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Wiki source code of Social Robot

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1 //<Describe the selection of the social robot you intend to use. The description includes the reasons for this selection and the characteristics of this specific robot..>//
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3 = **Selection of the Social Robot: Pepper** =
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5 We have selected Pepper, a semi-humanoid social robot developed by SoftBank Robotics, to serve as an activity companion in our eldercare facility. This choice addresses the core challenge of reducing loneliness among residents while enabling caregivers like John to dedicate more time to critical medical care and facility management tasks.
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7 **Justification for Selection**
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9 Pepper's design and capabilities align closely with the needs of our eldercare environment for several key reasons:
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11 * **Humanoid Embodiment for Social Engagement**: Pepper features a compact humanoid upper body (approximately 1.2m tall) with expressive arms, hands, and an animated tablet display on its chest. This embodiment choice directly supports our task requirements by leveraging the Media Equation theory (Reeves & Nass, 1996) and anthropomorphic design principles:** **patients naturally respond to humanlike forms with greater trust and social engagement. Research has demonstrated that humanoid robots like Pepper can effectively reduce loneliness in elderly populations (Pandey & Gelin, 2018). The human-scale proportions make Pepper non-intimidating for elderly residents while still being recognizable as a social agent, which is critical for therapeutic alliance building in care settings (Broadbent et al., 2009).
12 * **Wheeled Mobility Platform**: Pepper's omnidirectional wheeled base enables smooth, autonomous navigation through hallways, communal game rooms, and resident spaces without staff assistance. This mobility solution is specifically chosen over legged locomotion because it provides greater reliability and safety in indoor environments, reduces fall risks from mechanical failures, and requires minimal floor modifications. These are critical considerations given our elderly population and existing facility infrastructure.
13 * **Multimodal Interaction Capabilities**: Pepper integrates cameras, microphones, and speakers to enable natural multimodal human-robot interaction. The robot employs facial recognition software to identify patients and staff, supporting personalized interaction based on individual preferences and histories. This addresses human factors requirements for situational awareness and adaptive automation (Parasuraman et al., 2000), as Pepper can detect when residents enter common areas and initiate contextually appropriate conversations.
14 * **Emotion Detection and Adaptive Dialogue**: Through its cameras and AI-powered software, Pepper can detect emotional states through facial expressions and vocal tone. This capability allows the robot to adapt its conversation style (e.g., offering encouragement when detecting sadness or celebrating with residents showing positive emotion), directly supporting the emotional support aspect of our operational requirements (Robinson et al., 2014).
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16 **Operational Integration**
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18 Pepper's operational design addresses HF considerations:
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20 * **Task Allocation and Workload Management**: By automating routine social engagement activities (leading group exercises, facilitating games, providing reminders), Pepper reduces caregiver cognitive load and allows staff to focus on tasks requiring human judgment and medical expertise.
21 * **Transparency and Trust**: Pepper's tablet display provides visual feedback showing its current activity, scheduled events, and recognition status, supporting system transparency. This is a key factor in building trust with elderly users who may have limited experience with robotics (Hancock et al., 2011).
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23 **Limitations and Mitigations**
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25 While Pepper offers substantial benefits, there are specific limitations:
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27 * **Limited Physical Assistance Capability**: Pepper cannot perform physical caregiving tasks (lifting, physical therapy assistance).
28 ** **Mitigation**: We position Pepper strictly as a social companion, not a replacement for physical caregivers. Staff remain responsible for all activities of daily living support.
29 * **Navigation Constraints**: While mobile, Pepper requires clear floor space and cannot navigate stairs or narrow doorways in older facility sections.
30 ** **Mitigation**: We map accessible routes and designated primary operational zones (common areas, main hallways). For residents in less accessible rooms, we schedule dedicated visit times and have staff escort Pepper when needed.
31 * **Recognition Accuracy with Elderly Populations**: Facial recognition may struggle with age-related facial changes or low-light conditions.
32 ** **Mitigation**: We implement multimodal identification using RFID wristbands as backup authentication, and train staff to monitor and correct misidentifications. The system learns from corrections to improve accuracy over time.
33 * **Technology Acceptance Barriers**: Some elderly residents may experience technology anxiety or resistance to robot interaction (Broadbent et al., 2009).
34 ** **Mitigation**: We follow a gradual introduction protocol, starting with simple group activities and allowing voluntary participation. Staff monitor comfort levels and provide human alternatives for residents who prefer traditional interaction.
35 * **Battery Life and Charging**: Pepper requires charging approximately every 12 hours.
36 ** **Mitigation**: We establish a structured schedule with designated charging periods during low-activity times and maintain a charging station in a central location.
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38 By selecting Pepper and implementing these operational safeguards, we create a socially assistive robotics solution that enhances quality of life for residents while respecting the irreplaceable role of human caregivers in eldercare (Bemelmans et al., 2012).
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40 == References ==
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42 Broadbent, E., Stafford, R., & MacDonald, B. (2009). Acceptance of healthcare robots for the older population: Review and future directions. //International Journal of Social Robotics//, 1(4), 319-330. [[https:~~/~~/doi.org/10.1007/s12369-009-0030-6>>url:https://doi.org/10.1007/s12369-009-0030-6]]
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44 Hancock, P. A., Billings, D. R., Schaefer, K. E., Chen, J. Y., De Visser, E. J., & Parasuraman, R. (2011). A meta-analysis of factors affecting trust in human-robot interaction. //Human Factors//, 53(5), 517-527. [[https:~~/~~/doi.org/10.1177/0018720811417254>>url:https://doi.org/10.1177/0018720811417254]]
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46 Pandey, A. K., & Gelin, R. (2018). A mass-produced sociable humanoid robot: Pepper: The first machine of its kind. //IEEE Robotics & Automation Magazine//, 25(3), 40-48. [[https:~~/~~/doi.org/10.1109/MRA.2018.2833157>>url:https://doi.org/10.1109/MRA.2018.2833157]]
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48 Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. //IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans//, 30(3), 286-297. [[https:~~/~~/doi.org/10.1109/3468.844354>>url:https://doi.org/10.1109/3468.844354]]
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50 Robinson, H., MacDonald, B., & Broadbent, E. (2014). The role of healthcare robots for older people at home: A review. //International Journal of Social Robotics//, 6(4), 575-591. [[https:~~/~~/doi.org/10.1007/s12369-014-0242-2>>url:https://doi.org/10.1007/s12369-014-0242-2]]
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52 Reeves, B., & Nass, C. (1996). //The media equation: How people treat computers, television, and new media like real people and places//. Cambridge University Press.