PLEIA: A Reconfigurable Platform for Evaluation of HCI Acting

Peralta, Hector; Riman, Chadi; Baklouti, Malek; Ouezdou, Fethi Ben; Mougharbel, Imad; Laffont, I.; Bouteille, Justine

doi:10.5772/5173

Cited by 2 publications

References 10 publications

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Exploring of Discrete and Continuous Input Control for AI-enhanced Assistive Robotic Arms

Pascher,

Zinta,

Gerken

2024

Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction

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Robotic arms, integral in domestic care for individuals with motor impairments, enable them to perform Activities of Daily Living (ADLs) independently, reducing dependence on human caregivers. These collaborative robots require users to manage multiple Degrees-of-Freedom (DoFs) for tasks like grasping and manipulating objects. Conventional input devices, typically limited to two DoFs, necessitate frequent and complex mode switches to control individual DoFs. Modern adaptive controls with feed-forward multimodal feedback reduce the overall task completion time, number of mode switches, and cognitive load. Despite the variety of input devices available, their effectiveness in adaptive settings with assistive robotics has yet to be thoroughly assessed. This study explores three different input devices by integrating them into an established XR framework for assistive robotics, evaluating them and providing empirical insights through a preliminary study for future developments. CCS CONCEPTS• Computer systems organization → Robotic control; • Humancentered computing → Visualization techniques; Virtual reality.

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Exploring of Discrete and Continuous Input Control for AI-enhanced Assistive Robotic Arms

Pascher,

Zinta,

Gerken

2024

Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction

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Wang¹

2014

J. Med. Biol. Eng.

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Cerebral vascular disease is the leading cause of functional disability among adults. Approximately half of all stroke survivors continue to suffer from severe neurological deficits and hemiparesis in the upper extremities as well as many secondary complications due to immobilization. Robotics can provide highly intensive intervention in stroke rehabilitation as well as an objective means of measuring patient progress. This study designs an upper limb rehabilitation (Rehab) robot with multiple degrees of freedom. This design provides a wider range of motion in 3-dimentional space than that provided by an existing endpoint-fixation system. In addition, unlike cable suspension systems that lack biofeedback, the sensors incorporated into the proposed design can be used to detect the voluntary force produced by the stroke patient. The Rehab robot features an exoskeleton-type design with in-built redundancy, a guidance control system, and force feedback using an electromyographic trigger. Three rehabilitation modes can be selected by physical therapists according to the severity of the patient's upper-limb impairment: passive, active, and guidance. Guidance mode assists patients in motor training, with programs such as drawing circles, which involves complex movements that require coordination between the shoulder and elbow joints. Such skills are ideally suited to relearning functional tasks following a stroke. Physical experiments were conducted in this pilot study to evaluate the performance of the Rehab robot. The results indicate that the robot could be effective. Guidance mode achieves the desired guidance functions, informing the subject of the pose required to complete the task as well as enabling them to reduce unnecessary muscle use.

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PLEIA: A Reconfigurable Platform for Evaluation of HCI Acting

Cited by 2 publications

References 10 publications

Exploring of Discrete and Continuous Input Control for AI-enhanced Assistive Robotic Arms

Exploring of Discrete and Continuous Input Control for AI-enhanced Assistive Robotic Arms

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