Unpacking the cultural baggage of travelling robots

Blond, Lasse; Olesen, Finn

doi:10.4324/9781315227207-8

Cited by 5 publications

(3 citation statements)

References 1 publication

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“…While this intersubjective level of coordination is crucial to engage the elderly residents, it does not apply for any robot thus far. Symmetrical accounts that consider human and nonhumans as actors alike, based on ANT, would run the risk of borrowing these key differences, relating to intentionality (Blond, 2018, p. 55) or ‘the psychological significance of interacting with technological artifacts’ (Jones, 2017, p. 568); my contribution here seeks instead to answer Suchman’s call for a ‘rearticulation of asymmetry’, assuming that ‘persons and artifacts do not constitute each other in the same way ’ (Suchman, 2006, p. 269, emphasis in the original). Lastly, and from another perspective, the asymmetries of agency and sense of coordination between Paro and the caregivers mirrored the distinct ways caregivers and the coordinators of the ‘Paro project’ collaborated on it.…”

Section: Conclusion: the Care Of Making Robot(s) Care As Constitutive...mentioning

confidence: 99%

“…In line with recent attempts to unfold the socio-material conditions of care robots’ testing (Blond, 2018; Jeon et al, 2020; Leeson, 2017; Lipp, 2023; Wright, 2018a), this article investigates the tentative introduction and staging of Paro, a seal robot, for everyday care of older adults suffering from dementia in a French care home (EHPAD) in the Paris suburbs. Contrasting with existing biomedical research that disentangles Paro’s therapeutic effects from ‘the broader social context that scaffolds interactions between older adults and [it]’ (Chang & Šabanović, 2015), it also extends the scope of previous ethnographic accounts of human participants’ work to make robots ‘sociable’, insofar as it homes in on the care workers’ pervasive micro-practices of interaction framing in situ .…”

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confidence: 99%

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Staging Paro: The care of making robot(s) care

Chevallier

2022

Soc Stud Sci

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Paro, a baby seal robot, is arguably the best-known care robot worldwide. Its clinical effects on people with special needs have been studied for more than twenty years by multidisciplinary teams. However, there are very few studies of Paro ‘in the wild’, inserted in the routines and pressures of a care home, which is supposed to be Paro’s natural environment. Based on fieldwork in a French public nursing home, this article relocates Paro’s psychosocial effects – asserted by its corporate and academic advocates to stem from the robot’s software and user-friendly design – in the situated practice of care deployed by the nursing staff. Although it is commonly assumed that the robot can interact autonomously, thanks to its AI, it took considerable efforts to enable ‘patient-robot’ interactions, and interactions were always mediated by care staff. The supervision of the interaction between Paro and its user(s) by at least one care worker, prescribed by its sales representative to prevent the seal robot from being harmed or monopolized by a resident, thus framed the very course and meaning of the interaction, through an entanglement of verbal and tactile guidance. Staging Paro often meant awakening and maintaining interest from confused or passive users, and playing with its ontological flexibility to meet the preferences of its elderly interlocutors.

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Section: Conclusion: the Care Of Making Robot(s) Care As Constitutive...mentioning

confidence: 99%

mentioning

confidence: 99%

Staging Paro: The care of making robot(s) care

Chevallier

2022

Soc Stud Sci

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“…In this study, when a potential user fall is detected by IMU-L sensor data analysis, a mobile robot system is used to doublecheck the fall situation of the user using an RGB camera. The robot used was a Silbot-3 [78][79][80], developed and sold by Robocare Corp. of South Korea.…”

Section: B Mobile Robot System To Double-check Fall Detectionmentioning

confidence: 99%

Deep Neural Network–Based Double-Check Method for Fall Detection Using IMU-L Sensor and RGB Camera Data

et al. 2021

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Existing methods for fall detection may not detect a fall when it occurs or may generate a false alarm when a fall does not occur. In order to overcome these limitations and detect falls with 100% accuracy, a double-check method for fall detection in elderly people via an inertial measurement unit-location (IMU-L) sensor and a red-green-blue (RGB) camera is proposed. The IMU-L sensor is a combination of an IMU sensor (accelerometer and gyroscope) and an ultrawideband signal-based location sensor; the RGB sensor is mounted on a robot. The proposed method involves detecting and confirming the fall of an elderly individual via the IMU-L sensor and an RGB image, respectively. The IMU-L sensor is worn on the body to detect falls. When a potential fall occurs, the individual's location information is synchronized with the motion data. During detection, because of the sequential nature of IMU data, a deep learning technique called a recurrent neural network (RNN) is trained to classify falls. When the IMU indicates a suspected fall situation, the robot moves to the corresponding location and confirms whether a fall has occurred. During the confirmation stage, a convolutional neural network-based technique is applied to the RGB image data to recognize and confirm the fall. Repeated confirmed fall detections using this method classified falls more accurately than existing methods that use only an IMU sensor. We conducted a real-time experiment to validate our method using a dataset developed in a laboratory and achieved 100% accuracy in our experimental environment. INDEX TERMS convolutional neural network, deep learning, elderly fall, fall detection, motion data with location, recurrent neural network, transfer learning.

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Who is behind the robot? The role of public social workers in implementing robotic eldercare program in South Korea

Shin,

Lee

2024

Social Work in Health Care

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Unpacking the cultural baggage of travelling robots

Cited by 5 publications

References 1 publication

Staging Paro: The care of making robot(s) care

Staging Paro: The care of making robot(s) care

Deep Neural Network–Based Double-Check Method for Fall Detection Using IMU-L Sensor and RGB Camera Data

Who is behind the robot? The role of public social workers in implementing robotic eldercare program in South Korea

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