Route selection and obstacle avoidance with a short-range haptic sensory substitution device✰

Lobo, Lorena; Nordbeck, Patric C.; Raja, Vicente; Chemero, Anthony; Riley, Michael A.; Jacobs, David M.; Travieso, David

doi:10.1016/j.ijhcs.2019.03.004

Cited by 15 publications

(13 citation statements)

References 29 publications

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“…As described above, the signal included directional information: Figure 4 shows the haptic whiskers used to map wall proximity to the intensity of vibrotactile feedback experienced on the controller's trigger motors. The visual 'sensory deprivation' in this condition resembles the blindfolded training conditions commonly utilized in several other navigation-focused haptic SSAD studies [6,7,14,31,32]. Similarly, parallels may be drawn between this condition and the utilization of a novel SSAD by sensorily impaired individuals, whose sensory apparatus may offer little to no overlap with the information presented by the device.…”

Section: Study Conditions and Hypothesesmentioning

confidence: 78%

“…Process/approaches to the task Exploration of participant mental models, sense-making and task completion/device use strategies can provide insights into how specific devices present user-environment affordances. Lobo [31] specifically analyses route selection in a navigational task for insight into how various devices contribute to users' perception of space in a target approach task using the Enactive Torch.Further, understanding participants' task approach also gives insight into the "what it is like" component of SSAD utilization; a concept at the heart of enactive device design [30] and associated theories of cognition [42].…”

Section: Qualitative Measuresmentioning

confidence: 99%

“…The four visual/haptic feedback conditions we tested in this study are designed to resemble some of the familiarization and training mechanisms utilized in the vibrotactile SSAD navigation studies [14,25,27,31,38] detailed in the related work section. Figure 6 shows an illustration of our four study conditions.…”

Section: Study Conditions and Hypothesesmentioning

confidence: 99%

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Investigating the Effect of Sensory Concurrency on Learning Haptic Spatiotemporal Signals

Carson

Quigley

Clarke

et al. 2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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A new generation of multimodal interfaces and interactions is emerging. Drawing on the principles of Sensory Substitution and Augmentation Devices (SSADs), these new interfaces offer the potential for rich, immersive human-computer interactions, but are difficult to design well, and take time to master, creating significant barriers towards wider adoption. Following a review of the literature surrounding existing SSADs, their metrics for success and their growing influence on interface design in Human Computer Interaction, we present a medium term (4-day) study comparing the effectiveness of various combinations of visual and haptic feedback (sensory concurrencies) in preparing users to perform a virtual maze navigation task using haptic feedback alone. Participants navigated 12 mazes in each of 3 separate sessions under a specific combination of visual and haptic feedback, before performing the same task using the haptic feedback alone. Visual sensory deprivation was shown to be inferior to visual & haptic concurrency in enabling haptic signal comprehension, while a new hybridized condition combining reduced visual feedback with the haptic signal was shown to be superior. Potential explanations for the effectiveness of the hybrid mechanism are explored, and the scope and implications of its generalization to new sensory interfaces is presented.

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Section: Study Conditions and Hypothesesmentioning

confidence: 78%

Section: Qualitative Measuresmentioning

confidence: 99%

See 1 more Smart Citation

Investigating the Effect of Sensory Concurrency on Learning Haptic Spatiotemporal Signals

Carson

Quigley

Clarke

et al. 2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

View full text Add to dashboard Cite

show abstract

“…The sensations were mediated by a hand-held sensory substitution device called the Enactive Torch (Figure 3; Froese et al, 2012a), which translates infrared-based measures of distance to nearby objects into intensity of vibrotactile feedback in the user's hand. Like a cane for blind people, this device permits people to learn to perceive passages through objects in space (Favela et al, 2018), and user's walking trajectories coincide with those of visually-guided locomotion (Lobo et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Where Is the Action in Perception? An Exploratory Study With a Haptic Sensory Substitution Device

Froese

Ortiz-Garin

2020

Front. Psychol.

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Enactive cognitive science (ECS) and ecological psychology (EP) agree that active movement is important for perception, but they remain ambiguous regarding the precise role of agency. EP has focused on the notion of sensorimotor invariants, according to which bodily movements play an instrumental role in perception. ECS has focused on the notion of sensorimotor contingencies, which goes beyond an instrumental role because skillfully regulated movements are claimed to play a constitutive role. We refer to these two hypotheses as instrumental agency and constitutive agency, respectively. Evidence comes from a variety of fields, including neural, behavioral, and phenomenological research, but so far with confounds that prevent an experimental distinction between these hypotheses. Here we advance the debate by proposing a novel double-participant setup that aims to isolate agency as the key variable that distinguishes bodily movement in active and passive conditions of perception. We pilot this setup with a psychological study of width discrimination using the Enactive Torch, a haptic sensory substitution device. There was no evidence favoring the stronger hypothesis of constitutive agency over instrumental agency. However, we caution that during debriefing several participants reported using cognitive strategies that did not rely on spatial perception. We conclude that this approach is a viable direction for future research, but that greater care is required to establish and confirm the desired modality of first-person experience.

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“…The simple transparency with which Equation (1) can capture complex human steering behavior and locomotory navigation has been verified across numerous experimental procedures and tasks (see Warren, 2006 ; Warren and Fajen, 2008 for reviews). Modest extensions of Equation (1) can also capture patterns of collective behavior in crowd locomotor dynamics (Bonneaud et al, 2012 ; Warren, 2018 ), the dynamics of intercepting a moving target (Fajen and Warren, 2007 ), robot navigation (Huang et al, 2006 ) object pick-and-place tasks (Lamb et al, 2017 ), and more recently, navigation around obstacles while using a short-range haptic sensory substitution device (Lobo et al, 2019 ). Moreover, it is important to note that in each of the above studies, the movement trajectories generated by the model are not a result of a-priori computation (planning), but instead, are simply a situated self-organized result of interacting attractors and repellors.…”

Section: Behavioral Dynamics Of Human Route Selectionmentioning

confidence: 99%

Navigational Behavior of Humans and Deep Reinforcement Learning Agents

et al. 2021

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Rapid advances in the field of Deep Reinforcement Learning (DRL) over the past several years have led to artificial agents (AAs) capable of producing behavior that meets or exceeds human-level performance in a wide variety of tasks. However, research on DRL frequently lacks adequate discussion of the low-level dynamics of the behavior itself and instead focuses on meta-level or global-level performance metrics. In doing so, the current literature lacks perspective on the qualitative nature of AA behavior, leaving questions regarding the spatiotemporal patterning of their behavior largely unanswered. The current study explored the degree to which the navigation and route selection trajectories of DRL agents (i.e., AAs trained using DRL) through simple obstacle ridden virtual environments were equivalent (and/or different) from those produced by human agents. The second and related aim was to determine whether a task-dynamical model of human route navigation could not only be used to capture both human and DRL navigational behavior, but also to help identify whether any observed differences in the navigational trajectories of humans and DRL agents were a function of differences in the dynamical environmental couplings.

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Route selection and obstacle avoidance with a short-range haptic sensory substitution device✰

Cited by 15 publications

References 29 publications

Investigating the Effect of Sensory Concurrency on Learning Haptic Spatiotemporal Signals

Investigating the Effect of Sensory Concurrency on Learning Haptic Spatiotemporal Signals

Where Is the Action in Perception? An Exploratory Study With a Haptic Sensory Substitution Device

Navigational Behavior of Humans and Deep Reinforcement Learning Agents

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