Social Assistive Robots: Assessing the Impact of a Training Assistant Robot in Cardiac Rehabilitation

Casas, Jonathan; Senft, Emmanuel; Gutiérrez, Luisa F.; Rincón-Rocancio, Mónica; Múnera, Marcela; Belpaeme, Tony; Cifuentes, Carlos A.

doi:10.1007/s12369-020-00708-y

Cited by 24 publications

(14 citation statements)

References 45 publications

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“…Table 1 shows the demographic data of these patients. The first four sessions of 4 patients (2 in Control, 2 in Robot) and the complete CR programme of 6 patients (3 in Control, 3 in Robot) were presented in prior work as case studies (Casas et al, 2018a(Casas et al, , 2020. The data for these patients were included in the analysis for this work.…”

Section: Participants and Demographic Datamentioning

confidence: 99%

“…We aimed to investigate the effects of using a socially assistive robot that uses these sensory values to give verbal feedback to the patient, in comparison to a Control condition, where the sensory values are only displayed on a tablet-based Graphical User Interface (GUI), i.e., no verbal feedback is given to the patient. Initial user studies (Lara et al, 2017 ; Casas et al, 2018a , b ; Casas et al, 2018c ; Casas et al, 2020 ; Aguirre et al, 2020 ; Irfan et al, 2020 ) validated the sensor interface and the robot within the laboratory and clinical settings based on case studies. In Casas et al ( 2019 ), we compared the perceptions of the patients that have conducted the cardiac rehabilitation programme with the robot to the expectations of the patients that have not interacted with the robot through a control group.…”

Section: Related Workmentioning

confidence: 99%

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A Socially Assistive Robot for Long-Term Cardiac Rehabilitation in the Real World

et al. 2021

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What are the benefits of using a socially assistive robot for long-term cardiac rehabilitation? To answer this question we designed and conducted a real-world long-term study, in collaboration with medical specialists, at the Fundación Cardioinfantil-Instituto de Cardiología clinic (Bogotá, Colombia) lasting 2.5 years. The study took place within the context of the outpatient phase of patients' cardiac rehabilitation programme and aimed to compare the patients' progress and adherence in the conventional cardiac rehabilitation programme (control condition) against rehabilitation supported by a fully autonomous socially assistive robot which continuously monitored the patients during exercise to provide immediate feedback and motivation based on sensory measures (robot condition). The explicit aim of the social robot is to improve patient motivation and increase adherence to the programme to ensure a complete recovery. We recruited 15 patients per condition. The cardiac rehabilitation programme was designed to last 36 sessions (18 weeks) per patient. The findings suggest that robot increases adherence (by 13.3%) and leads to faster completion of the programme. In addition, the patients assisted by the robot had more rapid improvement in their recovery heart rate, better physical activity performance and a higher improvement in cardiovascular functioning, which indicate a successful cardiac rehabilitation programme performance. Moreover, the medical staff and the patients acknowledged that the robot improved the patient motivation and adherence to the programme, supporting its potential in addressing the major challenges in rehabilitation programmes.

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Section: Participants and Demographic Datamentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Socially Assistive Robot for Long-Term Cardiac Rehabilitation in the Real World

et al. 2021

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“…Finally, it is significant to emphasize that social interaction has been shown to have a positive impact on general mental and physical well-being in physical exercise therapy [ 79 , 80 ]. Therefore, the use of these artificial intelligence systems has a great potential in physical rehabilitation scenarios application through the use of socially assistive robots (SAR) [ 81 , 82 , 83 ]. SAR is a system that employs interaction strategies, including the use of speech, facial expressions, and communicative gestures, to assist according to a specific healthcare environment [ 80 ].…”

Section: Related Workmentioning

confidence: 99%

“…SAR is a system that employs interaction strategies, including the use of speech, facial expressions, and communicative gestures, to assist according to a specific healthcare environment [ 80 ]. Several studies have shown that SAR systems improve (i) the ability to influence the patient’s intrinsic motivation to perform the task and (ii) the ability to personalize the social interaction to maintain user engagement in the task through permanent patient monitoring and feedback [ 79 , 81 , 82 , 83 , 84 , 85 , 86 ]. Therefore, with the support of the machine learning models, this technology has been used to provide better service and improve patient’s experience and treatment outcomes [ 81 , 83 ].…”

Section: Related Workmentioning

confidence: 99%

A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States

Pinto-Bernal

Cifuentes

Perdomo

et al. 2021

Sensors

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Physical exercise contributes to the success of rehabilitation programs and rehabilitation processes assisted through social robots. However, the amount and intensity of exercise needed to obtain positive results are unknown. Several considerations must be kept in mind for its implementation in rehabilitation, as monitoring of patients’ intensity, which is essential to avoid extreme fatigue conditions, may cause physical and physiological complications. The use of machine learning models has been implemented in fatigue management, but is limited in practice due to the lack of understanding of how an individual’s performance deteriorates with fatigue; this can vary based on physical exercise, environment, and the individual’s characteristics. As a first step, this paper lays the foundation for a data analytic approach to managing fatigue in walking tasks. The proposed framework establishes the criteria for a feature and machine learning algorithm selection for fatigue management, classifying four fatigue diagnoses states. Based on the proposed framework and the classifier implemented, the random forest model presented the best performance with an average accuracy of ≥98% and F-score of ≥93%. This model was comprised of ≤16 features. In addition, the prediction performance was analyzed by limiting the sensors used from four IMUs to two or even one IMU with an overall performance of ≥88%.

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“…Furthermore, social robots are being used in alternative rehabilitation areas. For instance, in cardiac rehabilitation, a humanoid robot was implemented to monitor and support patients with cardiovascular diseases (Casas et al, 2020 ). The robot gives the clinicians alerts to warn emergency events, provide motivation and correct the patient's physical activity performance.…”

Section: Related Workmentioning

confidence: 99%

Long-Term Social Human-Robot Interaction for Neurorehabilitation: Robots as a Tool to Support Gait Therapy in the Pandemic

et al. 2021

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COVID-19 pandemic has affected the population worldwide, evidencing new challenges and opportunities for several kinds of emergent and existing technologies. Social Assistive Robotics could be a potential tool to support clinical care areas, promoting physical distancing, and reducing the contagion rate. In this context, this paper presents a long-term evaluation of a social robotic platform for gait neurorehabilitation. The robot's primary roles are monitoring physiological progress and promoting social interaction with human distancing during the sessions. A clinical validation with ten patients during 15 sessions were conducted in a rehabilitation center located in Colombia. Results showed that the robot's support improves the patients' physiological progress by reducing their unhealthy spinal posture time, with positive acceptance. 65% of patients described the platform as helpful and secure. Regarding the robot's role within the therapy, the health care staff agreed (>95%) that this tool can promote physical distancing and it is highly useful to support neurorehabilitation throughout the pandemic. These outcomes suggest the benefits of this tool to be further implemented in the pandemic.

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Social Assistive Robots: Assessing the Impact of a Training Assistant Robot in Cardiac Rehabilitation

Cited by 24 publications

References 45 publications

A Socially Assistive Robot for Long-Term Cardiac Rehabilitation in the Real World

A Socially Assistive Robot for Long-Term Cardiac Rehabilitation in the Real World

A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States

Long-Term Social Human-Robot Interaction for Neurorehabilitation: Robots as a Tool to Support Gait Therapy in the Pandemic

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