their interaction with humans must be engaging. In this Thesis, we seek to understand if proposing robots with extraverted or introverted personalities could improve the user experience during a serious game scenario. Specifically, we design, implement, refine, and test a set of verbal and nonverbal parameters for such personality traits, which are general and potentially have different fields of application. The two personalities are implemented in an application that proposes typical cognitive training exercises using a Pepper robot. Additionally, we identify the requirements for designing and implementing a serious game to be a useful tool to be included in cognitive training. After evaluating the robot personalities with different tests and interviews with 52 users, including experts, healthy older adults and users with mild cognitive impairment, we address the problem of how to improve engagement and adaptation for older adults during repetitive cognitive training. The monotonous nature of repetitive cognitive training may cause older adults to lose interest and drop out. Social robots are used to reduce boredom and cognitive load when playing serious games as part of cognitive training, indeed. In this Thesis, a behaviour-adaptation technique is proposed to select the best actions, which consist of a combination of verbal and non-verbal interaction aspects, for the robot to maintain the attention level of older adult users during a serious game. The behaviour-adaptation technique proposed allows the robot to autonomously select the most appropriate actions to maintain the level of engagement of older adults during the full interaction session. After a session with 28 users, where both the adaptive and non-adaptive robot is used, a test to evaluate the adaptive behaviour of the robot is performed. The findings demonstrate users' ability to differentiate between the behaviours exhibited by the adaptive and non-adaptive robot. The users perceived the adaptive robot as displaying greater adaptability and engagement than the non-adaptive robot. This adaptability contributed to a more engaging and motivating user interaction with the robot. In summary, we provide a system and the guidelines to design a robotic behaviour of the future. The robot is able to autonomously adapt its personality to increase user engagement and experience, stimulating the users to continue the cognitive training.
Zedda E.
In the last decade, the ageing of the population is occurring worldwide, and ageing increases the degeneration in the cognitive and physical domains of older adults. For this reason, technologies to support older adults in trying to slow down the progression of cognitive impairment are becoming more and more important. In particular, humanoid robots with social skills are increasingly common in the real world. Although life expectancy is increasing, the quality of life is not necessarily doing so. Thus, we may find ourselves and our loved ones dependent and in need of another person to perform the most basic tasks, which has a strong negative psychological impact. As a result, social robots may be the definitive tool to improve the quality of life by empowering people dependent on others and extending their independent living. In this context, humanoid robots can be an effective tool for the cognitive training of older adults, and to achieve this, their interaction with humans must be engaging. In this Thesis, we seek to understand if proposing robots with extraverted or introverted personalities could improve the user experience during a serious game scenario. Specifically, we design, implement, refine, and test a set of verbal and nonverbal parameters for such personality traits, which are general and potentially have different fields of application. The two personalities are implemented in an application that proposes typical cognitive training exercises using a Pepper robot. Additionally, we identify the requirements for designing and implementing a serious game to be a useful tool to be included in cognitive training. After evaluating the robot personalities with different tests and interviews with 52 users, including experts, healthy older adults and users with mild cognitive impairment, we address the problem of how to improve engagement and adaptation for older adults during repetitive cognitive training. The monotonous nature of repetitive cognitive training may cause older adults to lose interest and drop out. Social robots are used to reduce boredom and cognitive load when playing serious games as part of cognitive training, indeed. In this Thesis, a behaviour-adaptation technique is proposed to select the best actions, which consist of a combination of verbal and non-verbal interaction aspects, for the robot to maintain the attention level of older adult users during a serious game. The behaviour-adaptation technique proposed allows the robot to autonomously select the most appropriate actions to maintain the level of engagement of older adults during the full interaction session. After a session with 28 users, where both the adaptive and non-adaptive robot is used, a test to evaluate the adaptive behaviour of the robot is performed. The findings demonstrate users' ability to differentiate between the behaviours exhibited by the adaptive and non-adaptive robot. The users perceived the adaptive robot as displaying greater adaptability and engagement than the non-adaptive robot. This adaptability contributed to a more engaging and motivating user interaction with the robot. In summary, we provide a system and the guidelines to design a robotic behaviour of the future. The robot is able to autonomously adapt its personality to increase user engagement and experience, stimulating the users to continue the cognitive training.
See at: etd.adm.unipi.it | CNR ExploRA
2023
Software
Unknown
Robot personalities adaptation system
Zedda E.
Description: An application with an adaptation technique implemented on a humanoid robot to make it autonomously adapt its behaviour during user interaction. The application read the user's state (smile value and gaze direction), classified the engagement level following a defined classification and adapted its behaviours using a Q-learning algorithm (Reinforcement Learning algorithm) performing different robot movements and dialogues according to the user state detected and robot personality chosen. The adaption system is applied at a difficult level of a cooking serious game. Technologies used: Java, QiSDK, SQL, Python CNR Link: https://gitea-s2i2s.isti.cnr.it/Human_in_Information_Systems_HIIS/MediumAdaptation
Zedda E.
Description: An application with an adaptation technique implemented on a humanoid robot to make it autonomously adapt its behaviour during user interaction. The application read the user's state (smile value and gaze direction), classified the engagement level following a defined classification and adapted its behaviours using a Q-learning algorithm (Reinforcement Learning algorithm) performing different robot movements and dialogues according to the user state detected and robot personality chosen. The adaption system is applied at a difficult level of a cooking serious game. Technologies used: Java, QiSDK, SQL, Python CNR Link: https://gitea-s2i2s.isti.cnr.it/Human_in_Information_Systems_HIIS/MediumAdaptation
See at: CNR ExploRA
2023
Software
Unknown
Match-mismatch user/robot personality
Zedda E.
An application that autonomously adapts the robot's personality based on the user personality test results, displaying a robot personality that aligns better with the user's traits in a cooking serious game for older adults with cognitive impairments. Technologies used: Java, QiSDK, SQL
Zedda E.
An application that autonomously adapts the robot's personality based on the user personality test results, displaying a robot personality that aligns better with the user's traits in a cooking serious game for older adults with cognitive impairments. Technologies used: Java, QiSDK, SQL
See at: CNR ExploRA
2023
Software
Unknown
DialogFlow, Google speech API and ChatGPT4 on Pepper
Zedda E., Manca M.
Integration of DialogFlow and Google Speech API on a HRI application for Pepper robot. Technologies used: Java, QiSDK, SQL, Google Speech API, DialogFlow API, Azure and OpenAI API CNR Link: https://giteas2i2s.isti.cnr.it/Human_in_Information_Systems_HIIS/DialogFlow_Pepper https://giteas2i2s.isti.cnr.it/Human_in_Information_Systems_HIIS/GoogleSpeechAPI https://gitea-s2i2s.isti.cnr.it/Human_in_Information_Systems_HIIS/ChatGPTAzure
Zedda E., Manca M.
Integration of DialogFlow and Google Speech API on a HRI application for Pepper robot. Technologies used: Java, QiSDK, SQL, Google Speech API, DialogFlow API, Azure and OpenAI API CNR Link: https://giteas2i2s.isti.cnr.it/Human_in_Information_Systems_HIIS/DialogFlow_Pepper https://giteas2i2s.isti.cnr.it/Human_in_Information_Systems_HIIS/GoogleSpeechAPI https://gitea-s2i2s.isti.cnr.it/Human_in_Information_Systems_HIIS/ChatGPTAzure
See at: CNR ExploRA
2023
Software
Unknown
Q-Learning algorithm for robot behaviour adaptation
Zedda E.
An application that applies the Q-learning algorithm to support an adaptation strategy for the robot in the context of an application for cognitive training. The goal is to help the user maintain a high-engaged level and stimulate in case the user is at a low-engaged level. In the project, the robot agent learns its policy by leveraging the simulator by interacting with the simulated users, updating its knowledge using the Bellman Equation. The algorithm returns a trained Q matrix(s, a). Programming Language: Python
Zedda E.
An application that applies the Q-learning algorithm to support an adaptation strategy for the robot in the context of an application for cognitive training. The goal is to help the user maintain a high-engaged level and stimulate in case the user is at a low-engaged level. In the project, the robot agent learns its policy by leveraging the simulator by interacting with the simulated users, updating its knowledge using the Bellman Equation. The algorithm returns a trained Q matrix(s, a). Programming Language: Python
See at: CNR ExploRA
2022
Journal article
Open Access
Play with me! A serious game for cognitive stimulation of older adults with a humanoid robot
Zedda E., Manca M., Paternò F.
Serious games in humanoid robots have an interesting potential to help older adults with cognitive stimulation in non-pharmacological treatments. Additionally, exploiting the multimodal possibilities of the humanoid robots in such a way to provide them with a personality can be suitable to improve users' engagement, and thus their potential participation in cognitive training programmes.Source: ERCIM news 130 (2022): 24–25.
Zedda E., Manca M., Paternò F.
Serious games in humanoid robots have an interesting potential to help older adults with cognitive stimulation in non-pharmacological treatments. Additionally, exploiting the multimodal possibilities of the humanoid robots in such a way to provide them with a personality can be suitable to improve users' engagement, and thus their potential participation in cognitive training programmes.Source: ERCIM news 130 (2022): 24–25.
See at:
ercim-news.ercim.eu | ISTI Repository | CNR ExploRA
2022
Software
Unknown
Cooking serious game on Pepper robot
Zedda E., Manca M.
An application implemented a serious coking game with 3 different levels to support cognitive training for older adults with cognitive impairment. Application integrated into Pepper robot to improve the HRI. Technologies used: Java, QiSDK
Zedda E., Manca M.
An application implemented a serious coking game with 3 different levels to support cognitive training for older adults with cognitive impairment. Application integrated into Pepper robot to improve the HRI. Technologies used: Java, QiSDK
See at: CNR ExploRA
2022
Software
Unknown
Pepper biographycal serious games (SERENI Project)
Zedda E., Manca M.
SERENI application delivers serious biographical-based serious games using personal information from older adults' lives through a humanoid robot. It aims to stimulate cognitive functions through play sessions, which should last 15-20 minutes. The biographical app, provides relevant biographical data that are mainly used to customise the games, which thereby will be highly personalised for the older adults. Technologies used: Java, QiSDK, SQL
Zedda E., Manca M.
SERENI application delivers serious biographical-based serious games using personal information from older adults' lives through a humanoid robot. It aims to stimulate cognitive functions through play sessions, which should last 15-20 minutes. The biographical app, provides relevant biographical data that are mainly used to customise the games, which thereby will be highly personalised for the older adults. Technologies used: Java, QiSDK, SQL
See at: CNR ExploRA
2022
Software
Unknown
Transfer learning project
Zedda E.
The application uses machine learning to classify satellites based on their IQ samples. It begins by preprocessing the IQ samples and splitting them into training (80%) and testing (20%) subsets using stratified sampling. Transfer learning is applied using various pre-trained CNN models from the ImageNet dataset. The CNN models are modified with a dense classifier for ten classes and a softmax layer for satellite classification. The best performance is achieved with MobileNetV2, trained for 26 epochs using the RMSprop optimiser, CategoricalCrossentropy as the loss function, and CategoricalCrossentropy as the metric. Python, Keras
Zedda E.
The application uses machine learning to classify satellites based on their IQ samples. It begins by preprocessing the IQ samples and splitting them into training (80%) and testing (20%) subsets using stratified sampling. Transfer learning is applied using various pre-trained CNN models from the ImageNet dataset. The CNN models are modified with a dense classifier for ten classes and a softmax layer for satellite classification. The best performance is achieved with MobileNetV2, trained for 26 epochs using the RMSprop optimiser, CategoricalCrossentropy as the loss function, and CategoricalCrossentropy as the metric. Python, Keras
See at: CNR ExploRA
2022
Software
Unknown
Topic modelling
Zedda E.
An application that uses the Latent Dirichlet Allocation algorithms Algorithm to identify and extract topics from a text corpus composed of different scientific papers related to the domain of Social Robotics for assisting older adults with cognitive impairments. The approach uses Natural Language Process (NPL) techniques to evaluate how this domain has evolved and if it is possible to identify some subtopics by analysing academic publications. Language: R
Zedda E.
An application that uses the Latent Dirichlet Allocation algorithms Algorithm to identify and extract topics from a text corpus composed of different scientific papers related to the domain of Social Robotics for assisting older adults with cognitive impairments. The approach uses Natural Language Process (NPL) techniques to evaluate how this domain has evolved and if it is possible to identify some subtopics by analysing academic publications. Language: R
See at: CNR ExploRA
2021
Journal article
Open Access
The impact of serious games with humanoid robots on mild cognitive impairment older adults
Manca M., Paterno F., Santoro C., Zedda E., Braschi C., Franco R., Sale A.
The number of Mild Cognitive Impairment (MCI) older adults is increasing; thus, it becomes more and more important to provide them with support to avoid, or at least slow down, their cognitive decline. To this end, interactive serious games can play an important role. So far, most of them have been deployed through tablets, which represent a cost-effective solution, yet offering only limited possibilities for truly engaging such users in a multimodal manner. However, emerging humanoid robots, through their physical embodiment and human-like attributes, including facial expressions and body language, may open up new possibilities in more effectively engaging MCI older adults during repetitive cognitive training. We present a study aiming to better understand the impact of humanoid robots in supporting serious games for such users. In particular, we investigate how seniors with Mild Cognitive Impairments relate to and perceive serious games accessed through humanoid robots, as part of a training programme aimed to improve their cognitive status. For this purpose, two versions of a music-based memory game have been designed by a multi-disciplinary team, one for humanoid robots and one for tablets. We report on its use during a between-subject study that involved MCI seniors, and discuss their experience. The results show that the robot was received with more enthusiasm by the older adults, thus improving their level of engagement.Source: International journal of human-computer studies (Print) 145 (2021). doi:10.1016/j.ijhcs.2020.102509
DOI: 10.1016/j.ijhcs.2020.102509
Metrics:
Manca M., Paterno F., Santoro C., Zedda E., Braschi C., Franco R., Sale A.
The number of Mild Cognitive Impairment (MCI) older adults is increasing; thus, it becomes more and more important to provide them with support to avoid, or at least slow down, their cognitive decline. To this end, interactive serious games can play an important role. So far, most of them have been deployed through tablets, which represent a cost-effective solution, yet offering only limited possibilities for truly engaging such users in a multimodal manner. However, emerging humanoid robots, through their physical embodiment and human-like attributes, including facial expressions and body language, may open up new possibilities in more effectively engaging MCI older adults during repetitive cognitive training. We present a study aiming to better understand the impact of humanoid robots in supporting serious games for such users. In particular, we investigate how seniors with Mild Cognitive Impairments relate to and perceive serious games accessed through humanoid robots, as part of a training programme aimed to improve their cognitive status. For this purpose, two versions of a music-based memory game have been designed by a multi-disciplinary team, one for humanoid robots and one for tablets. We report on its use during a between-subject study that involved MCI seniors, and discuss their experience. The results show that the robot was received with more enthusiasm by the older adults, thus improving their level of engagement.Source: International journal of human-computer studies (Print) 145 (2021). doi:10.1016/j.ijhcs.2020.102509
DOI: 10.1016/j.ijhcs.2020.102509
Metrics:
See at:
ISTI Repository | ISTI Repository | International Journal of Human-Computer Studies | www.sciencedirect.com | CNR ExploRA
2021
Conference article
Open Access
A cooking game for cognitive training of older adults interacting with a humanoid robot
Zedda E., Manca M., Paternò F.
In this paper, we present the design and the implementation of a cooking game for older adults interacting through a humanoid robot. We discuss the motivations and the requirements that have driven such design and indicate how it has been implemented. The main goal is to stimulate the cognitive resources of older adults in order to limit their decline. For this purpose, we have exploited the multimodal possibilities of the humanoid robot and have identified two robot personalities, which are suitable to improve users' engagement, and thus their potential participation in cognitive training programmes.Source: CHIRA 2021 - 5th International Conference on Computer-Human Interaction Research and Applications, pp. 271–282, La Valletta, Malta, 28-29/10/2021
DOI: 10.5220/0010721500003060
Metrics:
Zedda E., Manca M., Paternò F.
In this paper, we present the design and the implementation of a cooking game for older adults interacting through a humanoid robot. We discuss the motivations and the requirements that have driven such design and indicate how it has been implemented. The main goal is to stimulate the cognitive resources of older adults in order to limit their decline. For this purpose, we have exploited the multimodal possibilities of the humanoid robot and have identified two robot personalities, which are suitable to improve users' engagement, and thus their potential participation in cognitive training programmes.Source: CHIRA 2021 - 5th International Conference on Computer-Human Interaction Research and Applications, pp. 271–282, La Valletta, Malta, 28-29/10/2021
DOI: 10.5220/0010721500003060
Metrics:
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