Social Robot

We considered the robots Navel, NAO, Pepper and Miro-E as potential fits for our use case.

NAO and Miro-E are small and risk appearing toy-like, which is not ideal since elderly users usually prefer life-sized humanoid robots [1].

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While Navel provides advanced interaction capabilities and a human-like figure, it is difficult to program for and does not offer a tablet interface to assist as a visual aid.

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Therefore, we decided on Pepper, since it outperforms NAO in studies [2], is easy to program for using Python and offers an expressive humanoid form and a large tablet to assist as a visual aid and for touch controls.

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(% style="background-color:#ffffff; color:#000000; font-family:Arial,sans-serif; font-size:11pt; font-style:normal; font-variant:normal; font-weight:400; text-decoration:none; white-space:pre-wrap" %)The Pepper robot is commonly used because it offers interactive services such as welcoming visitors, providing information, and even dancing. Its ability to recognize faces and interpret emotions enables it to create more natural connections, making it particularly effective in healthcare.Pepper was used before with older adults, including as a coach for those with psychiatric disorders. A lot of studies have proved that the interaction of elderly people with the Pepper robot is efficient and helps the patients improve the quality of their lives. [3]

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(% style="background-color:#ffffff; color:#000000; font-family:Arial,sans-serif; font-size:11pt; font-style:normal; font-variant:normal; font-weight:400; text-decoration:none; white-space:pre-wrap" %)Pepper is equipped with a lot of advanced sensors that enable it to interact with its environment effectively. It features four microphones, two RGB cameras, and a 3D sensor on its head, along with touch sensors on its hands and chest. Additionally, Pepper's legs are equipped with sonar, laser, and bumper sensors, along with a gyro sensor for balance. These sensors work together to allow Pepper to recognize faces, interpret emotions, and navigate its surroundings with precision [4].

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[[1.1 Pepper Robot>>image:1740857394953-812.png]]

**References:**

[1]Broekens, Joost, Marcel Heerink, and Henk Rosendal. "Assistive social robots in elderly care: a review." //Gerontechnology// 8.2 (2009): 94-103.

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[2]Liao Y-J, Jao Y-L, Boltz M, et al. Use of a Humanoid Robot in Supporting Dementia Care: A Qualitative Analysis. SAGE Open Nursing. 2023;9. doi:10.1177/23779608231179528

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[3] Neerincx, M.A., van Vught, W., Blanson Henkemans, O., Oleari, E., Broekens, J., Peters, R., Kaptein, F., Demiris, Y., Kiefer, B., Fumagalli, D. and Bierman, B. (2019). Socio-Cognitive Engineering of a Robotic Partner for Child’s Diabetes Self-Management. //Frontiers in Robotics and AI//, 6. doi:https:~/~/doi.org/10.3389/frobt.2019.00118.

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[4]** **Abdel-Hamid, Ossama, et al. “Convolutional Neural Networks for Speech Recognition.” //IEEE/ACM Transactions on Audio, Speech, and Language Processing//, vol. 22, no. 10, Oct. 2014, pp. 1533–1545, www.microsoft.com/en-us/research/wp-content/uploads/2016/02/CNN_ASLPTrans2-14.pdf, https:~/~/doi.org/10.1109/taslp.2014.2339736. Accessed 20 Nov. 2019.

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Wiki source code of Social Robot