Studying robots outside the lab: HRI as ethnography

Blond, Lasse

doi:10.1515/pjbr-2019-0007

Cited by 23 publications

(18 citation statements)

References 49 publications

(67 reference statements)

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“…• Audio and speech issues [ 39 – 41 , 43 , 46 , 47 , 53 , 63 , 71 , 74 – 76 , 81 , 84 , 86 , 88 , 89 ],…”

Section: Resultsmentioning

confidence: 99%

“…Technical issues were widely reported as barriers, particularly in relation to socially assistive robots. These included audio and speech issues [ 39 – 41 , 43 , 46 , 47 , 53 , 63 , 71 , 74 – 76 , 81 , 84 , 86 , 88 , 89 ], hardware problems [ 43 , 58 , 70 ], overheating [ 40 , 69 , 70 ], unreliability of functions [ 39 , 40 , 43 , 45 , 46 , 58 , 59 , 65 , 71 , 81 , 85 , 89 , 90 ], unclear or unpredictable actions [ 39 , 40 , 43 , 51 ] and other technical issues [ 43 , 47 , 54 , 61 , 77 ]. The frequent need to recharge batteries was also cited as a barrier [ 83 ].…”

Section: Resultsmentioning

confidence: 99%

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Barriers and facilitators to the implementation of social robots for older adults and people with dementia: a scoping review

et al. 2021

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Background Psychosocial issues, such as social isolation and loneliness among older adults and people with dementia, continue to pose challenges with a rapidly aging population worldwide. Social robots are a rapidly emerging field of technology, developed to help address the psychosocial needs of this population. Although studies have reported positive findings regarding their psychosocial benefits, their implementation in real-world practice remains a challenge. Nevertheless, little is known about the factors affecting their implementation. The purpose of this review is to provide a systematic overview of the barriers and facilitators affecting the implementation of social robots for older adults and people with dementia. Method The Arksey and O’Malley approach with methodological enhancement by Levac et al. was used to guide the conduct of this review. Seven electronic databases were searched. In addition, hand searching and backward citation tracing was conducted. Three independent reviewers were involved in the screening and data charting process. Findings were synthesised and categorised into the five domains outlined in the Consolidated Framework of Implementation Research (CFIR). Results A total of 53 studies were included in the final review. Most of the included studies were based in participants’ homes and in care facilities. Barriers and facilitators were mapped onto 18 constructs in the five domains of the CFIR. The most frequently cited barriers were mapped to the constructs within the domain of “Intervention characteristics”, where issues such as the complexity of using the technology and technical obstacles impeded implementation. Most facilitators were mapped onto the domain “Patient needs and resources”. Overall, existing research are disproportionately focused on the internal validity (i.e. characteristics) of social robots, and there is significantly less research investigating their external validity, such as organisational or wider contextual factors that can affect their implementation in real-world practice. Conclusion This review has identified and synthesised the breadth of evidence on the barriers and facilitators to the implementation of social robots for older adults and people with dementia. Future research should pay more attention to investigating the contextual factors, using an implementation framework, to identify barriers and facilitators to guide the implementation of social robots.

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“…• Audio and speech issues [ 39 – 41 , 43 , 46 , 47 , 53 , 63 , 71 , 74 – 76 , 81 , 84 , 86 , 88 , 89 ],…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Barriers and facilitators to the implementation of social robots for older adults and people with dementia: a scoping review

et al. 2021

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“…Entries have reflected on: rapport; positionalities; use of theory; and the ways in which embodied experiences frame interpretation and analysis. These processes enable us to acknowledge our situated-ness, and our "co-construction" of our own data through our "embodied participation in the empirical field" [8] (p.120). These reflections stimulate critical interdisciplinary discussions as we consider the sociality of robotic touch through semiotic and sensory lenses [10].…”

Section: Methods: Ethnography and Hrimentioning

confidence: 99%

Becoming in Touch with Industrial Robots through Ethnography

Barker

Jewitt

Price

2020

Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

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Touch is central to communication and social interaction. For both humans and robots touch is a mode through which they sense the world. A second wave of industrial robots is reshaping how touch operates within the labor process. Recent studies have turned their attention to the role of touch in Human-Robot Interaction (HRI). While these studies have produced useful knowledge in relation to the affective capacities of robotic touch, methods remain restrictive. This paper contributes to expanding research methods for the study of robotic touch. It reports on the design of an ongoing ethnography that forms part of the InTouch project. The interdisciplinary project takes forward a socially orientated stance and is concerned with how technologies shape the semiotic and sensory dimensions of touch in the 'real world'. We contend that these dimensions are key factors in shaping how humans and robots interact, yet are currently overlooked in the HRI community. This multi-sited sensory ethnography research has been designed to explore the social implications of robotic touch within industrial settings (e.g. manufacturing and construction).

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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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Studying robots outside the lab: HRI as ethnography

Cited by 23 publications

References 49 publications

Barriers and facilitators to the implementation of social robots for older adults and people with dementia: a scoping review

Barriers and facilitators to the implementation of social robots for older adults and people with dementia: a scoping review

Becoming in Touch with Industrial Robots through Ethnography

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

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